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Everything you need to know about Computed Tomography (CT) & CT Scanning

November 2019 Imaging Pearls - Educational Tools | CT Scanning | CT Imaging | CT Scan Protocols - CTisus
Imaging Pearls ❯ November 2019

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3D and Workflow

  • Errors on CT of the Body in the ER Setting
    Poor patient prep
    Poor selection of scan protocol
    Lack of interaction of the technologists and the Radiologist.
    Mis-reads of CT scans (false positive or negative)
  • “Another common situation that may produce errors is the inadequate flow of intravenously injected contrast material. Consequently, adequate catheter access and a flow rate of intravenous contrast of 3.5 ml/second higher should be established, as such conditions will help for the correct identification of vascular injuries. In fact, in polytrauma patients, the correct characterization of such injuries is crucial; the site of vascular contrast extravasation (blush) must be identified and the nature of the extravasation must be characterized.”
    Errors in imaging patients in the emergency setting.
    Pinto A, Reginelli A, Pinto F et al.
    Br J Radiol 2016; 89: 20150914.
  • “Injuries of the diaphragm are not common and represent 5% of missed injuries, half of which are not recognized in the first 24 h after the traumatic event. Diagnosis of the injured diaphragm is particularly difficult, resulting in a late diagnosis, and some studies have reported that the sensitivity CT examination for the diagnosis of fractures of the diaphragm is relatively low (50–73%).”
    Errors in imaging patients in the emergency setting.
    Pinto A, Reginelli A, Pinto F et al.
    Br J Radiol 2016; 89: 20150914.
  • “Technical parameters of CT scans (region of interest, use of a contrast agent and scanning timing) differ according to the clinical suspected diseases, and if performed under in- appropriate conditions, CT images will not provide appropriate information for diagnosis. Thus, to avoid missing positive CT findings, in addition to careful readings, radiologists need to obtain such patient information from clinicians.”
    Errors in imaging patients in the emergency setting.
    Pinto A, Reginelli A, Pinto F et al.
    Br J Radiol 2016; 89: 20150914.
  • “Evaluation of stomach neoplasms by traditional 3-dimensional (3D) computed tomography methods such as volume rendering and maxi- mum-intensity projection plays an important role in lesion detection and characterization, preoperative planning, staging, and follow-up. Recently, a new 3D visualization method has become available known as cinematic rendering (CR). This novel technique makes use of a complex global lighting model to impart photorealistic levels of detail to 3D images. Although this new technique has yet to be systematically studied for the evaluation of stomach neoplasms, its intrinsic ability to create realistic shadowing effects to enhance understanding of the 3D relative locations of anatomic structures and to enhance detail and texture may prove valuable for a variety of applications. In this article, we demonstrate the CR appearance of multiple different gastric neoplasms, describe potential advantages of CR, and suggest future research directions.”
    Evaluation of Stomach Neoplasms With 3-Dimensional Computed Tomography: Focus on the Potential Role of Cinematic Rendering
    Steven P. Rowe, Linda C. Chu, Elliot K. Fishman
    J Comput Assist Tomogr 2018;42: 661–666
  • “Evaluation of stomach neoplasms by traditional 3-dimensional (3D) computed tomography methods such as volume rendering and maxi- mum-intensity projection plays an important role in lesion detection and characterization, preoperative planning, staging, and follow-up. Recently, a new 3D visualization method has become available known as cinematic rendering (CR). This novel technique makes use of a complex global lighting model to impart photorealistic levels of detail to 3D images. Although this new technique has yet to be systematically studied for the evaluation of stomach neoplasms, its intrinsic ability to create realistic shadowing effects to enhance understanding of the 3D relative locations of anatomic structures and to enhance detail and texture may prove valuable for a variety of applications.”
    Evaluation of Stomach Neoplasms With 3-Dimensional Computed Tomography: Focus on the Potential Role of Cinematic Rendering
    Steven P. Rowe, Linda C. Chu, Elliot K. Fishman
    J Comput Assist Tomogr 2018;42: 661–666
  • “Recently, a novel method of 3D CT volumetric data visualization became available. This method, known as cinematic rendering (CR), makes use of standard acquisition CT volumetric data composed of isotropic voxels and is fundamentally similar to VR. However, whereas VR uses a ray casting lighting model to create 3D images from acquired volumes, CR instead makes use of a complex global lighting model that takes into account a number of potential interactions of photons with the material in the imaged volume; this leads to enhanced surface detail and a photorealistic quality to the images.”
    Evaluation of Stomach Neoplasms With 3-Dimensional Computed Tomography: Focus on the Potential Role of Cinematic Rendering
    Steven P. Rowe, Linda C. Chu, Elliot K. Fishman
    J Comput Assist Tomogr 2018;42: 661–666
  • ”Computed tomography is the imaging method of choice for evaluating stomach neoplasms, and traditional 3D methodologies have previously been shown to have value in lesion detection, staging, and follow-up for treatment response. With the addition of enhanced surface detail intrinsic to CR, the role of 3D CT visualizations in stomach neoplasm imaging may be expanded. Prospective trials with pathologic correlation that evaluate the ability of CR to enhance detection of subtle mucosal irregularities, study whether CR provides better lesion characterization through highlighting intratumoral texture, and lead to improved preoperative planning would be of value.”
    Evaluation of Stomach Neoplasms With 3-Dimensional Computed Tomography: Focus on the Potential Role of Cinematic Rendering
    Steven P. Rowe, Linda C. Chu, Elliot K. Fishman
    J Comput Assist Tomogr 2018;42: 661–666
  • “Recently, a novel 3-dimensional visualization methodology for volumetric computed tomography data has become available. This method, known as cinematic rendering, uses an advanced lighting model to create photorealistic images from standard computed tomography acquisition data composed of isotropic voxels. We have observed that cinematic rendering visualizations in which patients have been administered dense, positive oral contrast do not have any substantive visual artifacts and can be used to demonstrate bowel pathology to advantage (ie, “virtual fluoroscopy”). In this technical note, we describe our acquisition and visualization parameters, and we also include demonstrative examples.”
    Cinematic Rendering With Positive Oral Contrast: Virtual Fluoroscopy
    Steven P. Rowe, Linda C. Chu, MD, Elliot K. Fishman,
    J Comput Assist Tomogr 2019;43: 718–720
  • “In this article, we describe our observation that CR can effectively display bowel anatomy and pathology after the administration of positive oral contrast with no perceptible visual artifacts. Indeed, the presence of positive oral contrast in CR images allows for highly detailed displays of the bowel mucosal fold pattern and might be considered a type of “virtual fluoroscopy.”
    Cinematic Rendering With Positive Oral Contrast: Virtual Fluoroscopy
    Steven P. Rowe, Linda C. Chu, MD, Elliot K. Fishman,
    J Comput Assist Tomogr 2019;43: 718–720

  • Cinematic Rendering With Positive Oral Contrast: Virtual Fluoroscopy
    Steven P. Rowe, Linda C. Chu, MD, Elliot K. Fishman,
    J Comput Assist Tomogr 2019;43: 718–720
  • “As demonstrated in this article, CR can produce photorealistic images of the oral contrast–opacified bowel without significant vi- sual artifact. The intrinsic advantages of CR, including high levels of surface detail and realistic shadowing, contribute to the visualization of the bowel in a manner analogous to fluoroscopic studies. In- deed, the quality of the visualizations would suggest that CR may have the ability to replace fluoroscopy in certain contexts, while preserving the anatomic and pathologic information that would normally be obtained from a fluoroscopic examination, with the exception that real-time imaging of motion of the bowel and compression maneuvers are not possible. The preset parameters included in this article may be helpful as a starting point for further evaluation of the utility of CR in this context.”
    Cinematic Rendering With Positive Oral Contrast: Virtual Fluoroscopy
    Steven P. Rowe, Linda C. Chu, MD, Elliot K. Fishman,
    J Comput Assist Tomogr 2019;43: 718–720
  • “Photorealistic CR images of the bowel after administration of positive oral contrast demonstrate detailed anatomy and pathology without evidence of visual artifacts. We postulate that CR of the bowel with positive contrast may be able to function as a “virtual fluoroscopy” in some contexts, although this will require significantly more studies to validate.”
    Cinematic Rendering With Positive Oral Contrast: Virtual Fluoroscopy
    Steven P. Rowe, Linda C. Chu, MD, Elliot K. Fishman,
    J Comput Assist Tomogr 2019;43: 718–720
  • Results: The BBCR technique was successfully utilized to demonstrate intraluminal cardiac findings in a patient with a normal left ventricle, a patient with a left ventricular mural thrombus, and a patient status-post transcatheter aortic valve replacement.
    Conclusions: BBCR is a new method of utilizing volumetric chest CT data in order to provide detailed images of intraluminal anatomy and pathology of the heart. Further study of this promising method is warranted.
    Black-blood cinematic rendering: A new method for cardiac CT intraluminal visualization
    Steven P. Rowe, Linda C. Chu, Hannah S. Recht, Cheng Ting Lin, Elliot K. Fishman
    Journal of Cardiovascular Computed Tomography (in press)
  • “We have empirically developed a preset for the visualization of intraluminal structures within the heart and great vessels. We refer to this preset as black-blood cinematic rendering (BBCR) due to the observation that the visual effect is similar to the appearance of black-blood magnetic resonance imaging. In the following manuscript, we describe the BBCR methodology and provide relevant clinical examples of its application to gated cardiac CT data.”
    Black-blood cinematic rendering: A new method for cardiac CT intraluminal visualization
    Steven P. Rowe, Linda C. Chu, Hannah S. Recht, Cheng Ting Lin, Elliot K. Fishman
    Journal of Cardiovascular Computed Tomography (in press)
  • ”The examples in this manuscript demonstrate the potential utility of the BBCR preset to allow the visualization of normal intraluminal cardiac anatomy as well as important pathologic processes. Although this technique will need to be rigorously compared to other methods of cardiac imaging, there appears to be significant promise in utilizing BBCR for cardiac intraluminal visualization. In particular, the utility of BBCR for identifying changes in myocardial trabeculation (e.g. thinning due to prior infarct or left ventricular non-compaction cardiomyopathy), depicting artificial valves and other devices and diagnosing vegetations, and providing intraluminal views of aneurysms and pseudoaneurysms are all applications that merit exploration.”
    Black-blood cinematic rendering: A new method for cardiac CT intraluminal visualization
    Steven P. Rowe, Linda C. Chu, Hannah S. Recht, Cheng Ting Lin, Elliot K. Fishman
    Journal of Cardiovascular Computed Tomography (in press)
  • “In conclusion, the BBCR method appears to provide high levels of intraluminal anatomic detail for cardiac CT imaging. This may facilitate the detection of important pathologic entities such as intramural thrombus, and may also allow improved evaluation of cardiac devices.”
    Black-blood cinematic rendering: A new method for cardiac CT intraluminal visualization
    Steven P. Rowe, Linda C. Chu, Hannah S. Recht, Cheng Ting Lin, Elliot K. Fishman
    Journal of Cardiovascular Computed Tomography (in press)

  • Black-blood cinematic rendering: A new method for cardiac CT intraluminal visualization
    Steven P. Rowe, Linda C. Chu, Hannah S. Recht, Cheng Ting Lin, Elliot K. Fishman
    Journal of Cardiovascular Computed Tomography (in press)

  • Black-blood cinematic rendering: A new method for cardiac CT intraluminal visualization
    Steven P. Rowe, Linda C. Chu, Hannah S. Recht, Cheng Ting Lin, Elliot K. Fishman
    Journal of Cardiovascular Computed Tomography (in press)

  • Black-blood cinematic rendering: A new method for cardiac CT intraluminal visualization
    Steven P. Rowe, Linda C. Chu, Hannah S. Recht, Cheng Ting Lin, Elliot K. Fishman
    Journal of Cardiovascular Computed Tomography (in press)
Adrenal

