google ads
Search

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

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

-- OR --

3D and Workflow

  • OBJECTIVE. Three-dimensional printing is being used for surgical assistance, particularly for robot-assisted partial nephrectomy (RAPN). The objective of this study was to assess the anatomic accuracy of the 3D model used for 3D model–guided RAPN.
    CONCLUSION. Three-dimensional printed models are accurate with respect to anatomic reality. The reliability of surgical assistance with 3D printed models must be evaluated.
    Measurement of the Accuracy of 3D-Printed Medical Models to Be Used for Robot-Assisted Partial Nephrectomy
    Michiels C et al.
    AJR 2019; 213:1–6
  • “Our 3D models are accurate with respect to the anatomic reality of the different measurements and arterial distribution. These 3D models allow use of a clampless technique or segmental renal artery clamping to minimize renal ischemia and to preserve postoperative renal function. The reliability of surgical assistance with 3D printing must be prospectively evaluated.”
    Measurement of the Accuracy of 3D-Printed Medical Models to Be Used for Robot-Assisted Partial Nephrectomy
    Michiels C et al.
    AJR 2019; 213:1–6
  • Three-dimensional printing is appreciated because surgeons can have a tactile experience with the renal tumor and re- nal system and thus determine better surgical plans and treatment strategies. Marconi et al. found that 3D printed models assisted medical students, surgeons, and radiologists in identifying anatomic structures.”
    Measurement of the Accuracy of 3D-Printed Medical Models to Be Used for Robot-Assisted Partial Nephrectomy
    Michiels C et al.
    AJR 2019; 213:1–6
  • “Cinematic rendering images contain high levels of detail with shadowing and depth that are not available from traditional 3D CT techniques. As yet, the role of CR in evaluating colonic pathology has not been investigated. However, given the breadth of pathologic processes that affect the colon, including inflammatory bowel disease, diverticulitis, neoplastic conditions, herniation, and gastrointestinal bleeding, we undertook a survey of recent cases at our institution to demonstrate colon pathology as visualized with CR. The following review discusses the role of 3D CT visualizations for colonic pathology with an emphasis on CR example images.”
    Computed Tomography Cinematic Rendering in the Evaluation of Colonic Pathology: Technique and Clinical Applications
    Steven P. Rowe, Linda C. Chu, Elliot K. Fishman,
    J Comput Assist Tomogr 2019 May/Jun;43(3):475-484
  • “Cinematic rendering makes use of volumetric data; however, it differs from the MIP and VR methods in using a global lighting model that incorporates complex path tracing and models how photons interact with the materials within the volume. This creates photorealistic images with improved surface detail relative to other 3D visualization methods. Cinematic rendering im- ages are derived from standard clinical CT protocols and can be created from any volumetric CT data composed of isotropic voxels."
    Computed Tomography Cinematic Rendering in the Evaluation of Colonic Pathology: Technique and Clinical Applications
    Steven P. Rowe, Linda C. Chu, Elliot K. Fishman,
    J Comput Assist Tomogr 2019 May/Jun;43(3):475-484
  • For those patients with complications or who require surgical interventions, the overall photorealistic 3D representations provided by CR may be of value in preoperative planning. Figure 4 shows such a complication in which a fistulous tract between the sigmoid colon and the urinary bladder has occurred after an episode of acute diverticulitis.
    Computed Tomography Cinematic Rendering in the Evaluation of Colonic Pathology: Technique and Clinical Applications
    Steven P. Rowe, Linda C. Chu, Elliot K. Fishman,
    J Comput Assist Tomogr 2019 May/Jun;43(3):475-484
  • “As with many arterially hyperenhancing tumors, 3D visualization methods offer high-contrast resolution that allows for clear delineation of the tumor from the bowel wall and bowel contents. Relative to MIP and VR images, CR can provide greater soft tissue detail, and varying the window width and level settings can modulate the amount of other soft tissue seen relative to the hypervascular tumor, aiding in tumor visualization and potentially in surgical planning.”
    Computed Tomography Cinematic Rendering in the Evaluation of Colonic Pathology: Technique and Clinical Applications
    Steven P. Rowe, Linda C. Chu, Elliot K. Fishman,
    J Comput Assist Tomogr 2019 May/Jun;43(3):475-484
  • Overall, the detection and characterization of tumors may be a particularly important application of CR in the colon. The potentially high contrast combined with the surface detail and shadowing from the global lighting model can make tumors stand out from background bowel wall and luminal contents. Furthermore, as we come to understand more about the implications of textural features in tumors, the visual appearance of lesions on CR may allow for prognostication regarding tumor aggressiveness.
    Computed Tomography Cinematic Rendering in the Evaluation of Colonic Pathology: Technique and Clinical Applications
    Steven P. Rowe, Linda C. Chu, Elliot K. Fishman,
    J Comput Assist Tomogr 2019 May/Jun;43(3):475-484
  • Although colonoscopy, invasive angiography, and tagged red blood cell scintigraphy remain important modalities in the workup of lower gastrointestinal bleed- ing, CT angiography has recently taken on a more prominent role given that it can be performed rapidly and can often suggest underlying cause of bleeding even in patients who are not actively bleeding. For example, Figure 11 demonstrates the appearance of angiodysplasia of the cecum and proximal ascending colon discovered incidentally in a 65-year-old man being imaged for chronic abdominal pain. Note that the spatial arrangement of numerous prominent vessels within the wall of the colon and the large, early draining veins are well displayed by the CR technique.
    Computed Tomography Cinematic Rendering in the Evaluation of Colonic Pathology: Technique and Clinical Applications
    Steven P. Rowe, Linda C. Chu, Elliot K. Fishman,
    J Comput Assist Tomogr 2019 May/Jun;43(3):475-484
  • “For those patients who are actively bleeding, 3D methodologies including CR provide high-contrast images on which active contrast extravasation into the bowel lumen can often be easily appreciated. Maximum intensity projection images have utility in identifying very subtle sites of active extravasation given that even a single high-density voxel may stand out, whereas VR images can be useful for evaluating the bowel wall for pathology that can lead to lower gastrointestinal bleeding. The exact role of CR in evaluating the colon for a site of bleeding has yet to be specifically identified, yet the images would seem to maintain much of the high contrast of MIPs and at the same time allow for iden- tification of soft tissue abnormalities.”
    Computed Tomography Cinematic Rendering in the Evaluation of Colonic Pathology: Technique and Clinical Applications
    Steven P. Rowe, Linda C. Chu, Elliot K. Fishman,
    J Comput Assist Tomogr 2019 May/Jun;43(3):475-484
  • ”Indeed, a great deal of work remains to be done to validate the utility of CR images in clinical practice, although the technique is promising given the enhanced detail and realistic shadowing intrinsic to the CR methodology. In particular, the use- fulness of CR images for preoperative assessment and correlation of findings as displayed by CR relative to intraoperative experi- ence will be of interest, and these aspects of this new visualization technique should be explored in the context of pathologies of the colon.”
    Computed Tomography Cinematic Rendering in the Evaluation of Colonic Pathology: Technique and Clinical Applications
    Steven P. Rowe, Linda C. Chu, Elliot K. Fishman,
    J Comput Assist Tomogr 2019 May/Jun;43(3):475-484
  • ”CR will almost certainly be used for patient en- gagement and trainee education purposes, where the intuitive display of the relative relationships of objects within the imaged volume can aid in understanding complex anatomy and pathology. Furthermore, the global lighting model that underlies CR may prove useful for the detection and characterization of pathology, as well as after response to therapy, given its ability to accentuate different tissue types.”
    Computed Tomography Cinematic Rendering in the Evaluation of Colonic Pathology: Technique and Clinical Applications
    Steven P. Rowe, Linda C. Chu, Elliot K. Fishman,
    J Comput Assist Tomogr 2019 May/Jun;43(3):475-484
  • OBJECTIVE. The objective of our study was to determine the utility of radiomics features in differentiating CT cases of pancreatic ductal adenocarcinoma (PDAC) from normal pancreas.