  • “Incidental adrenal nodules are quite common, occurring in 6% of the population in a large autopsy series and approximately 4% of all abdominal CT exams. The prevalence of incidental adrenal nodules increases with patient age, ranging from < 1% in patients in their 20’s to 7% in patients older than 70. Despite adrenal nodules being a very common finding, the vast majority of all incidentally detected adrenal nodules are benign.”
    Management of incidental adrenal masses: an update
    Daniel I. Glazer, William W. Mayo‐Smith
    Abdominal Radiology (in press 2019) https://doi.org/10.1007/s00261-019-02149-2
  • The most likely danger is that “[w]e’ll do what computers tell us to do, because we’re awestruck by them and trust them to make important decisions”. Radiologists can avoid this by educating themselves and future colleagues about AI, collaborating with researchers to ensure it is deployed in a useful, safe, and meaningful way, and ensuring that its use is always directed primarily towards the patient benefit. In this way, AI can enhance radiology, and allow radiologists to continually improve their relevance and value.”
    Management of incidental adrenal masses: an update
    Daniel I. Glazer, William W. Mayo‐Smith
    Abdominal Radiology (in press 2019) https://doi.org/10.1007/s00261-019-02149-2 
  • ”Despite adrenal nodules being a very common finding, the vast majority of all incidentally detected adrenal nodules are benign [5]. The challenge is determining which incidentally detected nodules can safely be left alone (nonhyperfunctioning mass, myelolipoma, hemorrhage, cyst) and which require further work-up to be certain that they are not clinically significant neoplasms [adrenocortical carcinoma (ACC), pheochromocytoma, or metastases.”
    Management of incidental adrenal masses: an update
    Daniel I. Glazer, William W. Mayo‐Smith
    Abdominal Radiology (in press 2019) https://doi.org/10.1007/s00261-019-02149-2 
  • “When an incidental adrenal mass is discovered at imaging, three important items to determine are a history of malignancy, presence of prior abdominal imaging, and symptoms of a hyperfunctioning mass, as this will guide subsequent management. First, if the patient has a history of malignancy, then an incidental mass has a higher chance of being a metastasis although the risk is still only 26–36%. Second, if prior imaging is available, greater than 1 year of stability is indicative of benignity. Third, if the patient presents with signs or symptoms of adrenal hyperfunction (including hypertension or Cushing’s features) this is suggestive of a biochemically active neoplasm that would require further investigation.”
    Management of incidental adrenal masses: an update
    Daniel I. Glazer, William W. Mayo‐Smith
    Abdominal Radiology (in press 2019) https://doi.org/10.1007/s00261-019-02149-2 
  • ”Adrenal protocol CT is the modality of choice for evaluating adrenal masses as it can characterize a nodule using both density measurements and contrast washout. An adrenal protocol CT consists of a dose reduced unenhanced CT followed by measurement of the attenuation of the nodule in question. If the attenuation of the adrenal mass is ≤ 10 HU (Hounsfield Units), then no further imaging is necessary. If attenuation is > 10 HU, then intravenous contrast is administered, and the patient imaged at 70 s and 15 min to calculate contrast washout. Attenuation based diagnosis of adrenal nodules relies on the presence of lipid within an adenoma which reduces mean attenuation within the entire nodule.”
    Management of incidental adrenal masses: an update
    Daniel I. Glazer, William W. Mayo‐Smith
    Abdominal Radiology (in press 2019) https://doi.org/10.1007/s00261-019-02149-2
  • “Using a threshold of 10 HU on unenhanced images, a diagnosis of benign lipid-rich adenoma can be made with 98% specificity. However, lipid-poor benign adrenal nodules (approximately 20% of adenomas) measure greater than 10 HU on unenhanced CT but can be confidently diagnosed using contrast washout . Masses that have an absolute washout of ≥ 60% [(enhanced HU—delayed HU)/(enhanced HU—unenhanced HU)] or relative washout of ≥ 40% [(enhanced HU—delayed HU)/enhanced HU] are lipid- poor adenomas. Masses that have absolute washout of < 60% or relative washout of < 40% remain indeterminant and require further work-up.”
    Management of incidental adrenal masses: an update
    Daniel I. Glazer, William W. Mayo‐Smith
    Abdominal Radiology (in press 2019) https://doi.org/10.1007/s00261-019-02149-2
  • “Chemical shift MRI has been shown to have a sensitivity of 67% and specificity of 89–100%, similar to CT. However, subgroup analysis shows that chemical shift MRI is inferior to CT at diagnosing adenomas when a nodule measures > 20 HU at unenhanced CT. For this reason, adrenal protocol CT is favored over MRI unless there are patient specific factors which render CT undesirable such as history of iodinated contrast allergy.”
    Management of incidental adrenal masses: an update
    Daniel I. Glazer, William W. Mayo‐Smith
    Abdominal Radiology (in press 2019) https://doi.org/10.1007/s00261-019-02149-2
  • “In addition to imaging tests which are useful in determining if a mass is benign or malignant, biochemical screening is necessary to determine if a mass is hyperfunctioning. Recommendations regarding biochemical screening are somewhat varied, but both the ACR and the American Association of Clinical Endocrinologists and the American Association of Endocrine Surgeons recommend biochemical screening of most if not all patients presenting with an incidentally discovered adrenal mass.”
    Management of incidental adrenal masses: an update
    Daniel I. Glazer, William W. Mayo‐Smith
    Abdominal Radiology (in press 2019) https://doi.org/10.1007/s00261-019-02149-2
  • Adrenal Imaging Pitfalls
    First, a subset of pheochromocytomas may demonstrate greater than 60% washout on adrenal protocol CT and could potentially be misdiagnosed as lipid-poor adenomas. It is for this reason that bio- chemical evaluation is recommended by the American Association of Clinical Endocrinologists and the American Asso- ciation of Endocrine Surgeons even for masses with benign imaging features.
  • Adrenal Imaging Pitfalls
    Finally, both benign and malignant adrenal lesions may show uptake at FDG- 18 PET/CT. Although most benign conditions that can mimic adrenal malignancy are typically bilateral (adrenal hyperplasia, tuberculosis), it is possible for adenomas to demonstrate mild FDG avidity although it is typically less than liver.
  • “Even though almost all incidental adrenal nodules are benign, there is significant overlap of imaging features between malignant and benign entities at single phase contrast enhanced CT. No imaging features on single phase enhanced CT are reliably predictive of benignity although irregular margins and thick enhancing rim are very specific for malignancy. For this reason, in masses without definitely benign features on CT or MRI (attenuation ≤ 10 HU, macroscopic fat, signal loss on chemical shift MRI), further work-up with adrenal protocol CT is recommended in the absence of prior studies for comparison. Adrenal CT is favored over MRI as it can reliable distinguish between lipid-rich and lipid-poor adenomas versus other adrenal pathologies.”
    Management of incidental adrenal masses: an update
    Daniel I. Glazer, William W. Mayo‐Smith
    Abdominal Radiology (in press 2019) https://doi.org/10.1007/s00261-019-02149-2 
Cardiac

  • Results: The BBCR technique was successfully utilized to demonstrate intraluminal cardiac findings in a patient with a normal left ventricle, a patient with a left ventricular mural thrombus, and a patient status-post transcatheter aortic valve replacement.
    Conclusions: BBCR is a new method of utilizing volumetric chest CT data in order to provide detailed images of intraluminal anatomy and pathology of the heart. Further study of this promising method is warranted.
    Black-blood cinematic rendering: A new method for cardiac CT intraluminal visualization
    Steven P. Rowe, Linda C. Chu, Hannah S. Recht, Cheng Ting Lin, Elliot K. Fishman
    Journal of Cardiovascular Computed Tomography (in press)
  • “We have empirically developed a preset for the visualization of intraluminal structures within the heart and great vessels. We refer to this preset as black-blood cinematic rendering (BBCR) due to the observation that the visual effect is similar to the appearance of black-blood magnetic resonance imaging. In the following manuscript, we describe the BBCR methodology and provide relevant clinical examples of its application to gated cardiac CT data.”
    Black-blood cinematic rendering: A new method for cardiac CT intraluminal visualization
    Steven P. Rowe, Linda C. Chu, Hannah S. Recht, Cheng Ting Lin, Elliot K. Fishman
    Journal of Cardiovascular Computed Tomography (in press)
  • ”The examples in this manuscript demonstrate the potential utility of the BBCR preset to allow the visualization of normal intraluminal cardiac anatomy as well as important pathologic processes. Although this technique will need to be rigorously compared to other methods of cardiac imaging, there appears to be significant promise in utilizing BBCR for cardiac intraluminal visualization. In particular, the utility of BBCR for identifying changes in myocardial trabeculation (e.g. thinning due to prior infarct or left ventricular non-compaction cardiomyopathy), depicting artificial valves and other devices and diagnosing vegetations, and providing intraluminal views of aneurysms and pseudoaneurysms are all applications that merit exploration.”
    Black-blood cinematic rendering: A new method for cardiac CT intraluminal visualization
    Steven P. Rowe, Linda C. Chu, Hannah S. Recht, Cheng Ting Lin, Elliot K. Fishman
    Journal of Cardiovascular Computed Tomography (in press)
  • “In conclusion, the BBCR method appears to provide high levels of intraluminal anatomic detail for cardiac CT imaging. This may facilitate the detection of important pathologic entities such as intramural thrombus, and may also allow improved evaluation of cardiac devices.”
    Black-blood cinematic rendering: A new method for cardiac CT intraluminal visualization
    Steven P. Rowe, Linda C. Chu, Hannah S. Recht, Cheng Ting Lin, Elliot K. Fishman
    Journal of Cardiovascular Computed Tomography (in press)

  • Black-blood cinematic rendering: A new method for cardiac CT intraluminal visualization
    Steven P. Rowe, Linda C. Chu, Hannah S. Recht, Cheng Ting Lin, Elliot K. Fishman
    Journal of Cardiovascular Computed Tomography (in press)

  • Black-blood cinematic rendering: A new method for cardiac CT intraluminal visualization
    Steven P. Rowe, Linda C. Chu, Hannah S. Recht, Cheng Ting Lin, Elliot K. Fishman
    Journal of Cardiovascular Computed Tomography (in press)

  • Black-blood cinematic rendering: A new method for cardiac CT intraluminal visualization
    Steven P. Rowe, Linda C. Chu, Hannah S. Recht, Cheng Ting Lin, Elliot K. Fishman
    Journal of Cardiovascular Computed Tomography (in press)
  • Results: The BBCR technique was successfully utilized to demonstrate intraluminal cardiac findings in a patient with a normal left ventricle, a patient with a left ventricular mural thrombus, and a patient status-post transcatheter aortic valve replacement.
    Conclusions: BBCR is a new method of utilizing volumetric chest CT data in order to provide detailed images of intraluminal anatomy and pathology of the heart. Further study of this promising method is warranted.
    Black-blood cinematic rendering: A new method for cardiac CT intraluminal visualization
    Steven P. Rowe, Linda C. Chu, Hannah S. Recht, Cheng Ting Lin, Elliot K. Fishman
    Journal of Cardiovascular Computed Tomography (in press)
  • “We have empirically developed a preset for the visualization of intraluminal structures within the heart and great vessels. We refer to this preset as black-blood cinematic rendering (BBCR) due to the observation that the visual effect is similar to the appearance of black-blood magnetic resonance imaging. In the following manuscript, we describe the BBCR methodology and provide relevant clinical examples of its application to gated cardiac CT data.”
    Black-blood cinematic rendering: A new method for cardiac CT intraluminal visualization
    Steven P. Rowe, Linda C. Chu, Hannah S. Recht, Cheng Ting Lin, Elliot K. Fishman
    Journal of Cardiovascular Computed Tomography (in press)
  • ”The examples in this manuscript demonstrate the potential utility of the BBCR preset to allow the visualization of normal intraluminal cardiac anatomy as well as important pathologic processes. Although this technique will need to be rigorously compared to other methods of cardiac imaging, there appears to be significant promise in utilizing BBCR for cardiac intraluminal visualization. In particular, the utility of BBCR for identifying changes in myocardial trabeculation (e.g. thinning due to prior infarct or left ventricular non-compaction cardiomyopathy), depicting artificial valves and other devices and diagnosing vegetations, and providing intraluminal views of aneurysms and pseudoaneurysms are all applications that merit exploration.”
    Black-blood cinematic rendering: A new method for cardiac CT intraluminal visualization
    Steven P. Rowe, Linda C. Chu, Hannah S. Recht, Cheng Ting Lin, Elliot K. Fishman
    Journal of Cardiovascular Computed Tomography (in press)
  • “In conclusion, the BBCR method appears to provide high levels of intraluminal anatomic detail for cardiac CT imaging. This may facilitate the detection of important pathologic entities such as intramural thrombus, and may also allow improved evaluation of cardiac devices.”
    Black-blood cinematic rendering: A new method for cardiac CT intraluminal visualization
    Steven P. Rowe, Linda C. Chu, Hannah S. Recht, Cheng Ting Lin, Elliot K. Fishman
    Journal of Cardiovascular Computed Tomography (in press)

  • Black-blood cinematic rendering: A new method for cardiac CT intraluminal visualization
    Steven P. Rowe, Linda C. Chu, Hannah S. Recht, Cheng Ting Lin, Elliot K. Fishman
    Journal of Cardiovascular Computed Tomography (in press)

  • Black-blood cinematic rendering: A new method for cardiac CT intraluminal visualization
    Steven P. Rowe, Linda C. Chu, Hannah S. Recht, Cheng Ting Lin, Elliot K. Fishman
    Journal of Cardiovascular Computed Tomography (in press)

  • Black-blood cinematic rendering: A new method for cardiac CT intraluminal visualization
    Steven P. Rowe, Linda C. Chu, Hannah S. Recht, Cheng Ting Lin, Elliot K. Fishman
    Journal of Cardiovascular Computed Tomography (in press)
Colon

  • Purpose Cinematic rendering (CR) is a new technique for visualizing volumetric three-dimensional data. The purpose of this study was to investigate the added value of CR to conventional computed tomography (CT) in the diagnosis and evaluation of ulcerative colitis (UC).
    Conclusion Adding CR to conventional CT improved the diagnostic performance of evaluating the extent of UC.
    Cinematic rendering: a new imaging approach for ulcerative colitis
    Jun Yang et al.
    Japanese Journal of Radiology https://doi.org/10.1007/s11604-019-00844-0
  • Purpose Cinematic rendering (CR) is a new technique for visualizing volumetric three-dimensional data. The purpose of this study was to investigate the added value of CR to conventional computed tomography (CT) in the diagnosis and evaluation of ulcerative colitis (UC).
    Conclusion Adding CR to conventional CT improved the diagnostic performance of evaluating the extent of UC.
    Cinematic rendering: a new imaging approach for ulcerative colitis
    Jun Yang et al.
    Japanese Journal of Radiology https://doi.org/10.1007/s11604-019-00844-0
Contrast