    RESULTS. Mean tumor size was 4.1 ± 1.7 (SD) cm. The overall accuracy of the random forest binary classification was 99.2% (124/125), and AUC was 99.9%. All PDAC cases (60/60) were correctly classified. One case from a renal donor was misclassified as PDAC (1/65). The sensitivity was 100%, and specificity was 98.5%.
    CONCLUSION. Radiomics features extracted from whole pancreas can be used to differentiate between CT cases from patients with PDAC and healthy control subjects with normal pancreas.
    Utility of CT Radiomics Features in Differentiation of Pancreatic Ductal Adenocarcinoma From Normal Pancreatic Tissue
    Chu LC, Park S, Fishman EK et al
    AJR 2019; 213:1–9
  • RESULTS. Mean tumor size was 4.1 ± 1.7 (SD) cm. The overall accuracy of the random forest binary classification was 99.2% (124/125), and AUC was 99.9%. All PDAC cases (60/60) were correctly classified. One case from a renal donor was misclassified as PDAC (1/65). The sensitivity was 100%, and specificity was 98.5%.
    CONCLUSION. Radiomics features extracted from whole pancreas can be used to differentiate between CT cases from patients with PDAC and healthy control subjects with normal pancreas.
    Utility of CT Radiomics Features in Differentiation of Pancreatic Ductal Adenocarcinoma From Normal Pancreatic Tissue
    Chu LC, Park S et al.
    AJR 2019; 213:1–9
  • “CT features of early PDAC can be subtle and missed by even experienced radiologists. Early signs of PDAC such as pancreatic parenchyma inhomogeneity and loss of normal fatty marbling of the pancreas have been described on retrospective CT review up to 34 months before the diagnosis of PDAC. Quantitative analysis of these imaging features offers the potential for computer-aided diagnosis of PDAC.”
    Utility of CT Radiomics Features in Differentiation of Pancreatic Ductal Adenocarcinoma From Normal Pancreatic Tissue
    Chu LC, Park S, Kawamoto S, Fouladi DF, Shayesteh S, Zinreich ES, Graves JS, Horton KM, Hruban RH, Yuille AL, Kinzler KW, Vogelstein B, Fishman EK.
    AJR Am J Roentgenol. 2019 Apr 23:1-9.
  • “This study aimed to tackle the second goal—differentiation of abnormal from normal pancreatic tissue using segmentation of the entire pancreas (i.e., without relying on separate segmentation of the tumor region). Our results showed that, after manual segmentation of pancreas boundaries, radiomics features and the random forest classifier were highly accurate in differentiating PDAC cases from normal control cases (sensitivity, 100%; specificity, 98.5%; accuracy, 99.2%). The radiomics features most relevant to differentiate PDAC from normal pancreas were based on shape and textural heterogeneity.”
    Utility of CT Radiomics Features in Differentiation of Pancreatic Ductal Adenocarcinoma From Normal Pancreatic Tissue
    Chu LC, Park S, Kawamoto S, Fouladi DF, Shayesteh S, Zinreich ES, Graves JS, Horton KM, Hruban RH, Yuille AL, Kinzler KW, Vogelstein B, Fishman EK.
    AJR Am J Roentgenol. 2019 Apr 23:1-9.
  • “Given the high accuracy of automatic pan- creas segmentation by existing algorithms, these algorithms could be used to generate the boundaries for pancreas segmentation, and then the radiomics feature analysis algorithm could be performed to differentiate PDAC from normal pancreas. Some technical hurdles need to be overcome before these complex algorithms can be combined, but we anticipate that will be possible in the near future.”
    Utility of CT Radiomics Features in Differentiation of Pancreatic Ductal Adenocarcinoma From Normal Pancreatic Tissue
    Chu LC, Park S, Kawamoto S, Fouladi DF, Shayesteh S, Zinreich ES, Graves JS, Horton KM, Hruban RH, Yuille AL, Kinzler KW, Vogelstein B, Fishman EK.
    AJR Am J Roentgenol. 2019 Apr 23:1-9.
  • ”All of the scans in the current study were obtained at a single institution on units manufactured by a single vendor using matched protocols and the same reconstruction algorithm. Differences in image acquisition, reconstruction, segmentation, and feature extraction can affect radiomics features and results. There is currently no standardization in the optimal protocol for imaging acquisition and postprocessing for radiomics analysis.”
    Utility of CT Radiomics Features in Differentiation of Pancreatic Ductal Adenocarcinoma From Normal Pancreatic Tissue
    Chu LC, Park S, Kawamoto S, Fouladi DF, Shayesteh S, Zinreich ES, Graves JS, Horton KM, Hruban RH, Yuille AL, Kinzler KW, Vogelstein B, Fishman EK.
    AJR Am J Roentgenol. 2019 Apr 23:1-9.
  • “This preliminary study showed that the radiomics features extracted from the whole pancreas can be used to differentiate between CT images of patients with PDAC and CT images of healthy control subjects. There is the potential to combine this algorithm with automatic organ segmentation algorithms for automatic detection of PDAC.”
    Utility of CT Radiomics Features in Differentiation of Pancreatic Ductal Adenocarcinoma From Normal Pancreatic Tissue
    Chu LC, Park S, Kawamoto S, Fouladi DF, Shayesteh S, Zinreich ES, Graves JS, Horton KM, Hruban RH, Yuille AL, Kinzler KW, Vogelstein B, Fishman EK.
    AJR Am J Roentgenol. 2019 Apr 23:1-9.
  • OBJECTIVE. The objective of our study was to investigate the potential influence of intra- and interobserver manual segmentation variability on the reliability of single-slice–based 2D CT texture analysis of renal masses.
    CONCLUSION. Single-slice–based 2D CT texture analysis of renal masses is sensitive to intra- and interobserver manual segmentation variability. Therefore, it may lead to nonreproducible results in radiomic analysis unless a reliability analysis is considered in the workflow.
    Reliability of Single-Slice–Based 2D CT Texture Analysis of Renal Masses: Influence of Intra- and Interobserver Manual Segmentation Variability on Radiomic Feature Reproducibility
    Kocak B et al.
    AJR 2019; 213:1–7
  • “Lately, texture analysis has been an active area of research in the field of radiomics, suggesting that it can be used in predicting tumor subtypes, tumor stage, tumor grade, response to treatment, genomic profile, and overall survival . Nonetheless, recent evi- dence also suggests that conclusions must be treated with caution because several texture parameters may have reproducibility problems, which is an important challenge for building reliable predictive models to be used in clinical practice.”
    Reliability of Single-Slice–Based 2D CT Texture Analysis of Renal Masses: Influence of Intra- and Interobserver Manual Segmentation Variability on Radiomic Feature Reproducibility
    Kocak B et al.
    AJR 2019; 213:1–7
  • Although whole-tumor segmentation is known to be the most representative for tumor texture , it is considered an impractical and time-consuming process to be used in clinical routine, particularly in large tumors such as renal masses. For renal tumors, there has been a trend toward using a single image slice along with manual segmentation in an attempt to bring texture analysis into a daily routine.
    Reliability of Single-Slice–Based 2D CT Texture Analysis of Renal Masses: Influence of Intra- and Interobserver Manual Segmentation Variability on Radiomic Feature Reproducibility
    Kocak B et al.
    AJR 2019; 213:1–7
  • “In conclusion, single-slice–based 2D CT texture analysis of RCCs is sensitive to intra-and interobserver manual segmentation variability, which may lead to nonreproducible results in radiomic analysis. Therefore, a reliability analysis with as much and heterogeneous data as possible must be incorporated into every scientific research study using this technique. Otherwise, the radiomic studies of renal masses without a reliability analysis might lead to a chain of nonreproducible outcomes in terms of selected texture features and statistical models created, which might further influence the generalizability and replicability of the findings of the radiomic studies. bring texture analysis into a daily routine.”
    Reliability of Single-Slice–Based 2D CT Texture Analysis of Renal Masses: Influence of Intra- and Interobserver Manual Segmentation Variability on Radiomic Feature Reproducibility
    Kocak B et al.
    AJR 2019; 213:1–7
  • In addition, CECT provides more texture features with good to excellent interobserver reliability than unenhanced CT does. Filtered and transformed images might be useful for reducing the influence of manual segmentation variations on single-slice–based 2D CT texture analysis, yielding more features with good to excellent reliability than original images do.
    Reliability of Single-Slice–Based 2D CT Texture Analysis of Renal Masses: Influence of Intra- and Interobserver Manual Segmentation Variability on Radiomic Feature Reproducibility
    Kocak B et al.
    AJR 2019; 213:1–7
Adrenal