  • “Recently, a novel 3-dimensional visualization methodology for volumetric computed tomography data has become available. This method, known as cinematic rendering, uses an advanced lighting model to create photorealistic images from standard computed tomography acquisition data composed of isotropic voxels. We have observed that cinematic rendering visualizations in which patients have been administered dense, positive oral contrast do not have any substantive visual artifacts and can be used to demonstrate bowel pathology to advantage (ie, “virtual fluoroscopy”). In this technical note, we describe our acquisition and visualization parameters, and we also include demonstrative examples.”
    Cinematic Rendering With Positive Oral Contrast: Virtual Fluoroscopy
    Steven P. Rowe, Linda C. Chu, MD, Elliot K. Fishman,
    J Comput Assist Tomogr 2019;43: 718–720
  • “In this article, we describe our observation that CR can effectively display bowel anatomy and pathology after the administration of positive oral contrast with no perceptible visual artifacts. Indeed, the presence of positive oral contrast in CR images allows for highly detailed displays of the bowel mucosal fold pattern and might be considered a type of “virtual fluoroscopy.”
    Cinematic Rendering With Positive Oral Contrast: Virtual Fluoroscopy
    Steven P. Rowe, Linda C. Chu, MD, Elliot K. Fishman,
    J Comput Assist Tomogr 2019;43: 718–720

  • Cinematic Rendering With Positive Oral Contrast: Virtual Fluoroscopy
    Steven P. Rowe, Linda C. Chu, MD, Elliot K. Fishman,
    J Comput Assist Tomogr 2019;43: 718–720
  • “As demonstrated in this article, CR can produce photorealistic images of the oral contrast–opacified bowel without significant vi- sual artifact. The intrinsic advantages of CR, including high levels of surface detail and realistic shadowing, contribute to the visualization of the bowel in a manner analogous to fluoroscopic studies. In- deed, the quality of the visualizations would suggest that CR may have the ability to replace fluoroscopy in certain contexts, while preserving the anatomic and pathologic information that would normally be obtained from a fluoroscopic examination, with the exception that real-time imaging of motion of the bowel and compression maneuvers are not possible. The preset parameters included in this article may be helpful as a starting point for further evaluation of the utility of CR in this context.”
    Cinematic Rendering With Positive Oral Contrast: Virtual Fluoroscopy
    Steven P. Rowe, Linda C. Chu, MD, Elliot K. Fishman,
    J Comput Assist Tomogr 2019;43: 718–720
  • “Photorealistic CR images of the bowel after administration of positive oral contrast demonstrate detailed anatomy and pathology without evidence of visual artifacts. We postulate that CR of the bowel with positive contrast may be able to function as a “virtual fluoroscopy” in some contexts, although this will require significantly more studies to validate.”
    Cinematic Rendering With Positive Oral Contrast: Virtual Fluoroscopy
    Steven P. Rowe, Linda C. Chu, MD, Elliot K. Fishman,
    J Comput Assist Tomogr 2019;43: 718–720
  • “Recently, a novel 3-dimensional visualization methodology for volumetric computed tomography data has become available. This method, known as cinematic rendering, uses an advanced lighting model to create photorealistic images from standard computed tomography acquisition data composed of isotropic voxels. We have observed that cinematic rendering visualizations in which patients have been administered dense, positive oral contrast do not have any substantive visual artifacts and can be used to demonstrate bowel pathology to advantage (ie, “virtual fluoroscopy”). In this technical note, we describe our acquisition and visualization parameters, and we also include demonstrative examples.”
    Cinematic Rendering With Positive Oral Contrast: Virtual Fluoroscopy
    Steven P. Rowe, Linda C. Chu, MD, Elliot K. Fishman,
    J Comput Assist Tomogr 2019;43: 718–720
  • “In this article, we describe our observation that CR can effectively display bowel anatomy and pathology after the administration of positive oral contrast with no perceptible visual artifacts. Indeed, the presence of positive oral contrast in CR images allows for highly detailed displays of the bowel mucosal fold pattern and might be considered a type of “virtual fluoroscopy.”
    Cinematic Rendering With Positive Oral Contrast: Virtual Fluoroscopy
    Steven P. Rowe, Linda C. Chu, MD, Elliot K. Fishman,
    J Comput Assist Tomogr 2019;43: 718–720

  • Cinematic Rendering With Positive Oral Contrast: Virtual Fluoroscopy
    Steven P. Rowe, Linda C. Chu, MD, Elliot K. Fishman,
    J Comput Assist Tomogr 2019;43: 718–720
  • “As demonstrated in this article, CR can produce photorealistic images of the oral contrast–opacified bowel without significant vi- sual artifact. The intrinsic advantages of CR, including high levels of surface detail and realistic shadowing, contribute to the visualization of the bowel in a manner analogous to fluoroscopic studies. In- deed, the quality of the visualizations would suggest that CR may have the ability to replace fluoroscopy in certain contexts, while preserving the anatomic and pathologic information that would normally be obtained from a fluoroscopic examination, with the exception that real-time imaging of motion of the bowel and compression maneuvers are not possible. The preset parameters included in this article may be helpful as a starting point for further evaluation of the utility of CR in this context.”
    Cinematic Rendering With Positive Oral Contrast: Virtual Fluoroscopy
    Steven P. Rowe, Linda C. Chu, MD, Elliot K. Fishman,
    J Comput Assist Tomogr 2019;43: 718–720
  • “Photorealistic CR images of the bowel after administration of positive oral contrast demonstrate detailed anatomy and pathology without evidence of visual artifacts. We postulate that CR of the bowel with positive contrast may be able to function as a “virtual fluoroscopy” in some contexts, although this will require significantly more studies to validate.”
    Cinematic Rendering With Positive Oral Contrast: Virtual Fluoroscopy
    Steven P. Rowe, Linda C. Chu, MD, Elliot K. Fishman,
    J Comput Assist Tomogr 2019;43: 718–720
Deep Learning

  • ”Once an algorithm is deployed into clinical practice, legal and ethical challenges must be considered. When errors are made using AI algorithms, the question arises who is responsible for the mistakes made by a computer. Is it the radiologist, the AI application itself, or the company that made the AI application responsible? This question is especially important if the algorithm has not explained the inferences in terms that can be understood by humans such as bounding boxes or saliency maps. At times radiologists may not truly understand how AI algorithms arrive at certain conclusions. If we don't understand the process behind how AI algorithms work, how can we be held solely accountable for mistakes? This “black box” problem has made many groups, including the American Medical Association, develop policies that insist developers provide transparency and explicability in algorithm development.”
    Artificial intelligence in radiology: the ecosystem essential to improving patient care
    Julie Sogania,Bibb Allen Jr,Keith Dreyer,Geraldine McGinty
    Clinical Imaging (in press)
  • ” As AI continues to evolve, healthcare as we know it will dramatically change. Radiologists have always served at the forefront in adapting new technologies in medicine, and it should be no different with the advent of the AI revolution. AI will not replace radiologists; instead those radiologists who take advantage of AI may ultimately replace those who refuse to accept it. It is crucial we build an ecosystem of key players in technology, research, radiology, and the regulatory bodies who will work together to effectively and safely integrate AI into clinical practice. As a result, adoption of this technology will expand our efficiency and decision-making capabilities, leading to earlier and better detection of disease and improved outcomes for our patients.”
    Artificial intelligence in radiology: the ecosystem essential to improving patient care
    Julie Sogania,Bibb Allen Jr,Keith Dreyer,Geraldine McGinty
    Clinical Imaging (in press)
  • “The AI-based noise reduction could improve the IQ of aorta CTA with low kV and reduced CM, which achieved the potential of radiation dose and contrast media reduction compared with conventional aorta CTA protocol.”
    Application of Artificial Intelligence–based Image Optimization for Computed Tomography Angiography of the Aorta With Low Tube Voltage and Reduced Contrast Medium Volume
    Wang, Y et al.
    Journal of Thoracic Imaging (in press)
  • Purpose: The purpose of this study was to evaluate the impact of artificial intelligence (AI)-based noise on aorta computed tomography angiography (CTA) image quality (IQ) at 80 kVp tube voltage and 40 mL contrast medium (CM)
    Results: The image noise significantly decreased while signal-to-noise ratio and contrast-to-noise ratio significantly increased in the order of group A1, B, and A2 (all P<0.05). Compared with group B, the subjective IQ score of group A1 was significantly lower (P<0.05), while that of group A2 had no significant difference (P>0.05). The effective dose and CM volume of group A were reduced by 79.18% and 50%, respectively, than that of group B.
    Application of Artificial Intelligence–based Image Optimization for Computed Tomography Angiography of the Aorta With Low Tube Voltage and Reduced Contrast Medium Volume
    Wang, Y et al.
    Journal of Thoracic Imaging (in press)
  • “With machine learning, the input is based on hand-engineered features, while unsupervised deep learning is able to learn these features itself directly from data. Multiple research groups are working on applying AI to improve the reconstruction of CT images. One application is image-space-based reconstructions in which convolutional neural networks are trained with low-dose CT images to recon- struct routine-dose CT images. Another approach is to optimize IR algorithms.”
    The evolution of image reconstruction for CT—from filtered back projection to artificial intelligence
    Willemink MJ, Noël PB
    European Radiology (2019) 29:2185–2195
  • “Generally, IR algorithms are based on manually designed prior functions resulting in low-noise images without loss of structures. Deep learning methods allow for implementing more complex functions, which have the potential to enable lower-dose CT and sparse-sampling CT. These AI techniques have the potential to reduce CT radiation doses while speeding up reconstruction times. Also, deep learning can be used to optimize image quality without reducing the radiation dose, e.g., by more advanced DECT monochromatic image reconstruction and metal artifact reduction.”
    The evolution of image reconstruction for CT—from filtered back projection to artificial intelligence
    Willemink MJ, Noël PB
    European Radiology (2019) 29:2185–2195
  • “These methods are not yet ready for clinical implementation; however, it is expected that AI will play, in the near future, a major role in CT image reconstruction and restoration. We expect that AI will fit in current clinical CT imaging workflow by enhancing current reconstruction methods, for example by significantly accelerating the reconstruction process since application of a trained network can be instantaneously.”
    The evolution of image reconstruction for CT—from filtered back projection to artificial intelligence
    Willemink MJ, Noël PB
    European Radiology (2019) 29:2185–2195
  • “In this context, artificial-intelligence tools have been designed to support radiologists in the identification of lung nodules since when chest radiography was the diagnostic imaging modality of choice to detect lung cancer. With the advent of low-dose CT and in particular with its implementation in screening trials, many computer-aided detection (CAD) systems for lung nodule identification have been developed . The CAD potential in improving radiologists’ performance has been deeply investigated, highlighting that the CAD can successfully be used as a second reader.”
    The potential contribution of artificial intelligence to dose reduction in diagnostic imaging of lung cancer. 
    Retico A, Fantacci M
    Journal of Medical Artificial Intelligence, North America, 2, mar. 2019
  • “The research in lung cancer diagnosis is now advancing in two distinct fields: the improvement in the image acquisition instrumentation and reconstruction techniques based on iterative processes is allowing to obtain high-quality CT images even at low and ultra-low dose (i.e., a dose amount very similar to that of a chest radiography), whereas the recent acceleration in the implementation of deep-learning methods in the medical imaging field is leading to an enhancement of the performance of CAD systems across different imaging modalities, in both detection and diagnosis tasks.”
    The potential contribution of artificial intelligence to dose reduction in diagnostic imaging of lung cancer. 
    Retico A, Fantacci M
    Journal of Medical Artificial Intelligence, North America, 2, mar. 2019
  • “As AI continues to evolve,health care as we know it will dramatically change. Radiologists have always served at the forefront in adapting new technologies in medicine, and it should be no different with the advent of the AI revolution. I will not replace radiologists; instead those radiologists who take advantage of AI may ultimately replace those who refuse to accept it .It is crucial we build an ecosystem of key players in technology, research, radiology, and the regulatory bodies who will work together to effectively and safely integrate AI into clinical practice. As a of this technology will expand our efficiency and decision making capabilities, leading to earlier and better detection of disease and improve outcomes for our patients.”
    Artificial intelligence in radiology: the ecosystem essential to improving patient care
    Sogani J, Allen B Jr, K Dreyer, McGintgy GY
    Clinical Imaging (in press)
  • “Commercial iterative reconstruction techniques help to reduce the radiation dose of computed tomography (CT), but altered image appearance and artefacts can limit their adoptability and potential use. Deep learning has been investigated for low-dose CT (LDCT). Here, we design a modularized neural network for LDCT and compare it with commercial iterative reconstruction methods from three leading CT vendors. Although popular networks are trained for an end-to-end mapping, our network performs an end-to-process mapping so that intermediate denoised images are obtained with associated noise reduction directions towards a final denoised image.”
    Competitive performance of a modularized deep neural network compared to commercial algorithms for low-dose CT image reconstruction
    Hongming Shan et al.
    Nature Machine Intelligence volume 1, pages 269–276 (2019)
  • ”This study confirms that our deep learning approach performs either favourably or comparably in terms of noise suppression and structural fidelity, and is much faster than commercial iterative reconstruction algorithms.”
    Competitive performance of a modularized deep neural network compared to commercial algorithms for low-dose CT image reconstruction
    Hongming Shan et al.
    Nature Machine Intelligence volume 1, pages 269–276 (2019)