  • OBJECTIVE. The purpose of this study is to determine the differences in growth rate of adrenal adenomas and malignant adrenal nodules. RESULTS. A growth rate of 3 mm/year distinguished adenomas from malignant nodules with a sensitivity of 100% (95% CI, 86.8–100%) and a specificity of 100% (95% CI, 96.6–100%).
    CONCLUSION. Approximately one-third of radiologically proven adrenal adenomas grew, all of which grew at a rate less than 3 mm/year. All malignant adrenal nodules grew, and all at a rate greater than 5 mm/year.
    Differences in Growth Rate on CT of Adrenal Adenomas and Malignant Adrenal Nodules
    Corwin MT et al.
    AJR 2019; 213:1–5
  • “Growth or stability over time are important factors in distinguishing benign from malignant adrenal nodules with indeterminate imaging features. However, a spe- cific growth rate to reliably distinguish between the two has not been established, to our knowledge. This is because benign nodules can grow. The results of our study show that approximately one-third of radiologically proven adrenal adenomas grow over time, and all adenomas that grew did so at a rate less than 3 mm/year, whereas all malignant adrenal nodules grew faster than 5 mm/year.”
    Differences in Growth Rate on CT of Adrenal Adenomas and Malignant Adrenal Nodules
    Corwin MT et al.
    AJR 2019; 213:1–5
  • ”In conclusion, approximately one-third of radiologically proven adrenal adenomas in our retrospective single-institution study grew over time, at a rate of 3 mm/year or less, whereas malignant adrenal nodules grew at a faster rate, greater than 5 mm/year. If confirmed by larger multiinstitutional studies, a growth rate of 3 mm/year may be a useful threshold to distinguish benign from malignant adrenal nodules.”
    Differences in Growth Rate on CT of Adrenal Adenomas and Malignant Adrenal Nodules
    Corwin MT et al.
    AJR 2019; 213:1–5
Colon

  • “Cinematic rendering images contain high levels of detail with shadowing and depth that are not available from traditional 3D CT techniques. As yet, the role of CR in evaluating colonic pathology has not been investigated. However, given the breadth of pathologic processes that affect the colon, including inflammatory bowel disease, diverticulitis, neoplastic conditions, herniation, and gastrointestinal bleeding, we undertook a survey of recent cases at our institution to demonstrate colon pathology as visualized with CR. The following review discusses the role of 3D CT visualizations for colonic pathology with an emphasis on CR example images.”
    Computed Tomography Cinematic Rendering in the Evaluation of Colonic Pathology: Technique and Clinical Applications
    Steven P. Rowe, Linda C. Chu, Elliot K. Fishman,
    J Comput Assist Tomogr 2019 May/Jun;43(3):475-484
  • “Cinematic rendering makes use of volumetric data; however, it differs from the MIP and VR methods in using a global lighting model that incorporates complex path tracing and models how photons interact with the materials within the volume. This creates photorealistic images with improved surface detail relative to other 3D visualization methods. Cinematic rendering - ages are derived from standard clinical CT protocols and can be created from any volumetric CT data composed of isotropic voxels."
    Computed Tomography Cinematic Rendering in the Evaluation of Colonic Pathology: Technique and Clinical Applications
    Steven P. Rowe, Linda C. Chu, Elliot K. Fishman,
    J Comput Assist Tomogr 2019 May/Jun;43(3):475-484
  • For those patients with complications or who require surgical interventions, the overall photorealistic 3D representations provided by CR may be of value in preoperative planning. Figure 4 shows such a complication in which a fistulous tract between the sigmoid colon and the urinary bladder has occurred after an episode of acute diverticulitis.
    Computed Tomography Cinematic Rendering in the Evaluation of Colonic Pathology: Technique and Clinical Applications
    Steven P. Rowe, Linda C. Chu, Elliot K. Fishman,
    J Comput Assist Tomogr 2019 May/Jun;43(3):475-484
  • “As with many arterially hyperenhancing tumors, 3D visualization methods offer high-contrast resolution that allows for clear delineation of the tumor from the bowel wall and bowel contents. Relative to MIP and VR images, CR can provide greater soft tissue detail, and varying the window width and level settings can modulate the amount of other soft tissue seen relative to the hypervascular tumor, aiding in tumor visualization and potentially in surgical planning.”
    Computed Tomography Cinematic Rendering in the Evaluation of Colonic Pathology: Technique and Clinical Applications
    Steven P. Rowe, Linda C. Chu, Elliot K. Fishman,
    J Comput Assist Tomogr 2019 May/Jun;43(3):475-484
  • Overall, the detection and characterization of tumors may be a particularly important application of CR in the colon. The potentially high contrast combined with the surface detail and shadowing from the global lighting model can make tumors stand out from background bowel wall and luminal contents. Furthermore, as we come to understand more about the implications of textural features in tumors, the visual appearance of lesions on CR may allow for prognostication regarding tumor aggressiveness.
    Computed Tomography Cinematic Rendering in the Evaluation of Colonic Pathology: Technique and Clinical Applications
    Steven P. Rowe, Linda C. Chu, Elliot K. Fishman,
    J Comput Assist Tomogr 2019 May/Jun;43(3):475-484
  • Although colonoscopy, invasive angiography, and tagged red blood cell scintigraphy remain important modalities in the workup of lower gastrointestinal bleed- ing, CT angiography has recently taken on a more prominent role given that it can be performed rapidly and can often suggest underlying cause of bleeding even in patients who are not actively bleeding. For example, Figure 11 demonstrates the appearance of angiodysplasia of the cecum and proximal ascending colon discovered incidentally in a 65-year-old man being imaged for chronic abdominal pain. Note that the spatial arrangement of numerous prominent vessels within the wall of the colon and the large, early draining veins are well displayed by the CR technique.
    Computed Tomography Cinematic Rendering in the Evaluation of Colonic Pathology: Technique and Clinical Applications
    Steven P. Rowe, Linda C. Chu, Elliot K. Fishman,
    J Comput Assist Tomogr 2019 May/Jun;43(3):475-484
  • “For those patients who are actively bleeding, 3D methodologies including CR provide high-contrast images on which active contrast extravasation into the bowel lumen can often be easily appreciated. Maximum intensity projection images have utility in identifying very subtle sites of active extravasation given that even a single high-density voxel may stand out, whereas VR images can be useful for evaluating the bowel wall for pathology that can lead to lower gastrointestinal bleeding. The exact role of CR in evaluating the colon for a site of bleeding has yet to be specifically identified, yet the images would seem to maintain much of the high contrast of MIPs and at the same time allow for iden- tification of soft tissue abnormalities.”
    Computed Tomography Cinematic Rendering in the Evaluation of Colonic Pathology: Technique and Clinical Applications
    Steven P. Rowe, Linda C. Chu, Elliot K. Fishman,
    J Comput Assist Tomogr 2019 May/Jun;43(3):475-484
  • ”Indeed, a great deal of work remains to be done to validate the utility of CR images in clinical practice, although the technique is promising given the enhanced detail and realistic shadowing intrinsic to the CR methodology. In particular, the use- fulness of CR images for preoperative assessment and correlation of findings as displayed by CR relative to intraoperative experience will be of interest, and these aspects of this new visualization technique should be explored in the context of pathologies of the colon.”
    Computed Tomography Cinematic Rendering in the Evaluation of Colonic Pathology: Technique and Clinical Applications
    Steven P. Rowe, Linda C. Chu, Elliot K. Fishman,
    J Comput Assist Tomogr 2019 May/Jun;43(3):475-484
  • ”CR will almost certainly be used for patient en- gagement and trainee education purposes, where the intuitive display of the relative relationships of objects within the imaged volume can aid in understanding complex anatomy and pathology. Furthermore, the global lighting model that underlies CR may prove useful for the detection and characterization of pathology, as well as after response to therapy, given its ability to accentuate different tissue types.”
    Computed Tomography Cinematic Rendering in the Evaluation of Colonic Pathology: Technique and Clinical Applications
    Steven P. Rowe, Linda C. Chu, Elliot K. Fishman,
    J Comput Assist Tomogr 2019 May/Jun;43(3):475-484
Deep Learning

  • “While the health system believes acquisition and use of this data are in the best interest of its patients (after all, office visits are short, and this knowledge can help guide its physicians as to a patient’s greatest risks), many patients might perceive this as an invasion of privacy and worry that the data might paint an incomplete picture of their lives and lead to unnecessary or inaccurate medical recommendations.”
    Leading your organization to responsible AI
    Roger Burkhardt, Nicolas Hohn, and Chris Wigley
    McKinsey & Company (May 2019)
  • As a result, leaders must ask data-science teams fairly granular questions to understand how they sampled the data to train their models. Do data sets reflect real-world populations? Have they included data that are relevant to minority groups? Will performance tests during model development and use uncover issues with the data set? What could we be missing?
    Leading your organization to responsible AI
    Roger Burkhardt, Nicolas Hohn, and Chris Wigley
    McKinsey & Company (May 2019)
  • Additionally, leaders must encourage their organization to move from a compliance mind-set to a co-creation mind-set in which they share their company’s market and technical acumen in the development of new regulations. Recent work in the United Kingdom between the Financial Conduct Authority (FCA), the country’s banking regulator, and the banking industry offers a model for this new partnership approach. The FCA and banking industry have teamed in creating a “regulatory sandbox” where banks can experiment with AI approaches that challenge or lie outside of current regulatory norms, such as using new data to improve fraud detection or better predict a customer’s propensity to purchase products.
    Leading your organization to responsible AI
    Roger Burkhardt, Nicolas Hohn, and Chris Wigley
    McKinsey & Company (May 2019)
  • “Today, some 80% of large companies have adopted machine learning and other forms of artificial intelligence (AI) in their core business. Five years ago, the figure was less than 10%. Nevertheless, the majority of companies still use AI tools as point solutions — discrete applications, isolated from the wider enterprise IT architecture. That’s what we found in a recent analysis of AI practices at more than 8,300 large, global companies in what we believe is one of the largest-scale studies of enterprise IT systems to date.”
    Taking a Systems Approach to Adopting AI
    by Bhaskar Ghosh, Paul R. Daugherty, H. James Wilson, and Adam Burden
    Harvard Business Review
  • “Edge computing is also breaking boundaries by moving much of the processing out to the edge of networks, where they meet with the physical world, as with smartphones, robots, drones, security cameras, and IoT. For instance, blockchain company Filament is using data-efficient AI, blockchain, and the Internet of Things (IoT) to enable secure and autonomous edge-computing transactions through a decentralized network stack — independent of underlying infrastructure.”
    Taking a Systems Approach to Adopting AI
    by Bhaskar Ghosh, Paul R. Daugherty, H. James Wilson, and Adam Burden
    Harvard Business Review
  • ”Artificial intelligence is a vital part of adaptable systems. Whether it’s virtual agents, natural language processing, machine learning, advanced analytics, or other forms of AI, companies have a host of opportunities to transform the way they do business once their architectures make AI an integral part of the transaction flow. By finding a responsible, transparent balance between human and machine intelligence, and combining it with more basic forms of robotic process automation, adaptable systems can create value in ways that were previously impossible.”
    Taking a Systems Approach to Adopting AI
    by Bhaskar Ghosh, Paul R. Daugherty, H. James Wilson, and Adam Burden
    Harvard Business Review
  • As systems evolve, so must the IT workforce. Companies will need multidisciplinary talent that can bridge infrastructure, development tools, programming languages, AI, and machine learning. They’ll also need to combine human talent with a growing army of smart machines to create entirely new kinds of hybrid IT roles. And they’ll need to develop new ways to continuously evolve their workforce, using ongoing learning and organizational transformation to adapt to the relentless pace of systemic AI advances.
    Taking a Systems Approach to Adopting AI
    by Bhaskar Ghosh, Paul R. Daugherty, H. James Wilson, and Adam Burden
    Harvard Business Review

  • Pneumothorax Detection

  • PE Detection

  • PE Detection

  • AI and MR of the Knee
  • Purpose: To investigate the feasibility of using a deep learning–based approach to detect an anterior cruciate ligament (ACL) tear within the knee joint at MRI by using arthroscopy as the reference standard.
    Results: The sensitivity and specificity of the ACL tear detection system at the optimal threshold were 0.96 and 0.96, respectively. In comparison, the sensitivity of the clinical radiologists ranged between 0.96 and 0.98, while the specificity ranged between 0.90 and 0.98. There was no statistically significant difference in diagnostic performance between the ACL tear detection system and clinical radiologists at P < .05. The area under the ROC curve for the ACL tear detection system was 0.98, indicating high overall diagnostic accuracy.
    Conclusion: There was no significant difference between the diagnostic performance of the ACL tear detection system and clinical radiologists for determining the presence or absence of an ACL tear at MRI.
    Fully Automated Diagnosis of Anterior Cruciate Ligament Tears on Knee MR Images by Using Deep Learning
    Fang Liu et al.
    Radiology: Artificial Intelligence 2019; 1(3):e180091 • https://doi.org/10.1148/ryai.2019180091
  • Purpose: To investigate the feasibility of using a deep learning–based approach to detect an anterior cruciate ligament (ACL) tear within the knee joint at MRI by using arthroscopy as the reference standard. 0.98, indicating high overall diagnostic accuracy.
    Conclusion: There was no significant difference between the diagnostic performance of the ACL tear detection system and clinical radiologists for determining the presence or absence of an ACL tear at MRI.
    Fully Automated Diagnosis of Anterior Cruciate Ligament Tears on Knee MR Images by Using Deep Learning
    Fang Liu et al.
    Radiology: Artificial Intelligence 2019; 1(3):e180091 • https://doi.org/10.1148/ryai.2019180091
  • Results: The sensitivity and specificity of the ACL tear detection system at the optimal threshold were 0.96 and 0.96, respectively. In comparison, the sensitivity of the clinical radiologists ranged between 0.96 and 0.98, while the specificity ranged between 0.90 and 0.98. There was no statistically significant difference in diagnostic performance between the ACL tear detection system and clinical radiologists at P < .05. The area under the ROC curve for the ACL tear detection system was 0.98, indicating high overall diagnostic accuracy.
    Conclusion: There was no significant difference between the diagnostic performance of the ACL tear detection system and clinical radiologists for determining the presence or absence of an ACL tear at MRI.
    Fully Automated Diagnosis of Anterior Cruciate Ligament Tears on Knee MR Images by Using Deep Learning
    Fang Liu et al.
    Radiology: Artificial Intelligence 2019; 1(3):e180091 • https://doi.org/10.1148/ryai.2019180091
  • Summary
    * There was no statistically significant difference between the anterior cruciate ligament (ACL) tear detection system and clinical radiologists with varying levels of experience for determining the presence or absence of a full-thickness ACL tear using sagittal proton density–weighted and fat-suppressed T2-weighted fast spin-echo MR images.
    Key Points
    * There was no significant difference between the diagnostic perfor mance of a fully automated deep learning–based diagnosis system and clinical radiologists for detecting a full-thickness anterior cruciate ligament (ACL) tear at MRI.
    * Sensitivity and specificity of the ACL tear detection system at the optimal threshold were 0.96 and 0.96, respectively; the sensitivity of the clinical radiologists ranged between 0.96 and 0.98 and specificity ranged between 0.90 and 0.98.
    Fully Automated Diagnosis of Anterior Cruciate Ligament Tears on Knee MR Images by Using Deep Learning
    Fang Liu et al.
    Radiology: Artificial Intelligence 2019; 1(3):e180091 • https://doi.org/10.1148/ryai.2019180091
  • Key Points
    * There was no significant difference between the diagnostic performance of a fully automated deep learning–based diagnosis system and clinical radiologists for detecting a full-thickness anterior cru- ciate ligament (ACL) tear at MRI.
    * Sensitivity and specificity of the ACL tear detection system at the optimal threshold were 0.96 and 0.96, respectively; the sensitivity of the clinical radiologists ranged between 0.96 and 0.98 and specificity ranged between 0.90 and 0.98.
    Fully Automated Diagnosis of Anterior Cruciate Ligament Tears on Knee MR Images by Using Deep Learning
    Fang Liu et al.
    Radiology: Artificial Intelligence 2019; 1(3):e180091 • https://doi.org/10.1148/ryai.2019180091