  • Competitive performance of a modularized deep neural network compared to commercial algorithms for low-dose CT image reconstruction
    Hongming Shan et al.
    Nature Machine Intelligence volume 1, pages 269–276 (2019)
  • “The proposed MAP-NN, enhanced by the radiologist in the loop, performs favourably or comparably relative to the clinically used iterative reconstruction methods implemented by the three leading CT vendors. Once the MAP-NN is trained, the DL-based denoising process is highly efficient (about 100 slices per second per mapping depth) and easy to use in clinical practice, while iterative reconstruction techniques are time-consuming and subject to significant artefacts.”
    Competitive performance of a modularized deep neural network compared to commercial algorithms for low-dose CT image reconstruction
    Hongming Shan et al.
    Nature Machine Intelligence volume 1, pages 269–276 (2019)
  • ”With the availability of raw data, CT denoising can be performed from the sinogram domain to the image space, utilizing all the information for the best denoising results. Clearly, it is now time for CT vendors to open their data format, perform machine learning and develop the next generation of CT image reconstruction algorithms in the DL framework.”
    Competitive performance of a modularized deep neural network compared to commercial algorithms for low-dose CT image reconstruction
    Hongming Shan et al.
    Nature Machine Intelligence volume 1, pages 269–276 (2019)
  • “In conclusion, our DL method provides better or similar image quality compared to commercial IR techniques from three CT vendors, and there is great potential for optimizing DL-based CT reconstruction methods that handle sinogram data directly.”
    Competitive performance of a modularized deep neural network compared to commercial algorithms for low-dose CT image reconstruction
    Hongming Shan et al.
    Nature Machine Intelligence volume 1, pages 269–276 (2019)
  • “Another emerging technique is artificial intelligence (AI). Besides classification of images, detection of objects and playing games, AI has gained substantial interest for its potential to improve reconstruction of CT images. AI, and more specifically machine learning, is a group of methods that is able to produce a mapping from raw inputs, such as intensities of individual pixels, to specific outputs, such as classification of a disease. With machine learning, the input is based on hand-engineered features, while unsupervised deep learning is able to learn these features itself directly from data.”
    The evolution of image reconstruction for CT—from filtered back projection to artificial intelligence
    Willemink MJ, Noël PB
    European Radiology (2019) 29:2185–2195
  • OBJECTIVE. Although extensive attention has been focused on the enormous potential of artificial intelligence (AI) technology, a major question remains: how should this fundamentally new technology be regulated? The purpose of this article is to provide an overview of the pathways developed by the U.S. Food and Drug Administration to regulate the incorporation of AI in medical imaging.
    CONCLUSION. AI is the new wave of innovation in health care. The technology holds promising applications to revolutionize all aspects of medicine.
    Concepts in U.S. Food and Drug Administration Regulation of Artificial Intelligence for Medical Imaging
    Kohli A et al.
    AJR 2019; 213:886–888
  • “Unlike the regulation of drugs and devices, the regulation of AI by the FDA poses unique challenges. In its Digital Health In- novation Action Plan, the FDA acknowledged that the traditional approach to evaluating hardware-based medical devices is not suited for the faster iterative design of software- based medical technologies. This is partly because of the inherent variability in the parameters of AI-based technologies, which depend on both the nature and the source of the data. For example, in a recent study on deep learning algorithms for the auto- mated detection of an anterior cruciate ligament tear on knee MRI, the algorithm had an AUC value of 0.824 for an external test dataset and an AUC value of 0.937 for an internal test dataset. The veracity of algorithms would have to be judged by two witnesses, so to speak.”
    Concepts in U.S. Food and Drug Administration Regulation of Artificial Intelligence for Medical Imaging
    Kohli A et al.
    AJR 2019; 213:886–888
  • “Traditional image processing techniques were rule based and predictable, and they relied on well-defined features such as the size, texture, and heterogeneity of a lesion. AI- based technologies often use deep learning in which large amounts of data are fed into a computer system and the computer develops rules to predict outcomes from the data. A technology that learns on its own has an explainability problem—that is, we do not know how it arrived at the rules it derived from the data. The explainability problem makes it difficult to benchmark AI.”
    Concepts in U.S. Food and Drug Administration Regulation of Artificial Intelligence for Medical Imaging
    Kohli A et al.
    AJR 2019; 213:886–888
  • ”Class I devices, which are classified as low risk, are typically exempt from PMA review; an example of a class I device is an algorithm that merely labels nodules in a chest CT, rather than stating which nodule is malignant, so the nodules are brought to the attention of radiologists. Most AI algorithms are categorized as class I devices or are excluded from being designated as a device as outlined by the recent 21st Century Cures Act updated draft guidelines.”
    Concepts in U.S. Food and Drug Administration Regulation of Artificial Intelligence for Medical Imaging
    Kohli A et al.
    AJR 2019; 213:886–888
  • “Technology that lacks a predicate device (i.e., a predecessor) is a revolutionary technology. Because the technology is brand new, no evidence has accrued that can form a framework for regulation. Although AI- based imaging algorithms are fairly new, they can be either evolutionary or revolutionary. Quantification of coronary calcium or detection of a lung nodule with the use of machine learning techniques would be con- sidered evolutionary because these tasks have already been performed by software using rule-based automatic and semiautomatic methods.”
    Concepts in U.S. Food and Drug Administration Regulation of Artificial Intelligence for Medical Imaging
    Kohli A et al.
    AJR 2019; 213:886–888
  • “Computer-aided detection—CAD systems flag abnormalities for review by radiologists but do not assist in diagnostic or clinical decision making. They focus on the detection of abnormalities rather than their characterization. Examples of CAD include identification of colonic polyps on CT colonography, filling defects on pulmonary embolism CT, or liver lesions on CT or MRI. Critically, CAD analysis does not include further analysis of these lesions; instead, it flags a finding for clinician review but does not directly make a diagnosis of colon cancer, pulmonary embolism, hepatic malignancy, or other abnormality.”
    Concepts in U.S. Food and Drug Administration Regulation of Artificial Intelligence for Medical Imaging
    Kohli A et al.
    AJR 2019; 213:886–888
  • “Computer-aided diagnosis—CADx systems take analytics to a higher level than CAD systems. The FDA characterizes CADx not only as identifying the disease but also as providing an assessment of the disease through either a specific diagnosis or differential diagnosis as well as determining the extent of disease, the prognosis, and the presence of other known conditions. Thus, CADx involves the role of CAD, al- though the opposite is not true. As an example, CADx technology might identify lung nodules on CT (CAD) and might also pro- vide a malignancy score for those lesions (CADx)”.
    Concepts in U.S. Food and Drug Administration Regulation of Artificial Intelligence for Medical Imaging
    Kohli A et al.
    AJR 2019; 213:886–888
  • “PMA is the most stringent of the approval pathways. PMA approval is based on a deter- mination by the FDA that there is sufficient valid scientific evidence to ensure that the device is safe and effective for its intended use. This generally requires rigorous nonclinical and clinical studies to be conducted that show evidence of safety and efficacy in a substantial population. This is generally the pathway for class III devices that are considered high risk for patients or those that are revolutionary.”
    Concepts in U.S. Food and Drug Administration Regulation of Artificial Intelligence for Medical Imaging
    Kohli A et al.
    AJR 2019; 213:886–888
  • “Although AI may potentially revolutionize health care, it is often considered only evolutionary from the FDA’s point of view, because often a predicate de- vice can be identified so that the demanding PMA process can be avoided and a 510(k) approach can be pursued. For instance, newer image postprocessing algorithms that use deep learning have used commercially available postprocessing software that does not use deep learning as a predicate, and these algorithms have gone through the 510(k) pathway for FDA approval.”
    Concepts in U.S. Food and Drug Administration Regulation of Artificial Intelligence for Medical Imaging
    Kohli A et al.
    AJR 2019; 213:886–888
  • “More recently, the FDA developed the Digital Health Software Precertification (Pre-Cert) Program. This program is based on the assumption that because medical soft- ware evolves so rapidly, every iteration of a particular technology cannot realistically be reviewed by the FDA. This approach specifically regulates software by primarily evaluating the developer of the product rather than the product itself, thus deviating from the traditional approval processes that directly evaluated a particular product.”
    Concepts in U.S. Food and Drug Administration Regulation of Artificial Intelligence for Medical Imaging
    Kohli A et al.
    AJR 2019; 213:886–888
  • “The Pre-Cert program mirrors the Transportation Security Administration (TSA) Pre-Check program, because prevented companies are given a higher level of trust after meeting certain rigorous certification criteria. Several participants, including ma- jor consumer electronic companies, have already been enrolled in an early pilot version of this program. The participants will pro- vide the FDA access to the measures they use to develop, test, and maintain software products, including ways that they collect post- market data. After attaining certification, they will then undergo periodic audits rather than constant stepwise reviews as their dynamic products change. This approach may be a key solution to the rapid nature of software development and the associated workload burdens affecting the approval system.”
    Concepts in U.S. Food and Drug Administration Regulation of Artificial Intelligence for Medical Imaging
    Kohli A et al.
    AJR 2019; 213:886–888
  • “Medical device companies generally take one of three paths to gain regulatory approval: they seek approval in the United States first, seek approval overseas first, or seek approval in the United States and overseas in tandem. To develop a viable business strategy, a medical device company must understand the strengths and weaknesses of the regulatory system, its target market, the amount of internal and external resources required, and the amount of reimbursement available. In general, release in the United States requires a higher capital investment but gives a company access to the widest market, better intellectual property protection, and less foreign competition.”
    Concepts in U.S. Food and Drug Administration Regulation of Artificial Intelligence for Medical Imaging
    Kohli A et al.
    AJR 2019; 213:886–888
  • “Although AI algorithms pose a unique challenge to medical regulation agencies, these challenges are being acknowledged and addressed by the FDA, which recognizes that the standards by which medical tech- nology is evaluated may not apply to AI. By creating novel regulatory pathways, the FDA is encouraging the adoption of AI in medicine. The exact regulatory pathway and burden will be determined by intent—that is, whether AI is used for detection or diagnosis and whether is it used as an adjunct or a replacement. Regulatory standards are likely to evolve as AI algorithms become more robust and widespread.”
    Concepts in U.S. Food and Drug Administration Regulation of Artificial Intelligence for Medical Imaging
    Kohli A et al.
    AJR 2019; 213:886–888
  • “The medico-legal issue that then arises is the question of “who is responsible for the diagnosis,” especially if it is wrong. Whether data scientists or manufacturers involved in development, marketing, and installation of AI systems will carry the ultimate legal responsibility for adverse outcomes arising from AI algorithm use is a dif- ficult legal question; if doctors are no longer the primary agents of interpretation of radiological studies, will they still be held accountable?”
    What the radiologist should know about artificial intelligence – an ESR white paper
    Insights into Imaging (2019) 10:44 https://doi.org/10.1186/s13244-019-0738-2 
  • ”If radiologists monitor AI system outputs and still have a role in validating AI interpretations, do they still carry the ultimate responsibility, even though they do not understand, and cannot interrogate the precise means by which a diagnosis was determined? This “black box” element of AI poses many challenges, not least to the basic human need to under- stand how and why important decisions were made."
    What the radiologist should know about artificial intelligence – an ESR white paper
    Insights into Imaging (2019) 10:44 https://doi.org/10.1186/s13244-019-0738-2 
  • “Furthermore, if patient data are used to build AI products which go on to generate profit, consideration needs to be given to the issue of intellectual property rights. Do the involved patients and the collecting organizations have a right to share in the profits that derive from their data?”
    What the radiologist should know about artificial intelligence – an ESR white paper
    Insights into Imaging (2019) 10:44 https://doi.org/10.1186/s13244-019-0738-2 
  • “Fundamentally, each patient whose data is used by a third party should pro- vide consent for that use, and that consent may need to be obtained afresh if the data is re-used in a different context (e.g., to train an updated software version). Moreover, ownership of imaging datasets varies from one jurisdiction to another. In many countries, the ultimate ownership of such personal data resides with the patient, although the data may be stored, with consent, in a hospital or imaging centre repository.
    What the radiologist should know about artificial intelligence – an ESR white paper
    Insights into Imaging (2019) 10:44 https://doi.org/10.1186/s13244-019-0738-2 
  • The real challenge is not to oppose the incorporation of AI into the professional lives (a futile effort) but to embrace the inevitable change of radiological practice, incorporating AI in the radiological workflow. The most likely danger is that “[w]e’ll do what computers tell us to do, because we’re awestruck by them and trust them to make important decisions”
    What the radiologist should know about artificial intelligence – an ESR white paper
    Insights into Imaging (2019) 10:44 https://doi.org/10.1186/s13244-019-0738-2 
  • Radiomics: extraction of features from diagnostic images, the final product of which is a quantitative feature/parameter, measurable and mineable from images. A Radiomics analysis can extract over 400 features from a region of interest in a CT, MRI, or PET study, and correlate these features with each other and other data, far beyond the capability of the human eye or brain to appreciate. Such features may be used to predict prognosis and response to treatment . AI can support analysis of radiomics features and help in the correlation between radiomics and other data (proteomics, genomics, liquid biopsy, etc.) by building patients’ signatures.
    What the radiologist should know about artificial intelligence – an ESR white paper
    Insights into Imaging (2019) 10:44 https://doi.org/10.1186/s13244-019-0738-2 
  • Radiomics: extraction of features from diagnostic images, the final product of which is a quantitative feature/parameter, measurable and mineable from images. A Radiomics analysis can extract over 400 features from a region of interest in a CT, MRI, or PET study, and correlate these features with each other and other data, far beyond the capability of the human eye or brain to appreciate. Such features may be used to predict prognosis and response to treatment . AI can support analysis of radiomics features and help in the correlation between radiomics and other data (proteomics, genomics, liquid biopsy, etc.) by building patients’ signatures.
    What the radiologist should know about artificial intelligence – an ESR white paper
    Insights into Imaging (2019) 10:44 https://doi.org/10.1186/s13244-019-0738-2 
  • ”The main difference between CAD and “true” AI is that CAD only makes diagnoses for which it has been specifically trained and bases its performance on a training dataset and a rigid scheme of recognition that can only be improved if more datasets are given to the CAD algorithm. True AI is characterised by the process of autonomous learning, without explicit programming of each step, based on a network of algorithms and connections, similar to what humans do.”
    What the radiologist should know about artificial intelligence – an ESR white paper
    Insights into Imaging (2019) 10:44 https://doi.org/10.1186/s13244-019-0738-2
  • ”AI can be an optimizing tool for assisting the technologist and radiologist in choosing a personalised patient’s protocol, in tracking the patient’s dose parameters, and in providing an estimate of the radiation risks associated with cumulative dose and the patient’s susceptibility (age and other clinical parameters).”
    What the radiologist should know about artificial intelligence – an ESR white paper
    Insights into Imaging (2019) 10:44 https://doi.org/10.1186/s13244-019-0738-2 
  • ”The main difference between CAD and “true” AI is that CAD only makes diagnoses for which it has been specifically trained and bases its performance on a training dataset and a rigid scheme of recognition that can only be improved if more datasets are given to the CAD algorithm. True AI is characterised by the process of autonomous learning, without explicit programming of each step, based on a network of algorithms and connections, similar to what humans do.”
    What the radiologist should know about artificial intelligence – an ESR white paper
    Insights into Imaging (2019) 10:44 https://doi.org/10.1186/s13244-019-0738-2
  • ”AI can be an optimizing tool for assisting the technologist and radiologist in choosing a personalised patient’s protocol, in tracking the patient’s dose parameters, and in providing an estimate of the radiation risks associated with cumulative dose and the patient’s susceptibility (age and other clinical parameters).”
    What the radiologist should know about artificial intelligence – an ESR white paper
    Insights into Imaging (2019) 10:44 https://doi.org/10.1186/s13244-019-0738-2 
  • “Much fear has been generated among radiologists by the statements in public media from researchers engaged in AI development, predicting the imminent extinction of our specialty. For example, Andrew Ng (Stanford) stated that “[a] highly-trained and specialised radiologist may now be in greater danger of being replaced by a machine than his own executive assistant”, whereas Geoffrey Hinton (Toronto) said “if you work as a radiologist, you’re like the coyote that’s already over the edge of the cliff, but hasn’t yet looked down so doesn’t realise there’s no ground underneath him. People should stop training radiologists now. It’s just completely obvious that within 5 years, deep learning is going to do better than radiologists. We’ve got plenty of radiologists already ”.
    What the radiologist should know about artificial intelligence – an ESR white paper
    Insights into Imaging (2019) 10:44 https://doi.org/10.1186/s13244-019-0738-2
  • “Much fear has been generated among radiologists by the statements in public media from researchers engaged in AI development, predicting the imminent extinction of our specialty. For example, Andrew Ng (Stanford) stated that “[a] highly-trained and specialized radiologist may now be in greater danger of being replaced by a machine than his own executive assistant””.
    What the radiologist should know about artificial intelligence – an ESR white paper
    Insights into Imaging (2019) 10:44 https://doi.org/10.1186/s13244-019-0738-2 
  • “Unlike the regulation of drugs and devices, the regulation of AI by the FDA poses unique challenges. In its Digital Health In- novation Action Plan, the FDA acknowledged that the traditional approach to evaluating hardware-based medical devices is not suited for the faster iterative design of software- based medical technologies. This is partly because of the inherent variability in the parameters of AI-based technologies, which depend on both the nature and the source of the data. For example, in a recent study on deep learning algorithms for the auto- mated detection of an anterior cruciate ligament tear on knee MRI, the algorithm had an AUC value of 0.824 for an external test dataset and an AUC value of 0.937 for an internal test dataset. The veracity of algorithms would have to be judged by two witnesses, so to speak.”
    Concepts in U.S. Food and Drug Administration Regulation of Artificial Intelligence for Medical Imaging
    Kohli A et al.
    AJR 2019; 213:886–888
  • “Traditional image processing techniques were rule based and predictable, and they relied on well-defined features such as the size, texture, and heterogeneity of a lesion. AI- based technologies often use deep learning in which large amounts of data are fed into a computer system and the computer develops rules to predict outcomes from the data. A technology that learns on its own has an explainability problem—that is, we do not know how it arrived at the rules it derived from the data. The explainability problem makes it difficult to benchmark AI.”
    Concepts in U.S. Food and Drug Administration Regulation of Artificial Intelligence for Medical Imaging
    Kohli A et al.
    AJR 2019; 213:886–888
  • ”Class I devices, which are classified as low risk, are typically exempt from PMA review; an example of a class I device is an algorithm that merely labels nodules in a chest CT, rather than stating which nodule is malignant, so the nodules are brought to the attention of radiologists. Most AI algorithms are categorized as class I devices or are excluded from being designated as a device as outlined by the recent 21st Century Cures Act updated draft guidelines.”
    Concepts in U.S. Food and Drug Administration Regulation of Artificial Intelligence for Medical Imaging
    Kohli A et al.
    AJR 2019; 213:886–888
  • “Technology that lacks a predicate device (i.e., a predecessor) is a revolutionary technology. Because the technology is brand new, no evidence has accrued that can form a framework for regulation. Although AI- based imaging algorithms are fairly new, they can be either evolutionary or revolutionary. Quantification of coronary calcium or detection of a lung nodule with the use of machine learning techniques would be con- sidered evolutionary because these tasks have already been performed by software using rule-based automatic and semiautomatic methods.”
    Concepts in U.S. Food and Drug Administration Regulation of Artificial Intelligence for Medical Imaging
    Kohli A et al.
    AJR 2019; 213:886–888
  • “Computer-aided detection—CAD systems flag abnormalities for review by radiologists but do not assist in diagnostic or clinical decision making. They focus on the detection of abnormalities rather than their characterization. Examples of CAD include identification of colonic polyps on CT colonography, filling defects on pulmonary embolism CT, or liver lesions on CT or MRI. Critically, CAD analysis does not include further analysis of these lesions; instead, it flags a finding for clinician review but does not directly make a diagnosis of colon cancer, pulmonary embolism, hepatic malignancy, or other abnormality.”
    Concepts in U.S. Food and Drug Administration Regulation of Artificial Intelligence for Medical Imaging
    Kohli A et al.
    AJR 2019; 213:886–888
  • “Computer-aided diagnosis—CADx systems take analytics to a higher level than CAD systems. The FDA characterizes CADx not only as identifying the disease but also as providing an assessment of the disease through either a specific diagnosis or differential diagnosis as well as determining the extent of disease, the prognosis, and the presence of other known conditions. Thus, CADx involves the role of CAD, al- though the opposite is not true. As an example, CADx technology might identify lung nodules on CT (CAD) and might also pro- vide a malignancy score for those lesions (CADx)”.
    Concepts in U.S. Food and Drug Administration Regulation of Artificial Intelligence for Medical Imaging
    Kohli A et al.
    AJR 2019; 213:886–888
  • “PMA is the most stringent of the approval pathways. PMA approval is based on a deter- mination by the FDA that there is sufficient valid scientific evidence to ensure that the device is safe and effective for its intended use. This generally requires rigorous nonclinical and clinical studies to be conducted that show evidence of safety and efficacy in a substantial population. This is generally the pathway for class III devices that are considered high risk for patients or those that are revolutionary.”
    Concepts in U.S. Food and Drug Administration Regulation of Artificial Intelligence for Medical Imaging
    Kohli A et al.
    AJR 2019; 213:886–888
  • “Although AI may potentially revolutionize health care, it is often considered only evolutionary from the FDA’s point of view, because often a predicate de- vice can be identified so that the demanding PMA process can be avoided and a 510(k) approach can be pursued. For instance, newer image postprocessing algorithms that use deep learning have used commercially available postprocessing software that does not use deep learning as a predicate, and these algorithms have gone through the 510(k) pathway for FDA approval.”
    Concepts in U.S. Food and Drug Administration Regulation of Artificial Intelligence for Medical Imaging
    Kohli A et al.
    AJR 2019; 213:886–888
  • “More recently, the FDA developed the Digital Health Software Precertification (Pre-Cert) Program. This program is based on the assumption that because medical soft- ware evolves so rapidly, every iteration of a particular technology cannot realistically be reviewed by the FDA. This approach specifically regulates software by primarily evaluating the developer of the product rather than the product itself, thus deviating from the traditional approval processes that directly evaluated a particular product.”
    Concepts in U.S. Food and Drug Administration Regulation of Artificial Intelligence for Medical Imaging
    Kohli A et al.
    AJR 2019; 213:886–888
  • “The Pre-Cert program mirrors the Transportation Security Administration (TSA) Pre-Check program, because prevented companies are given a higher level of trust after meeting certain rigorous certification criteria. Several participants, including ma- jor consumer electronic companies, have already been enrolled in an early pilot version of this program. The participants will pro- vide the FDA access to the measures they use to develop, test, and maintain software products, including ways that they collect post- market data. After attaining certification, they will then undergo periodic audits rather than constant stepwise reviews as their dynamic products change. This approach may be a key solution to the rapid nature of software development and the associated workload burdens affecting the approval system.”
    Concepts in U.S. Food and Drug Administration Regulation of Artificial Intelligence for Medical Imaging
    Kohli A et al.
    AJR 2019; 213:886–888
  • “Medical device companies generally take one of three paths to gain regulatory approval: they seek approval in the United States first, seek approval overseas first, or seek approval in the United States and overseas in tandem. To develop a viable business strategy, a medical device company must understand the strengths and weaknesses of the regulatory system, its target market, the amount of internal and external resources required, and the amount of reimbursement available. In general, release in the United States requires a higher capital investment but gives a company access to the widest market, better intellectual property protection, and less foreign competition.”
    Concepts in U.S. Food and Drug Administration Regulation of Artificial Intelligence for Medical Imaging
    Kohli A et al.
    AJR 2019; 213:886–888
  • “Although AI algorithms pose a unique challenge to medical regulation agencies, these challenges are being acknowledged and addressed by the FDA, which recognizes that the standards by which medical tech- nology is evaluated may not apply to AI. By creating novel regulatory pathways, the FDA is encouraging the adoption of AI in medicine. The exact regulatory pathway and burden will be determined by intent—that is, whether AI is used for detection or diagnosis and whether is it used as an adjunct or a replacement. Regulatory standards are likely to evolve as AI algorithms become more robust and widespread.”
    Concepts in U.S. Food and Drug Administration Regulation of Artificial Intelligence for Medical Imaging
    Kohli A et al.
    AJR 2019; 213:886–888
  • OBJECTIVE. Although extensive attention has been focused on the enormous potential of artificial intelligence (AI) technology, a major question remains: how should this fundamentally new technology be regulated? The purpose of this article is to provide an overview of the pathways developed by the U.S. Food and Drug Administration to regulate the incorporation of AI in medical imaging.
    CONCLUSION. AI is the new wave of innovation in health care. The technology holds promising applications to revolutionize all aspects of medicine.
    Concepts in U.S. Food and Drug Administration Regulation of Artificial Intelligence for Medical Imaging Kohli A et al.
    AJR 2019; 213:886–888
  • “Geoffrey Hinton (Toronto) said “if you work as a radiologist, you’re like the coyote that’s already over the edge of the cliff, but hasn’t yet looked down so doesn’t realise there’s no ground underneath him. People should stop training radiologists now. It’s just completely obvious that within 5 years, deep learning is going to do better than radiologists. We’ve got plenty of radiologists already ”.
    What the radiologist should know about artificial intelligence – an ESR white paper
    Insights into Imaging (2019) 10:44 https://doi.org/10.1186/s13244-019-0738-2
  • “Deep learning is a subset of machine learning and is the basis of most AI tools for image interpretation. Deep learning means that the computer has multiple layers of algorithms interconnected and stratified into hierarchies of importance (like more or less meaningful data). These layers accumulate data from inputs and provide an output that can change step by step once the AI system learns new features from the data. Such multi-layered algorithms form large artificial neural networks.”
    What the radiologist should know about artificial intelligence – an ESR white paper
    Insights into Imaging (2019) 10:44 https://doi.org/10.1186/s13244-019-0738-2
Kidney