  • Fully Automated Diagnosis of Anterior Cruciate Ligament Tears on Knee MR Images by Using Deep Learning
    Fang Liu et al.
    Radiology: Artificial Intelligence 2019; 1(3):e180091 • https://doi.org/10.1148/ryai.2019180091
  • The aim of the Guidelines is to promote Trustworthy AI. Trustworthy AI has three components, which should be met throughout the system's entire life cycle: (1) it should be lawful, complying with all applicable laws and regulations (2) it should be ethical, ensuring adherence to ethical principles and values and (3) it should be robust, both from a technical and social perspective since, even with good intentions, AI systems can cause unintentional harm. Each component in itself is necessary but not sufficient for the achievement of Trustworthy AI. Ideally, all three components work in harmony and overlap in their operation. If, in practice, tensions arise between these components, society should endeavor to align them.
    ETHICS GUIDELINES FOR TRUSTWORTHY AI
    High-Level Expert Group on Artificial Intelligence
    European Commission 2019
  • Artificial intelligence (AI) software that analyzes medical images is becoming increasingly prevalent. Unlike earlier generations of AI software, which relied on expert knowledge to identify imaging features, machine learning approaches automatically learn to recognize these features. However, the promise of accurate personalized medicine can only be fulfilled with access to large quantities of medical data from patients. This data could be used for purposes such as predicting disease, diagnosis, treatment optimization, and prognostication. Radiology is positioned to lead development and implementation of AI algorithms and to manage the associated ethical and legal challenges. This white paper from the Canadian Association of Radiologists provides a framework for study of the legal and ethical issues related to AI in medical imaging, related to patient data (privacy, confidentiality, ownership, and sharing); algorithms (levels of autonomy, liability, and jurisprudence); practice (best practices and current legal framework); and finally, opportunities in AI from the perspective of a universal health care system.
    Canadian Association of Radiologists White Paper on Ethical and Legal Issues Related to Artificial Intelligence in Radiology
    Jacob L. Jaremko
    Can Assoc Radiol J. 2019 May;70(2):107-118
  • ”This white paper from the Canadian Association of Radiologists provides a framework for study of the legal and ethical issues related to AI in medical imaging, related to patient data (privacy, confidentiality, ownership, and sharing); algorithms (levels of autonomy, liability, and jurisprudence); practice (best practices and current legal framework); and finally, opportunities in AI from the perspective of a universal health care system.”
    Canadian Association of Radiologists White Paper on Ethical and Legal Issues Related to Artificial Intelligence in Radiology
    Jacob L. Jaremko
    Can Assoc Radiol J. 2019 May;70(2):107-118
  • ” Sharing medical data for research purposes is a complex issue balancing individual privacy rights versus potential collective societal benefits. This is particularly important for radiology AI data analysis, which uniquely requires large quantities of sensitive image data for algorithm training. A paradigm shift from a patient’s right to near-absolute data privacy, to the sharing of anonymized data becoming regarded as one of the duties or responsibilities of a citizen is underway. This requires a move from ‘‘informed consent’’ for traditional research projects, toward other forms of consent (‘‘broad consent,’’ ‘‘opt-out’ consent,’’ ‘‘presumed consent’’) for AI data analyses.”
    Canadian Association of Radiologists White Paper on Ethical and Legal Issues Related to Artificial Intelligence in Radiology
    Jacob L. Jaremko
    Can Assoc Radiol J. 2019 May;70(2):107-118
  • The institution implementing AI could potentially be held liable for AI-related medical error in several ways. It could be held responsible for malpractice under ‘‘vicarious liability’’ in the following circumstances: (1) if the AI system is deemed equivalent to an employee, or as a ‘‘learned intermediary,’’ or (2) if the AI system is deemed a technological device that the institution has a duty to deploy appropriately. The AI technology manufacturer/developer could theoretically be held liable under ‘‘products liability,’’ though this type of liability is notoriously difficult to demonstrate for computer software.
    Canadian Association of Radiologists White Paper on Ethical and Legal Issues Related to Artificial Intelligence in Radiology
    Jacob L. Jaremko
    Can Assoc Radiol J. 2019 May;70(2):107-118
  • “The issue of who owns this personal health data is characterized by a complex tension between health care provider proprietary interests, patient privacy, copyright issues, and AI developer intellectual property, and an overarching public interest in open access to data that can improve medical care. In Canada this is complicated by the constitutional division of powers, under which copyright law is in federal jurisdiction, governed by the Copyright Act R.S.C. 1985, c. C-42, while health care is in the jurisdiction of provinces and territories. To the question, ‘‘who owns patient medical data in Canada,’’ the answer is nuanced and depends on how and by whom the data is being used."
    Canadian Association of Radiologists White Paper on Ethical and Legal Issues Related to Artificial Intelligence in Radiology
    Jacob L. Jaremko
    Can Assoc Radiol J. 2019 May;70(2):107-118

  • Canadian Association of Radiologists White Paper on Ethical and Legal Issues Related to Artificial Intelligence in Radiology Jacob L. Jaremko Can Assoc Radiol J. 2019 May;70(2):107-118

  • Value of Data in the AI Era
  • Patient Data and AI: Who has access to what?
    - Who owns the patient data? (medical records, imaging studies, pathology report)
    - Who controls access to the patient data?
    - What do hospitals need to do to control the use of patient data?
    - What is a patients rights to their own data?
  • “To be clear, I welcome general artificial intelligence with open arms, because it will generate unprecedented prosperity for the human race just as automation has for centuries. However, it is coun- terproductive to prematurely announce its arrival. As radiologists, it behooves us to educate ourselves so that we can cut through the hype and harness the very real power of deep learning as it exists today, even with its substantial limitations. To channel Mark Twain, the reports of radiology’s demise are greatly exaggerated.”
    Why Radiologists Have Nothing to Fear From Deep Learning
    Alex Bratt
    JACR 2019 (in press)
  • ”Even when sufficient progress is made to overcome the aforementioned shortcomings, there is no reason to think that radiologists are any more likely to be displaced than artists, journalists, or CEOs, because breaking the barriers of long-term dependencies and abstract reasoning is likely to enable sweeping automation in these fields as well.”
    Why Radiologists Have Nothing to Fear From Deep Learning
    Alex Bratt
    JACR 2019 (in press)
  • Objective: Sepsis remains a costly and prevalent syndrome in hospitals; however, machine learning systems can increase timely sepsis detection using electronic health records. This study validates a gradient boosted ensemble machine learning tool for sepsis detection and prediction, and compares its performance to existing methods.
    Results: The MLA achieved an AUROC of 0.88, 0.84, and 0.83 for sepsis onset and 24 and 48 h prior to onset, respectively. These values were superior to those of SIRS (0.66), MEWS (0.61), SOFA (0.72), and qSOFA (0.60) at time of onset. When trained on UCSF data and tested on BIDMC data, sepsis onset AUROC was 0.89.
    Discussion and conclusion: The MLA predicts sepsis up to 48 h in advance and identifies sepsis onset more accurately than commonly used tools, maintaining high performance for sepsis detection when trained and tested on separate datasets.
    Evaluation of a machine learning algorithm for up to 48-hour advance prediction of sepsis using six vital signs
    Barton C et al.
    Computers in Biology and Medicine 109 (2019) 79-84
  • Objective: Sepsis remains a costly and prevalent syndrome in hospitals; however, machine learning systems can increase timely sepsis detection using electronic health records. This study validates a gradient boosted ensemble machine learning tool for sepsis detection and prediction, and compares its performance to existing methods.
    Discussion and conclusion: The MLA predicts sepsis up to 48 h in advance and identifies sepsis onset more accurately than commonly used tools, maintaining high performance for sepsis detection when trained and tested on separate datasets.
    Evaluation of a machine learning algorithm for up to 48-hour advance prediction of sepsis using six vital signs
    Barton C et al.
    Computers in Biology and Medicine 109 (2019) 79-84
  • “The machine learning algorithm assessed in this study is capable of predicting sepsis up to 48 h in advance of onset with an AUROC of 0.83. This performance exceeds that of commonly used detection methods at time of onset, and may in turn lead to improved patient outcomes through early detection and clinical intervention.”
    Evaluation of a machine learning algorithm for up to 48-hour advance prediction of sepsis using six vital signs
    Barton C et al.
    Computers in Biology and Medicine 109 (2019) 79-84
  • The Role of AI in the Diagnosis and Management of PDAC (2025)
    - Early detection of pancreatic cancer (FELIX)
    - Define the best management plan for the patient and the sequence (Surgery, Chemotherapy, Immunology, Radiation Therapy)
    - Predict ultimate survival for the patient based on a variable set of parameters
  • “Not surprisingly, though, as AI supercharges business and society, CEOs are under the spotlight to ensure their company’s responsible use of AI systems beyond complying with the spirit and letter of applicable laws. Ethical debates are well underway about what’s “right” and “wrong” when it comes to high-stakes AI applications such as autonomous weapons and surveillance systems. And there’s an outpouring of concern and skepticism regarding how we can imbue AI systems with human ethical judgment, when moral values frequently vary by culture and can be difficult to code in software.”
    Leading your organization to responsible AI
    Roger Burkhardt, Nicolas Hohn, and Chris Wigley
    McKinsey & Company (May 2019)
  • “AI development always involves trade-offs. For instance, when it comes to model development, there is often a perceived trade-off between the accuracy of an algorithm and the transparency of its decision making, or how easily predictions can be explained to stakeholders. Too great a focus on accuracy can lead to the creation of “black box” algorithms in which no one can say for certain why an AI system made the recommendation it did. Likewise, the more data that models can analyze, the more accurate the predictions, but also, often, the greater the privacy concerns.”
    Leading your organization to responsible AI
    Roger Burkhardt, Nicolas Hohn, and Chris Wigley
    McKinsey & Company (May 2019)
  • “Data serve as the fuel for AI. In general, the more data used to train systems, the more accurate and insightful the predictions. However, pressure on analytics teams to innovate can lead to the use of third-party data or the repurposing of existing customer data in ways that, while not yet covered by regulations, are considered inappropriate by consumers. For example, a healthcare provider might buy data about its patients—such as what restaurants they frequent or how much TV they watch—from data brokers to help doctors better assess each patient’s health risk.”
    Leading your organization to responsible AI
    Roger Burkhardt, Nicolas Hohn, and Chris Wigley
    McKinsey & Company (May 2019)
  • Background: Variation between radiologists when making recommendations for additional imaging and associated factors are, to the knowledge of the authors, unknown. Clear identification of factors that account for variation in follow-up recommendations might prevent unnecessary tests for incidental or ambiguous image findings.
    Purpose: To determine incidence and identify factors associated with follow-up recommendations in radiology reports from multiple modalities, patient care settings, and imaging divisions.
    Conclusion: Substantial interradiologist variation exists in the probability of recommending a follow-up examination in a radiology report, after adjusting for patient, examination, and radiologist factors.
    Variation in Follow-up Imaging Recommendations in Radiology Reports: Patient, Modality, and Radiologist Predictors
    LailaR.Cochon et al.
    Radiology 2019; 00:1–8 • https://doi.org/10.1148/radiol.2019182826
  • Materials and Methods: This retrospective study analyzed 318 366 reports obtained from diagnostic imaging examinations performed at a large urban quaternary care hospital from January 1 to December 31, 2016, excluding breast and US reports. A subset of 1000 reports were randomly selected and manually annotated to train and validate a machine learning algorithm to predict whether a report included a follow-up imaging recommendation (training-and-validation set consisted of 850 reports and test set of 150 reports). The trained algorithm was used to classify 318 366 reports. Multivariable logistic regression was used to determine the likelihood of follow-up recommendation. Additional analysis by im aging subspecialty division was performed, and intradivision and interradiologist variability was quantified.
    Variation in Follow-up Imaging Recommendations in Radiology Reports: Patient, Modality, and Radiologist Predictors
    LailaR.Cochon et al.
    Radiology 2019; 00:1–8 • https://doi.org/10.1148/radiol.2019182826
  • Results: The machine learning algorithm classified 38 745 of 318 366 (12.2%) reports as containing follow-up recommendations. Average patient age was 59 years 6 17 (standard deviation); 45.2% (143 767 of 318 366) of reports were from male patients. Among 65 radiologists, 57% (37 of 65) were men. At multivariable analysis, older patients had higher rates of follow-up recom- mendations (odds ratio [OR], 1.01 [95% confidence interval {CI}: 1.01, 1.01] for each additional year), male patients had lower rates of follow-up recommendations (OR, 0.9; 95% CI: 0.9, 1.0), and follow-up recommendations were most common among CT studies (OR, 4.2 [95% CI: 4.0, 4.4] compared with radiography). Radiologist sex (P = .54), presence of a trainee (P = .45), and years in practice (P = .49) were not significant predictors overall. A division-level analysis showed 2.8-fold to 6.7-fold interradiologist variation.
    Variation in Follow-up Imaging Recommendations in Radiology Reports: Patient, Modality, and Radiologist Predictors
    Laila R.Cochon et al.
    Radiology 2019; 00:1–8 • https://doi.org/10.148/radiol.2019182826
  • Purpose: To determine incidence and identify factors associated with follow-up recommendations in radiology reports from multiple modalities, patient care settings, and imaging divisions.
    Results: Radiologist sex (P = .54), presence of a trainee (P = .45), and years in practice (P = .49) were not significant predictors overall. A division-level analysis showed 2.8-fold to 6.7-fold interradiologist variation.
    Conclusion: Substantial interradiologist variation exists in the probability of recommending a follow-up examination in a radiology report, after adjusting for patient, examination, and radiologist factors.
    Variation in Follow-up Imaging Recommendations in Radiology Reports: Patient, Modality, and Radiologist Predictors
    Laila R.Cochon et al.
    Radiology 2019; 00:1–8 • https://doi.org/10.148/radiol.2019182826
  • “In conclusion, we used machine learning to analyze variation found attributable to both radiologist and nonradiologist factors. Whereas radiologist sex, trainee involvement, and experience did not contribute to unwarranted variation in follow-up recommendations, there was substantial variation in follow-up recommendations between radiologists within the same division. Therefore, interventions to reduce unwarranted variation in follow-up recommendations may be most effective if targeted to individual radiologists. Interventions could include feedback reports that show follow-up recommendation rates for individual radiologists, educational efforts to improve awareness and acceptance of evidence-based imaging guidelines, and improved decision support tools. Future studies will be needed to assess the effect multifaceted interventions have on reducing interradiologist variation in follow-up recommendations and the effect on quality of care.”
    Variation in Follow-up Imaging Recommendations in Radiology Reports: Patient, Modality, and Radiologist Predictors
    Laila R.Cochon et al.
    Radiology 2019; 00:1–8 • https://doi.org/10.148/radiol.2019182826