  • “The traditional technique for CTU is to acquire non-contrast images, administer the full contrast bolus and then acquire images in the nephrographic phase (80 to 120 s) and delayed excretory phase (5 to 15 min). Additional image acquisition in the corticomedullary phase (30 to 40 s) is optional and performed at some institutions. Following the image acquisitions, coronal and sagittal reformations are incorporated in most CTU protocols to increase sensitivity and visualization of the kidneys and urothelium. This single bolus technique yields maximal opacification and distension of the urinary tract because the entire administered intravenous contrast volume contributes to the excretory phase. This is also the simplest technique for the technologist to perform, as it only requires a single contrast injection.”
    CT urography: how to optimize the technique
    Karen Cheng et al.
    Abdominal Radiology https://doi.org/10.1007/s00261-019-02111-2
  • “Accordingly, at our institution the corticomedullary phase is not performed; we obtain three acquisitions, beginning with non-contrast images to the level of the pelvic brim, after which we administer 120 mL of Iohexol (Omnipaque 350, GE Healthcare), followed by a bolus of 250 mL of normal saline. Post-contrast images through the abdomen and pelvis are then acquired during nephrographic phase (90 s) and excretory phase (10 min).”
    CT urography: how to optimize the technique
    Karen Cheng et al.
    Abdominal Radiology https://doi.org/10.1007/s00261-019-02111-2
  • “In general, longer delay times of at least 10 min are recommended to increase the likelihood that the distal ureter will be adequately opacified. However, waiting for an excessive time can increase the density of contrast within the ureters and bladder. Although appropriate windowing of images can help in certain cases, resultant streak artifact can be a problem with extremely dense excreted contrast within the collecting system. At our institution, we obtain the excretory phase images at 10 min following the single bolus injection to balance competing concerns of under-distension and excessive contrast density.”
    CT urography: how to optimize the technique
    Karen Cheng et al.
    Abdominal Radiology https://doi.org/10.1007/s00261-019-02111-2 
  • “Split bolus technique is recommended in younger patients because it reduces the radiation dose. However, it is less sensitive for the detection of smaller renal cell carcinomas since fewer post-contrast phases are available and there may be streak artifact present from contrast in the collecting system. Additionally, since only a part of the total contrast bolus contributes to the excretory phase, this technique has poorer contrast opacification and distension of the urinary tract. Despite this limitation, split bolus CTU has been reported to have a similar sensitivity for upper tract urothelial carcinoma as standard single bolus technique.”
    CT urography: how to optimize the technique
    Karen Cheng et al.
    Abdominal Radiology https://doi.org/10.1007/s00261-019-02111-2 
  • “Several different 3D reformation techniques can be utilized to enhance visualization of the urinary tract without increasing radiation exposure, including maximal intensity projection images (MIP), average intensity projection (AIP), volume-rendered reconstruction (VR), and curved planar reformats (CPR).”
    CT urography: how to optimize the technique
    Karen Cheng et al.
    Abdominal Radiology https://doi.org/10.1007/s00261-019-02111-2 
  • “Patient hydration, either with oral fluid administration or intravenous saline, may increase urine output, thereby increasing distension and opacification of the urinary tract and bladder. Adequate distension is critical in the visualization of subtle tumors, which may otherwise be undetectable. Hydration does not significantly hinder workflow and is a relatively safe and easy ancillary technique to most CTU protocols.”
    CT urography: how to optimize the technique
    Karen Cheng et al.
    Abdominal Radiology https://doi.org/10.1007/s00261-019-02111-2 
  • “CTU is a powerful tool and a highly useful imaging technique that allows for the detection and characterization of both benign and malignant conditions involving the urinary tract. The utility of CTU is now widely recognized and has become the imaging of choice in the evaluation of asymptomatic hematuria.”
    CT urography: how to optimize the technique
    Karen Cheng et al.
    Abdominal Radiology https://doi.org/10.1007/s00261-019-02111-2 
Pancreas