  • Variation in Follow-up Imaging Recommendations in Radiology Reports: Patient, Modality, and Radiologist Predictors
    Laila R.Cochon et al.
    Radiology 2019; 00:1–8 • https://doi.org/10.148/radiol.2019182826
  • “An artificially intelligent computer program can now diagnose skin cancer more accurately than a board-certified dermatologist. Better yet, the program can do it faster and more efficiently, requiring a training data set rather than a decade of expensive and labor-intensive medical education. While it might appear that it is only a matter of time before physicians are rendered obsolete by this type of technology, a closer look at the role this technology can play in the delivery of health care is warranted to appreciate its current strengths, limitations, and ethical complexities.”
    Ethical Dimensions of Using Artificial Intelligence in Health Care
    Michael J. Rigby
    AMA J Ethics. 2019;21(2):E121-124.
  • “Nonetheless, this powerful technology creates a novel set of ethical challenges that must be identified and mitigated since AI technology has tremendous capability to threaten patient preference, safety, and privacy. However, current policy and ethical guidelines for AI technology are lagging behind the progress AI has made in the health care field. While some efforts to engage in these ethical conversations have emerged, the medical community remains ill informed of the ethical complexities that budding AI technology can introduce. Accordingly, a rich discussion awaits that would greatly benefit from physician input, as physicians will likely be interfacing with AI in their daily practice in the near future.”
    Ethical Dimensions of Using Artificial Intelligence in Health Care
    Michael J. Rigby
    AMA J Ethics. 2019;21(2):E121-124.
Kidney

  • OBJECTIVE. Three-dimensional printing is being used for surgical assistance, particularly for robot-assisted partial nephrectomy (RAPN). The objective of this study was to assess the anatomic accuracy of the 3D model used for 3D model–guided RAPN.
    CONCLUSION. Three-dimensional printed models are accurate with respect to anatomic reality. The reliability of surgical assistance with 3D printed models must be evaluated.
    Measurement of the Accuracy of 3D-Printed Medical Models to Be Used for Robot-Assisted Partial Nephrectomy
    Michiels C et al.
    AJR 2019; 213:1–6
  • “Our 3D models are accurate with respect to the anatomic reality of the different measurements and arterial distribution. These 3D models allow use of a clampless technique or segmental renal artery clamping to minimize renal ischemia and to preserve postoperative renal function. The reliability of surgical assistance with 3D printing must be prospectively evaluated.”
    Measurement of the Accuracy of 3D-Printed Medical Models to Be Used for Robot-Assisted Partial Nephrectomy
    Michiels C et al.
    AJR 2019; 213:1–6
  • Three-dimensional printing is appreciated because surgeons can have a tac- tile experience with the renal tumor and re- nal system and thus determine better surgical plans and treatment strategies. Marconi et al. found that 3D printed models assisted medical students, surgeons, and radiologists in identifying anatomic structures.”
    Measurement of the Accuracy of 3D-Printed Medical Models to Be Used for Robot-Assisted Partial Nephrectomy
    Michiels C et al.
    AJR 2019; 213:1–6
  • OBJECTIVE. The objective of our study was to investigate the potential influence of intra- and interobserver manual segmentation variability on the reliability of single-slice–based 2D CT texture analysis of renal masses.
    CONCLUSION. Single-slice–based 2D CT texture analysis of renal masses is sensitive to intra- and interobserver manual segmentation variability. Therefore, it may lead to nonreproducible results in radiomic analysis unless a reliability analysis is considered in the workflow.
    Reliability of Single-Slice–Based 2D CT Texture Analysis of Renal Masses: Influence of Intra- and Interobserver Manual Segmentation Variability on Radiomic Feature Reproducibility
    Kocak B et al.
    AJR 2019; 213:1–7
  • “Lately, texture analysis has been an active area of research in the field of radiomics, suggesting that it can be used in predicting tumor subtypes, tumor stage, tumor grade, response to treatment, genomic profile, and overall survival . Nonetheless, recent evi- dence also suggests that conclusions must be treated with caution because several texture parameters may have reproducibility problems, which is an important challenge for building reliable predictive models to be used in clinical practice.”
    Reliability of Single-Slice–Based 2D CT Texture Analysis of Renal Masses: Influence of Intra- and Interobserver Manual Segmentation Variability on Radiomic Feature Reproducibility
    Kocak B et al.
    AJR 2019; 213:1–7
  • Although whole-tumor segmentation is known to be the most representative for tumor texture , it is considered an impractical and time-consuming process to be used in clinical routine, particularly in large tumors such as renal masses. For renal tumors, there has been a trend toward using a single image slice along with manual segmentation in an attempt to bring texture analysis into a daily routine.
    Reliability of Single-Slice–Based 2D CT Texture Analysis of Renal Masses: Influence of Intra- and Interobserver Manual Segmentation Variability on Radiomic Feature Reproducibility
    Kocak B et al.
    AJR 2019; 213:1–7
  • “In conclusion, single-slice–based 2D CT texture analysis of RCCs is sensitive to intra-and interobserver manual segmentation variability, which may lead to nonreproducible results in radiomic analysis. Therefore, a reliability analysis with as much and heterogeneous data as possible must be incorporated into every scientific research study using this technique. Otherwise, the radiomic studies of renal masses without a reliability analysis might lead to a chain of nonreproducible outcomes in terms of selected texture features and statistical models created, which might further influence the generalizability and replicability of the findings of the radiomic studies. bring texture analysis into a daily routine.”
    Reliability of Single-Slice–Based 2D CT Texture Analysis of Renal Masses: Influence of Intra- and Interobserver Manual Segmentation Variability on Radiomic Feature Reproducibility
    Kocak B et al.
    AJR 2019; 213:1–7
  • In addition, CECT provides more texture features with good to excellent interobserver reliability than unenhanced CT does. Filtered and transformed images might be useful for reducing the influence of manual segmentation variations on single-slice–based 2D CT texture analysis, yielding more features with good to excellent reliability than original images do.
    Reliability of Single-Slice–Based 2D CT Texture Analysis of Renal Masses: Influence of Intra- and Interobserver Manual Segmentation Variability on Radiomic Feature Reproducibility
    Kocak B et al.
    AJR 2019; 213:1–7
Liver