  • ”Paraduodenal pancreatitis (PDP) is a rare form of chronic pancreatitis that affects the space between the duodenal “C” loop and the head of the pancreas known as the pancreaticoduodenal groove. Two forms of PDP have been described—the pure form which involves the groove only and the segmental form which involves the groove and extends to the pancreatic head. The actual incidence of PDP is unknown, however an incidence of 2.7%, 19.5%, and 24.4% have been reported in three separate surgical series of patients undergoing pancreaticoduodenectomies.”
    Inflammatory mimickers of pancreatic adenocarcinoma
    Kunal Kothar ET AL.
    Abdominal Radiology (In Press, 2019) https://doi.org/10.1007/s00261-019-02233-7 
  • “The typical patient affected with PDP is a middle- aged male (40–50 years of age) with a history of significant alcohol abuse which can be more suggestive of the diagnosis. MDCT findings vary between the pure and segmental forms. The pure form may appear as ill-defined fat stranding/inflammatory changes in the pancreaticoduodenal groove or as a soft tissue in the pancreaticoduodenal groove often with a “sheetlike” curvilinear crescentic appearance that is best appreciated on coronal multiplanar reformatted images. Thickening of the medial duodenal wall may be seen, especially on the coronal images. The segmental form can be more difficult to observe because involvement of the groove is often concealed by mass-like enlargement of the pancreatic head.”
    Inflammatory mimickers of pancreatic adenocarcinoma
    Kunal Kothar ET AL.
    Abdominal Radiology (In Press, 2019) https://doi.org/10.1007/s00261-019-02233-7 
  • ”Inflammatory pancreatic processes can make it difficult for the radiologist to discover an underlying PDA. It is important for radiologists to assess for subtle clues to distinguish these processes from PDA. In the setting of an older patient without risk factors for pancreatitis, findings such as focal pancreatitis duct dilatation with abrupt duct cut-off may signal an underlying PDA. Mass-forming inflammatory conditions such as chronic pancreatitis can be confused with PDA, however supplementary findings, such as a smoothly stenotic pancreatic duct penetrating through the mass (duct-penetrating sign), can help favor a mass-forming inflammatory condition.”
    Inflammatory mimickers of pancreatic adenocarcinoma
    Kunal Kothar ET AL.
    Abdominal Radiology (In Press, 2019) https://doi.org/10.1007/s00261-019-02233-7 
  • “Type 1 auto- immune pancreatitis demonstrates elevated levels of serum IgG4 and is associated with IgG4-related disease involving other organs besides the pancreas. The diagnostic feature of Type 2 is extensive infiltration of the pancreatic ductal epithelium by neutrophils known as granulocyte epithelial lesion (GEL) on histopathological analysis. Type 1 AIP has a higher incidence in older males and those from Asia, while type 2 AIP favors younger patients in the United States and Europe without a clear gender predilection. Both types tend to respond to corticosteroids, however type 1 more commonly relapses (ranging from 30 to 60% of patients). Furthermore, the two types cannot be easily differentiated radiographically. Type 1 is more often associated with other organ involvement.”
    Inflammatory mimickers of pancreatic adenocarcinoma
    Kunal Kothar ET AL.
    Abdominal Radiology (In Press, 2019) https://doi.org/10.1007/s00261-019-02233-7 
  • Mass-forming autoimmune pancreatitis accounts for approximately 33 to 41% of all cases of autoimmune pancreatitis. It is important to distinguish this process from pancreatic adenocarcinoma to avoid potentially unnecessary surgical intervention in patients with autoimmune pancreatitis. Kamisawa et al. reported that approximately 19% of patients with autoimmune pancreatitis had surgery because they were misdiagnosed as having pancreatic or bile duct cancer.”
    Inflammatory mimickers of pancreatic adenocarcinoma
    Kunal Kothar ET AL.
    Abdominal Radiology (In Press, 2019) https://doi.org/10.1007/s00261-019-02233-7
  • “The presence of extrapancreatic manifestations of autoimmune pancreatitis are important to identify as they can suggest that a pancreatic mass may represent autoimmune pancreatitis. Renal involvement includes small peripheral cortical nodules, round lesions, well-defined wedge-shaped lesions or diffuse patchy involvement. Biliary involvement, retroperitoneal fibrosis, periaortitis and other features of IgG4-related systemic disease and improvement of imaging findings following treatment with corticosteroids suggests autoimmune pancreatitis.”
    Inflammatory mimickers of pancreatic adenocarcinoma
    Kunal Kothar ET AL.
    Abdominal Radiology (In Press, 2019) https://doi.org/10.1007/s00261-019-02233-7 
  • “A 2015 analysis of the National Cancer Institute’s Surveillance, Epidemiology, and End Results database found that patients with stage IV pancreatic cancer were, on average, only 1.3 years older than those with stage I disease. Chromothripsis, in which multiple chromosomal rearrangements occur in a single event on 1 or 2 chromosomes, may drive the rapid progression of some pancreatic cancers. Furthermore, recent 3-dimensional histological studies suggest that early stage pancreatic cancers often invade the veins, which drain directly to the liver and result in early metastatic spread.6 These hypotheses are supported by clinical data that show at diagnosis, more than half of patients with pancreatic cancer present with metastatic disease, and only 10% of patients have localized cancer.”
    Screening for Pancreatic Cancer—Is There Hope?
    Anne Marie Lennon, Ralph H. Hruban, Alison P. Klein
    JAMA (in press) 2019
  • “How can these challenges be addressed? First, there has been progress in characterizing the curable neoplasms that give rise to advanced, incurable pancreatic cancers. All 3 precursor lesions that lead to pancreatic cancer are known and well-characterized: pancreatic intraepithelial neoplasia (PanIN), intraductal papillary mucinous neoplasms (IPMNs), and mucinous cystic neoplasms (MCNs). Although high-quality data are lacking, the majority of cancers originate from PanINs, which measure less than 5 mm in diameter and can only be seen using a microscope. Together, IMPNs and MCNs ac- count for about 15% to 30% of cancers.”
    Screening for Pancreatic Cancer—Is There Hope?
    Anne Marie Lennon, Ralph H. Hruban, Alison P. Klein
    JAMA (in press) 2019
  • “Although the prevalence of pancreatic cancer is relatively low, groups with significantly increased risk can be identified and their risk quantified. High- risk populations include individuals with IPMNs or MCNs; a strong family history of pancreatic cancer (at least 2 family members); a germline pathogenic variant in BRCA1, BRCA2, p16/ CDKN2A, PALB2, STK11, ATM, PRSS1, and the DNA mismatch re- pair genes; and older individuals with new onset diabetes mellitus. Surveillance is currently recommended for individuals who are found to have an IPMN or MCN, and the International Cancer of the Pancreas Screening Consortium has developed guidelines for screening high-risk individuals.”
    Screening for Pancreatic Cancer—Is There Hope?
    Anne Marie Lennon, Ralph H. Hruban, Alison P. Klein
    JAMA (in press) 2019
  • “Second, with current technology it is often impossible to distinguish between pancreatic precursors that harbor either early cancer or high-grade dysplasia, which pose a risk great enough to warrant surgical resection, and low-grade precursors that can be safely watched. Thus, up to 60% of patients who undergo surgical resection for a precursor lesion are found to have a lesion with a low risk of progression and not to re- quire surgery at the time. Because pancreaticoduodenectomy is associated with a mortality of about 2%, some patients who undergo surgery will have no benefit, only potential harms.”
    Screening for Pancreatic Cancer—Is There Hope?
    Anne Marie Lennon, Ralph H. Hruban, Alison P. Klein
    JAMA (in press) 2019
  • “Fourth, as we have discussed, it is important to distinguish precursor lesions with a reasonable chance of progressing to advanced cancer from those with little or no risk of progression. Recently, there has been progress. Many of the somatic mutations associated with high-grade dysplasia or early invasive cancer, such as TP53 and SMAD4, are known and can be detected in cyst fluid from IPMNs and MCNs. In a recent study of approximately 600 patients with pancreatic cysts, a tumor marker panel was used to distinguish between IPMNs with high-grade dysplasia or early invasive cancer and those with no or low malignant potential with 79% sensitivity and 96% specificity.”
    Screening for Pancreatic Cancer—Is There Hope?
    Anne Marie Lennon, Ralph H. Hruban, Alison P. Klein
    JAMA (in press) 2019
  • “Although pancreatic cancer is relatively rare, populations with a significantly increased risk can now be identified and their risk quantified.8 These higher-risk populations can be targeted for screening, greatly increasing their positive pretest probability. For example, a number of germline variants have been discovered that increase the risk of pancreatic cancer. These include variants in BRCA2, BRCA1, p16/CDKN2A, PALB2, STK11, ATM, PRSS1, and the DNA mismatch repair genes. Similarly, new-onset diabetes mellitus in an elderly person significantly increases the likelihood that the person will be diagnosed as having pancreatic cancer. An integra- tion of risk factors will likely identify subgroups with a high enough positive pretest probability that they will be the first to benefit from screening.”
    Screening for Pancreatic Cancer Gets a D, But the Student Is Improving
    Ralph H. Hruban, MD; Keith D. Lillemoe, MD
    JAMA Surgery (in press) 2019
  • “In addition, we should recognize that millions of people are already undergoing screening and they just do not know it. Millions of abdominal computed tomography and magnetic resonance imaging scans are performed every year. Many of these scans will include the pancreas, and these scans provide an opportunity for early detection of asymptomatic disease. In our experience, many of these early asymptomatic cancers are subtle, and it is easy for the average radiologist to miss these lesions if they are focused on identifying the pressing clinical need of their patient. These easily missed lesions will soon be detected automatically by novel deep learning and radiomics approaches running in the background as scans are generated.”
    Screening for Pancreatic Cancer Gets a D, But the Student Is Improving
    Ralph H. Hruban, MD; Keith D. Lillemoe, MD
    JAMA Surgery (in press) 2019
  • “Notwithstanding these grim statistics, there is hope. Several studies have shown that long-term survival can be achieved when patients are diagnosed with small early-stage cancers and treated. Extending this further, the detection and treatment of precancerous lesions that give rise to invasive pancreatic ductal adenocarcinoma can prevent some patients from ever developing invasive cancer. These precursor lesions, pancreatic intraepithelial neoplasia, intraductal papillary mucinous neoplasms, and mucinous cystic neoplasms, can now be detected and treated.”
    Screening for Pancreatic Cancer Gets a D, But the Student Is Improving
    Ralph H. Hruban, MD; Keith D. Lillemoe, MD
    JAMA Surgery (in press) 2019
  • “In addition, pancreatic cancer clinically often progresses rapidly, suggesting that the window of opportunity for early detection is very narrow. For example, Yu and colleagues estimated that the average patient with pancreatic ductal adenocarcinoma progresses from stage I to stage IV disease in less than a year and a half. Indeed, venous invasion is present in most pancreatic ductal adenocarcinomas and the veins of the pancreas drain directly into the liver. Pancreatic cancers that invade veins therefore have direct access to the liver.”
    Screening for Pancreatic Cancer Gets a D, But the Student Is Improving
    Ralph H. Hruban, MD; Keith D. Lillemoe, MD
    JAMA Surgery (in press) 2019
  • “One can easily imagine the day in which high-risk individuals will be screened using new molecular-based technologies. In parallel, all abdominal imaging will be scanned using deep learning and other approaches to identify subtle changes in the pancreas. Individuals found to have a precancer will not undergo invasive surgery but instead will receive a therapeutic vaccine that will selectively kill the precancerous lesion before it has the opportunity to progress to invasive carcinoma.”
    Screening for Pancreatic Cancer Gets a D, But the Student Is Improving
    Ralph H. Hruban, MD; Keith D. Lillemoe, MD
    JAMA Surgery (in press) 2019
  • “The USPSTF found no evidence that screening for pancreatic cancer or treatment of screen-detected pancreatic cancer improves disease-specific morbidity or mortality,or all-cause mortality. The USPSTF found adequate evidence that the magnitude of the benefits of screening for pancreatic cancer in asymptomatic adults can be bounded as no greater than small. The USPSTF found adequate evidence that the magnitude of the harms of screening for pancreatic cancer and treatment of screen-detected pancreatic cancer can be bounded as at least moderate. The USPSTF reaffirms its previous conclusion that the potential benefits of screening for pancreatic cancer in asymptomatic adults do not outweigh the potential harms.”
    US Preventive Services Task Force
    JAMA. 2019;322(5):438-444.
  • “This recommendation is a reaffirmation of the USPSTF 2004 recommendation statement against screening for pancreatic cancer in asymptomatic adults. In 2004, the USPSTF reviewed the evidence on screening for pancreatic cancer and concluded that the harms of screening for pancreatic cancer exceed any potential benefits. For the current recommendation, the USPSTF commissioned a systematic review to look for new evidence on the benefits and harms of screening. The USPSTF found no new substantial evidence that would change its recommendation and therefore reaffirms its recommendation against screening for pancreatic cancer in asymptomatic adults.”
    US Preventive Services Task Force
    JAMA. 2019;322(5):438-444.
  • “The USPSTF found no evidence that screening for pancreatic cancer or treatment of screen-detected pancreatic cancer improves disease-specific morbidity or mortality,or all-cause mortality. The USPSTF found adequate evidence that the magnitude of the benefits of screening for pancreatic cancer in asymptomatic adults can be bounded as no greater than small. The USPSTF found adequate evidence that the magnitude of the harms of screening for pancreatic cancer and treatment of screen-detected pancreatic cancer can be bounded as at least moderate. The USPSTF reaffirms its previous conclusion that the potential benefits of screening for pancreatic cancer in asymptomatic adults do not outweigh the potential harms.”
    US Preventive Services Task Force
    JAMA. 2019;322(5):438-444.
  • “In 2019, an estimated 56 770 persons will be diagnosed with pancreatic cancer and 45 750 persons will die of the disease. About 85% to 90% of persons diagnosed with pancreatic cancer do not have known familial risk or genetic syndromes, 5% to 10% of persons have familial risk, and 3% to 5% of cases are due to inherited genetic cancer syndromes (such as Peutz-Jeghers syndrome). Familial pancreatic cancer is defined as a kindred with at least 2 affected first-degree relatives; a person’s degree of familial risk depends on the number of affected relatives.”
    US Preventive Services Task Force
    JAMA. 2019;322(5):438-444.
  • “Although its incidence is low, pancreatic cancer is the third most common cause of cancer death in the United States. Based on data from the Surveillance, Epidemiology, and End Results Program from 2009 to 2015, the overall 5-year survival rate for pancreatic cancer is 9.3%, and survival rates vary depending on the stage at which it is diagnosed. The 5-year survival rate for localized pancreatic cancer is 37.4%; when regional disease is present, the 5-year survival rate is 12.4%, and when distant metastatic disease is present, the 5-year survival rate is 2.9%.Surgical intervention at an early stage is the treatment most likely to improve chances of survival; however, most cases of pancreatic cancer are detected at an advanced stage, when surgical resection is not likely to be beneficial.”
    US Preventive Services Task Force
    JAMA. 2019;322(5):438-444.
  • CONCLUSIONS AND RECOMMENDATION
    The USPSTF recommends against screening for pancreatic cancer in asymptomatic adults. (D recommendation)
    US Preventive Services Task Force JAMA. 2019;322(5):438-444.
  • USPSTF Grades and Level of Evidence
  • USPSTF Grades and Level of Evidence
  • Background: Accurate assessment of local resectability of pancreatic cancer at initial workup is critical to determine the most appropriate management strategy among up-front operation, neoadjuvant treatment, or palliative treatment.
    Purpose: To investigate the interobserver agreement of the preoperative CT classification of the local resectability of pancreatic cancer and to determine if radiologist experience level impacts evaluation, and to evaluate the reader performance in assessing resectability at CT in a subset of patients with a reference standard for local resectability.
    Conclusion: Considerable interobserver variability exists in the assignment at CT of the local resectability of pancreatic cancer, even among experienced radiologists.
    Preoperative CT Classification of the Resectability of Pancreatic Cancer: Interobserver Agreement
    Joo I et al.
    Radiology 2019; 00:1–7 • https://doi.org/10.1148/radiol.2019190422
  • Results: There were 110 patients (mean age, 61 years 6 +/-11; 60 men) who were evaluated. Overall interobserver agreements were moderate for resectability classification (k = 0.48; 95% confidence interval: 0.45, 0.50). Only 30.0% of patients (33 of 110) were given the same resectability classification from all reviewers. More experienced reviewers demonstrated higher agreement in category assignments than less experienced reviewers (k = 0.55 [95% confidence interval: 0.50, 0.60] vs 0.43 [95% confidence interval: 0.38, 0.49], respectively). For prediction at CT of margin-negative (ie, R0) resections (n = 82), areas under the receiver operating characteristic curve of all reviewers were greater than 0.80 (range, 0.83–0.96). However, borderline resectable cancers showed di- verse R0 rates ranging from 0% to 74% depending on the reviewers.
    Conclusion: Considerable interobserver variability exists in the assignment at CT of the local resectability of pancreatic cancer, even among experienced radiologists.
    Preoperative CT Classification of the Resectability of Pancreatic Cancer: Interobserver Agreement
    Joo I et al.
    Radiology 2019; 00:1–7 • https://doi.org/10.1148/radiol.2019190422
  • Key Results
    * Classification at CT of pancreatic cancer as resectable, borderline resectable, or unresectable shows substantial interob- server variability (k = 0.48). Only 30.0% (33 of 110) of patients received consistent resectability classification by all eight reviewers.
    * Prediction at CT of local resectability of pancreatic cancer shows area under the receiver operating characteristic curve values greater than 0.80 (range, 0.83–0.96 depending on reviewers) by using a three-category classification. More experienced reviewers have higher agreement for category assignments than less experienced reviewers (k = 0.55 vs 0.43, respectively; P = .001).
    Preoperative CT Classification of the Resectability of Pancreatic Cancer: Interobserver Agreement
    Joo I et al.
    Radiology 2019; 00:1–7 • https://doi.org/10.1148/radiol.2019190422
  • Summary: Considerable interobserver variability exists in the assignment at CT of local resectability of pancreatic cancer, even among experienced radiologists.
    Preoperative CT Classification of the Resectability of Pancreatic Cancer: Interobserver Agreement
    Joo I et al.
    Radiology 2019; 00:1–7 • https://doi.org/10.1148/radiol.2019190422 
  • “In the interpretation of tumor-vascular relationships, our study showed only fair agreements between reviewers. Difficulties in differentiating abutment from encasement or tumors from inflammatory changes, or in determining reconstructible invasion at CT may have caused this interobserver variability. To enhance the objectivity of tumor abutment or encasement of tortuous or fine peripancreatic vessels, three-dimensional images reconstructed from thin-section CT data, such as maximal intensity projection images and multiplanar reconstruction images perpendicular to the vessels, can be helpful.”
    Preoperative CT Classification of the Resectability of Pancreatic Cancer: Interobserver Agreement
    Joo I et al.
    Radiology 2019; 00:1–7 • https://doi.org/10.1148/radiol.2019190422
  • “Pancreatic cancer itself, by obstructing the pancreatic duct, or related invasive procedures such as endoscopic US-guided biopsy or endoscopic retrograde cholangiopancreatography can cause acute pancreatitis, usually manifesting as peripancreatic fat infiltration at imaging, thereby mimicking tumor infiltration. Because extra-pancreatic perineural tumor invasion is typically seen as soft tissue infiltration around peripancreatic vessels extending from intrapancreatic tumors, it can be difficult to differentiate it from pancreatitis-related changes.”
    Preoperative CT Classification of the Resectability of Pancreatic Cancer: Interobserver Agreement
    Joo I et al.
    Radiology 2019; 00:1–7 • https://doi.org/10.1148/radiol.2019190422 
  • “In conclusion, considerable interobserver variability exists in the CT assignment of the local resectability of pancreatic cancer, even among experienced radiologists, raising concerns of reliable patient classification necessary for the appropriate selection of candidates for up-front operation or neoadjuvant treatment. Our results thus support the need for a central imaging review system to ensure consistency in the treatment allocation of patients with pancreatic cancer, particularly during prospective enrollment and in multicenter trials. Moreover, further study is warranted to obtain more refined imaging criteria or CT protocols that may further help improve interobserver agreement.”
    Preoperative CT Classification of the Resectability of Pancreatic Cancer: Interobserver Agreement
    Joo I et al.
    Radiology 2019; 00:1–7 • https://doi.org/10.1148/radiol.2019190422 
  • “Pancreatic ductal adenocarcinoma can be a difficult imaging diagnosis early in its course given its subtle imaging findings such as focal pancreatic duct dilatation, abrupt duct cut-off, and encasement of vasculature. A variety of pancreatitidies have imaging findings that mimic pancreatic ductal adenocarcinoma and lead to mass formation making diagnosis even more difficult on imaging alone. These conditions include acute focal pancreatitis, chronic pancreatitis, autoimmune pancreatitis, and paraduodenal (“groove”) pancreatitis.”
    Inflammatory mimickers of pancreatic adenocarcinoma
    Kunal Kothar ET AL.
    Abdominal Radiology (In Press, 2019) https://doi.org/10.1007/s00261-019-02233-7
  • “However, PDA may be an underlying cause in a minority of patients, especially in older patients or patients who do not have risk fac- tors for pancreatitis. Signs of an underlying PDA include focal pancreatic ductal dilatation, abrupt main pancreatic duct cut-off, and encasement of vasculature. Associated findings of malignancy such as lymphadenopathy or metastases can be more definitive.”
    Inflammatory mimickers of pancreatic adenocarcinoma
    Kunal Kothar ET AL.
    Abdominal Radiology (In Press, 2019) https://doi.org/10.1007/s00261-019-02233-7
  • “Patients with chronic pancreatitis develop PDA at a greater rate than the general population and the underly- ing cancer can be difficult to diagnosis. PDA demonstrates similar imaging findings as focal or mass-forming chronic pancreatitis of CT hypodensity, T1-weighted MR hypointensity, and hypoenhancement on arterial phase contrast-enhanced imaging and progressive delayed enhancement.”
    Inflammatory mimickers of pancreatic adenocarcinoma
    Kunal Kothar ET AL.
    Abdominal Radiology (In Press, 2019) https://doi.org/10.1007/s00261-019-02233-7
  • “Findings that help favor focal or mass-like chronic pancreatitis over PDA include irregularity of the pancreatic duct, intraductal or parenchymal calcification, diffuse pancreatic involvement, and normal or smoothly stenotic pancreatic duct penetrating through the mass (“duct penetrating sign”). A duct-penetrating sign has been found in a significantly higher number of patients with inflammatory pancreatic masses versus pancreatic cancer.”
    Inflammatory mimickers of pancreatic adenocarcinoma
    Kunal Kothar ET AL.
    Abdominal Radiology (In Press, 2019) https://doi.org/10.1007/s00261-019-02233-7 
Practice Management

  • OBJECTIVE: To examine physician attitudes toward and perceptions of social media use for therapeutic trial recruitment of patients with cancer.
    DESIGN, SETTING,AND PARTICIPANTS: This qualitative study engaged 44 physicians(24 academic based and 20 community based) at the main academic and 6 affiliated community sites of City of Hope in Duarte, California. Semistructured interviews were conducted in person or by telephone from March to June 2018. An interview guide was developed to explore perceptions of social media use for accrual of cancer therapeutic trials. Responses were recorded digitally and transcribed. Data were analyzed using qualitative content analysis.
    MAIN OUTCOMES AND MEASURES: Physicians’ perceptions of the advantages and disadvantages of using social media for clinical trial recruitment, strategies to improve uptake of social media in clinical trials, and the barriers and facilitators to social media use for professional purposes in general.
    Physician Perceptions of the Use of Social Media for Recruitment of Patients in Cancer Clinical Trials
    Mina S. Sedrak et al.
    JAMA Network Open. 2019;2(9):e1911528. doi:10.1001/jamanetworkopen.2019.11528
  • RESULTS Of the 44 participants,16(36%)were women,30(68%) had more than10 years of practice experience, 24 (55%) practiced in academia, and 20 (45%) practiced in the community. Physicians most commonly cited increased trial awareness and visibility as an advantage of using social media for trial recruitment. Cited disadvantages were increased administrative burden and risk of misinformation. Physicians also reported a need for institutional-level interventions (eg, restructuring of clinical trial offices to include personnel with social media expertise), increased evidence-based approaches to social media use, and more physician training on the use of social media. Perceived facilitators to professional social media use were networking and education; barriers included lack of time and lack of evidence of benefit.
    CONCLUSIONS AND RELEVANCE In this qualitative study,physicians recognized the benefits of using social media for clinical trial recruitment but noted that barriers, including increased administrative burden, increased time, and the risk of misinformation, remain. Future interventions to address these concerns are a required first step in increasing digital engagement for clinical trial accrual purposes.
    Physician Perceptions of the Use of Social Media for Recruitment of Patients in Cancer Clinical Trials
    Mina S. Sedrak et al.
    JAMA Network Open. 2019;2(9):e1911528. doi:10.1001/jamanetworkopen.2019.11528
  • CONCLUSIONS AND RELEVANCE In this qualitative study,physicians recognized the benefits of using social media for clinical trial recruitment but noted that barriers, including increased administrative burden, increased time, and the risk of misinformation, remain. Future interventions to address these concerns are a required first step in increasing digital engagement for clinical trial accrual purposes.
    Physician Perceptions of the Use of Social Media for Recruitment of Patients in Cancer Clinical Trials
    Mina S. Sedrak et al.
    JAMA Network Open. 2019;2(9):e1911528. doi:10.1001/jamanetworkopen.2019.11528
  • Key Points
    Question What are physicians’ attitudes toward and perceptions of using social media to recruit participants for cancer clinical trials?
    Findings In this qualitative study of 44 physicians from academic and community practices, physicians cited increased trial awareness and visibility as advantages of social media. Commonly cited disadvantages were increased administrative burden and risk of misinformation.
    Meaning This study’s findings suggest that physicians are aware of the benefits of social media as it relates to clinical trial recruitment, but key barriers remain; tailored interventions to address these concerns would be a required first step in increasing digital engagement among physicians.
    Physician Perceptions of the Use of Social Media for Recruitment of Patients in Cancer Clinical Trials
    Mina S. Sedrak et al.
    JAMA Network Open. 2019;2(9):e1911528. doi:10.1001/jamanetworkopen.2019.11528
  • OBJECTIVE: To examine physician attitudes toward and perceptions of social media use for therapeutic trial recruitment of patients with cancer.
    DESIGN, SETTING,AND PARTICIPANTS: This qualitative study engaged 44 physicians(24 academic based and 20 community based) at the main academic and 6 affiliated community sites of City of Hope in Duarte, California. Semistructured interviews were conducted in person or by telephone from March to June 2018. An interview guide was developed to explore perceptions of social media use for accrual of cancer therapeutic trials. Responses were recorded digitally and transcribed. Data were analyzed using qualitative content analysis.
    MAIN OUTCOMES AND MEASURES: Physicians’ perceptions of the advantages and disadvantages of using social media for clinical trial recruitment, strategies to improve uptake of social media in clinical trials, and the barriers and facilitators to social media use for professional purposes in general.
    Physician Perceptions of the Use of Social Media for Recruitment of Patients in Cancer Clinical Trials
    Mina S. Sedrak et al.
    JAMA Network Open. 2019;2(9):e1911528. doi:10.1001/jamanetworkopen.2019.11528
  • RESULTS Of the 44 participants,16(36%)were women,30(68%) had more than10 years of practice experience, 24 (55%) practiced in academia, and 20 (45%) practiced in the community. Physicians most commonly cited increased trial awareness and visibility as an advantage of using social media for trial recruitment. Cited disadvantages were increased administrative burden and risk of misinformation. Physicians also reported a need for institutional-level interventions (eg, restructuring of clinical trial offices to include personnel with social media expertise), increased evidence-based approaches to social media use, and more physician training on the use of social media. Perceived facilitators to professional social media use were networking and education; barriers included lack of time and lack of evidence of benefit.
    CONCLUSIONS AND RELEVANCE In this qualitative study,physicians recognized the benefits of using social media for clinical trial recruitment but noted that barriers, including increased administrative burden, increased time, and the risk of misinformation, remain. Future interventions to address these concerns are a required first step in increasing digital engagement for clinical trial accrual purposes.
    Physician Perceptions of the Use of Social Media for Recruitment of Patients in Cancer Clinical Trials
    Mina S. Sedrak et al.
    JAMA Network Open. 2019;2(9):e1911528. doi:10.1001/jamanetworkopen.2019.11528
  • CONCLUSIONS AND RELEVANCE In this qualitative study,physicians recognized the benefits of using social media for clinical trial recruitment but noted that barriers, including increased administrative burden, increased time, and the risk of misinformation, remain. Future interventions to address these concerns are a required first step in increasing digital engagement for clinical trial accrual purposes.
    Physician Perceptions of the Use of Social Media for Recruitment of Patients in Cancer Clinical Trials
    Mina S. Sedrak et al.
    JAMA Network Open. 2019;2(9):e1911528. doi:10.1001/jamanetworkopen.2019.11528
  • Key Points
    Question What are physicians’ attitudes toward and perceptions of using social media to recruit participants for cancer clinical trials?
    Findings In this qualitative study of 44 physicians from academic and community practices, physicians cited increased trial awareness and visibility as advantages of social media. Commonly cited disadvantages were increased administrative burden and risk of misinformation.
    Meaning This study’s findings suggest that physicians are aware of the benefits of social media as it relates to clinical trial recruitment, but key barriers remain; tailored interventions to address these concerns would be a required first step in increasing digital engagement among physicians.
    Physician Perceptions of the Use of Social Media for Recruitment of Patients in Cancer Clinical Trials
    Mina S. Sedrak et al.
    JAMA Network Open. 2019;2(9):e1911528. doi:10.1001/jamanetworkopen.2019.11528
Small Bowel

  • Location of Small Bowel Tumors
  • Purpose The purpose of this study was to identify the CT characteristics of metastatic disease of the small bowel and define the clinical time course between primary tumor diagnosis and small bowel metastasis detection.
    Results Melanoma was the most common malignancy to metastasize to small bowel (7 of 16 patients). Only one of the 16 cases was detected at the time of initial diagnosis of their primary malignancy. The average time from diagnosis of the primary malignancy or remission to the time of detection of the small bowel metastasis was 7.2 and 8.3 years, respectively. The most common symptoms were gastrointestinal bleeding (N = 5) and small bowel obstruction (N = 5). In 3 cases, the masses were not identified on pre-operative CT.
    Clinical time course and CT detection of metastatic disease to the small bowel
    Megan H. Lee, Atif Zaheer, Lysandra Voltaggio, Pamela T. Johnson, Elliot K. Fishman
    Abdominal Radiology (2019) 44:2104–2110
  • “Metastases to the small bowel often occur many years after the initial diagnosis of the primary malignancy or entering remission and may be symptomatic. Attention to the small bowel is particularly important in melanoma patients, who may have multiple small bowel metastases, even after many years of being disease free. As oncology patients undergo numerous surveillance scans and improved therapies allow for longer survival, detection of these masses at a small size can facilitate elective resection to avert urgent surgical intervention.”
    Clinical time course and CT detection of metastatic disease to the small bowel
    Megan H. Lee, Atif Zaheer, Lysandra Voltaggio, Pamela T. Johnson, Elliot K. Fishman
    Abdominal Radiology (2019) 44:2104–2110
  • ”In this retrospective case series of pathologically proven metastases to the small bowel, there was a relatively long latent period from the time of initial diagnosis of primary malignancy or remission to the time of detection of the small bowel metastasis. The most common primary malignancy was melanoma, which is especially known to recur at any time, even after a long remission. In only one patient was the metastasis detected at the time of initial diagnosis. In the majority of the cases, there was already distant metastatic disease at the time of diagnosis of the small bowel metastasis. The most common symptoms were gastrointestinal bleeding and small bowel obstruction.”
    Clinical time course and CT detection of metastatic disease to the small bowel
    Megan H. Lee, Atif Zaheer, Lysandra Voltaggio, Pamela T. Johnson, Elliot K. Fishman
    Abdominal Radiology (2019) 44:2104–2110
  • These findings suggest that attention should be paid to the small bowel on routine surveillance scans, as metastases to the small bowel tend to occur years after the initial diagnosis of malignancy or even after years of remission. Furthermore, gastrointestinal symptoms in oncology patients should also be cause for careful attention to the small bowel. Although the total number of cases in this series was small, a significant number of lesions were not seen on CT. Further work is needed to optimize the detection of metastases to the small bowel mucosa on CT, as these can have a significant clinical impact.”
    Clinical time course and CT detection of metastatic disease to the small bowel
    Megan H. Lee, Atif Zaheer, Lysandra Voltaggio, Pamela T. Johnson, Elliot K. Fishman
    Abdominal Radiology (2019) 44:2104–2110
Stomach

  • “Evaluation of stomach neoplasms by traditional 3-dimensional (3D) computed tomography methods such as volume rendering and maxi- mum-intensity projection plays an important role in lesion detection and characterization, preoperative planning, staging, and follow-up. Recently, a new 3D visualization method has become available known as cinematic rendering (CR). This novel technique makes use of a complex global lighting model to impart photorealistic levels of detail to 3D images. Although this new technique has yet to be systematically studied for the evaluation of stomach neoplasms, its intrinsic ability to create realistic shadowing effects to enhance understanding of the 3D relative locations of anatomic structures and to enhance detail and texture may prove valuable for a variety of applications. In this article, we demonstrate the CR appearance of multiple different gastric neoplasms, describe potential advantages of CR, and suggest future research directions.”
    Evaluation of Stomach Neoplasms With 3-Dimensional Computed Tomography: Focus on the Potential Role of Cinematic Rendering
    Steven P. Rowe, Linda C. Chu, Elliot K. Fishman
    J Comput Assist Tomogr 2018;42: 661–666
  • “Evaluation of stomach neoplasms by traditional 3-dimensional (3D) computed tomography methods such as volume rendering and maxi- mum-intensity projection plays an important role in lesion detection and characterization, preoperative planning, staging, and follow-up. Recently, a new 3D visualization method has become available known as cinematic rendering (CR). This novel technique makes use of a complex global lighting model to impart photorealistic levels of detail to 3D images. Although this new technique has yet to be systematically studied for the evaluation of stomach neoplasms, its intrinsic ability to create realistic shadowing effects to enhance understanding of the 3D relative locations of anatomic structures and to enhance detail and texture may prove valuable for a variety of applications.”
    Evaluation of Stomach Neoplasms With 3-Dimensional Computed Tomography: Focus on the Potential Role of Cinematic Rendering
    Steven P. Rowe, Linda C. Chu, Elliot K. Fishman
    J Comput Assist Tomogr 2018;42: 661–666
  • “Recently, a novel method of 3D CT volumetric data visualization became available. This method, known as cinematic rendering (CR), makes use of standard acquisition CT volumetric data composed of isotropic voxels and is fundamentally similar to VR. However, whereas VR uses a ray casting lighting model to create 3D images from acquired volumes, CR instead makes use of a complex global lighting model that takes into account a number of potential interactions of photons with the material in the imaged volume; this leads to enhanced surface detail and a photorealistic quality to the images.”
    Evaluation of Stomach Neoplasms With 3-Dimensional Computed Tomography: Focus on the Potential Role of Cinematic Rendering
    Steven P. Rowe, Linda C. Chu, Elliot K. Fishman
    J Comput Assist Tomogr 2018;42: 661–666
  • ”Computed tomography is the imaging method of choice for evaluating stomach neoplasms, and traditional 3D methodologies have previously been shown to have value in lesion detection, staging, and follow-up for treatment response. With the addition of enhanced surface detail intrinsic to CR, the role of 3D CT visualizations in stomach neoplasm imaging may be expanded. Prospective trials with pathologic correlation that evaluate the ability of CR to enhance detection of subtle mucosal irregularities, study whether CR provides better lesion characterization through highlighting intratumoral texture, and lead to improved preoperative planning would be of value.”
    Evaluation of Stomach Neoplasms With 3-Dimensional Computed Tomography: Focus on the Potential Role of Cinematic Rendering
    Steven P. Rowe, Linda C. Chu, Elliot K. Fishman
    J Comput Assist Tomogr 2018;42: 661–666
© 1999-2019 Elliot K. Fishman, MD, FACR. All rights reserved.