  • “A broad spectrum of pathologic conditions can present as spontaneous hemorrhage within or surrounding the liver and may present acutely or as a chronic or incidental finding. Imaging characteristics and clinical history can often narrow the differential diagnosis and guide management.”
    Bleeding Liver Masses: Imaging Features With Pathologic Correlation and Impact on Management
    Thomas AJ et al.
    AJR 2019; 213:1–9
  • “A broad spectrum of neoplastic and other pathologic conditions can result in hepatic hemorrhage. They have the potential for dev- astating consequences in cases of rupture, including hemorrhagic shock, abdominal compartment syndrome, and intraperitoneal tumor spillage. One can often narrow the differential diagnosis considerably by taking into account both the imaging features and aspects of the clinical history that may suggest a particular diagnosis.”
    Bleeding Liver Masses: Imaging Features With Pathologic Correlation and Impact on Management
    Thomas AJ et al.
    AJR 2019; 213:1–9
  • Bleeding Liver Masses: Differential Dx
    - Hepatoma
    - Hepatic metastases
    - Hepatic angiosarcoma
    - Hepatic adenoma
    - Cavernous hemangioma
    - Hepatic cyst
    - Traumatic lesions
  • Bleeding Liver Masses: Differential Dx
    - Hepatic artery aneurysm and pseudoaneurysm
    - HELLP syndrome
    - Peliosis
    - Miss. Lesions (FNH, hepatoblastoma, cirrhosis without tumor)
  • “Patients often present with nonspecific clinical symptoms, such as pain, vomiting, and malaise. The hematoma can rupture into the subcapsular space of the liver, where it is contained between the liver and its capsule, resulting in an elliptic collection that com presses the liver . Subcapsular extension is more likely with peripherally located lesions. When it is no longer contained by the capsule, the hemorrhage can rupture into the peritoneal space. This can have devastating consequences, such as hemorrhagic shock and abdominal compartment syndrome.”
    Bleeding Liver Masses: Imaging Features With Pathologic Correlation and Impact on Management
    Thomas AJ et al.
    AJR 2019; 213:1–9
  • “Arterial phase imaging may reveal contrast extravasation, indicating active bleeding that may require urgent endovascular therapy. An ancillary sign of severe bleeding is flattening of the inferior vena cava, which can reflect hypovolemia or shock. In addition, periportal areas of low attenuation may result from aggressive fluid resuscitation.”
    Bleeding Liver Masses: Imaging Features With Pathologic Correlation and Impact on Management
    Thomas AJ et al.
    AJR 2019; 213:1–9
  • “The annual incidence of HCC in patients with cirrhosis is estimated at 2–8%, risk depending on the severity of the cirrhosis, underlying cause (viral hepatitis being the greatest risk), male sex, and coinfection with HIV.”
    Bleeding Liver Masses: Imaging Features With Pathologic Correlation and Impact on Management
    Thomas AJ et al.
    AJR 2019; 213:1–9
  • “Hepatocellular carcinoma (HCC) is the fifth most common malignancy in the world among men and the seventh most common among women. It is the second leading cause of cancer-related mortality. It is seen overwhelmingly in patients with cirrhosis, most often as a sequela of hepatitis B or hepatitis C. Other predisposing fac tors include alcohol abuse, nonalcoholic fatty liver disease, hemochromatosis, dietary exposure to aflatoxins, and α1-antitrypsin deficiency.”
    Bleeding Liver Masses: Imaging Features With Pathologic Correlation and Impact on Management
    Thomas AJ et al.
    AJR 2019; 213:1–9
  • “A frequent complication of these tumors is rupture with hemorrhage into the subcapsular or peritoneal space. Estimated to occur in 3–15% of cases, this complication is a cause of considerable morbidity and mortality. Although an underlying lesion may frequently be obscured or incompletely evaluated in patients with an acute bleed, a high index of suspicion should be maintained in the care of patients with cirrhosis and hepatic bleeding.”
    Bleeding Liver Masses: Imaging Features With Pathologic Correlation and Impact on Management
    Thomas AJ et al.
    AJR 2019; 213:1–9
  • “The cause of hemorrhage in HCCs is likely multifactorial and is not completely understood. Rupture may be facilitated by tumor angiogenesis, which increases as it mutates from a cirrhotic nodule to a dysplastic nodule to HCC. This leads to an increasing proportion of its vascular supply being derived from new, unpaired hepatic arteries with diminishing contributions from the portal venous system. Microinjuries and elastin deposition in these small arteries may predispose to hemorrhage.”
    Bleeding Liver Masses: Imaging Features With Pathologic Correlation and Impact on Management
    Thomas AJ et al.
    AJR 2019; 213:1–9
  • “Patients with HCC are often at increased baseline risk of bleeding due to underlying cirrhosis-associated coagulopathy. Although they are commonly spontaneous, hemorrhagic complications may also be precipitated by minor trauma or transarterial chemoembolization.”
    Bleeding Liver Masses: Imaging Features With Pathologic Correlation and Impact on Management
    Thomas AJ et al.
    AJR 2019; 213:1–9
  • Key features that predispose a particular tumor to rupture include its location within the liver and its size. The highest-risk tumors are large and within the periphery of the liver, either abutting or protruding beyond the capsule. The minimal thickness of peritumor liver parenchyma and the degree of capsular protrusion both have been associated with increased risk of rupture.
    Bleeding Liver Masses: Imaging Features With Pathologic Correlation and Impact on Management
    Thomas AJ et al.
    AJR 2019; 213:1–9
  • “However, several primary tumors cause hypervascular liver metastases, including neuroendocrine tumors, renal cell carcinoma, melanoma, choriocarcinoma, and sarcoma, which may increase the risk of bleeding.”
    Bleeding Liver Masses: Imaging Features With Pathologic Correlation and Impact on Management
    Thomas AJ et al.
    AJR 2019; 213:1–9
  • “Hepatic angiosarcoma, although very rare, is the third most common primary hepatic malignancy. It has been associated with a variety of environmental exposures, most notably thorium dioxide (Thorotrast, Testagar), arsenic, and polyvinyl chloride. It is most commonly seen in men in the sixth and seventh decades of life. It is highly aggressive and often metastatic at diagnosis; common sites include nearby structures, such as the spleen, stomach, and peritoneum, and distant sites, such as lungs, bone, and brain.”
    Bleeding Liver Masses: Imaging Features With Pathologic Correlation and Impact on Management
    Thomas AJ et al.
    AJR 2019; 213:1–9
  • “As an endothelial tumor, angiosarcoma is composed of abundant anastomosing vascular channels with regions of necrosis and blood-filled cysts. At imaging it can display one of several patterns, including a dominant mass, multiple nodules, or diffuse infiltrative tumor. Angiosarcoma is generally hypervascular and exhibits heterogeneous enhancement, potentially mimicking cavernous hemangioma. Rupture is a devastating complication of hepatic angiosarcoma. In addition to the dangers of the acute hemorrhage, spillage can cause peritoneal angiosarcomatosis and subsequent recurrent hemoperitoneum, which have a dismal prognosis.”
    Bleeding Liver Masses: Imaging Features With Pathologic Correlation and Impact on Management
    Thomas AJ et al.
    AJR 2019; 213:1–9
  • ”Resection of adenomas is generally considered once lesions reach a diameter greater than 5 cm, because the likelihood of rupture increases with lesion size. For patients who are not eligible for surgery, other options include embolization or percutaneous ablation, although this practice may evolve with advances in adenoma subtyping, potentially allowing more conservative management of lower-risk lesions.”
    Bleeding Liver Masses: Imaging Features With Pathologic Correlation and Impact on Management
    Thomas AJ et al.
    AJR 2019; 213:1–9
  • ”The inflammatory subtype is most common and has the greatest propensity for hemorrhage, approximately 20–25% displaying intratumoral hemorrhage. Other subtypes, including hepatocyte nuclear factor 1α (HNF-1α)−mutated adenomas, β-catenin– mutated adenomas, and unclassified subtype, are thought to carry lower risk of hemorrhage and rupture. Inflammatory adenomas have an approximately 10% chance of malignant transformation, which is less than in β-catenin–mutated adenomas but more than in HNF-1α–mutated tumors.”
    Bleeding Liver Masses: Imaging Features With Pathologic Correlation and Impact on Management
    Thomas AJ et al.
    AJR 2019; 213:1–9
  • ” Giant hemangiomas are generally well defined and have round or lobular margins. Compared with their smaller counterparts, giant hemangiomas often have a more complex imaging appearance that includes internal hemorrhage and areas of central necrosis, scarring, or calcification. Another potential complication of these large lesions is Kasabach-Merritt syndrome, a consumptive coagulopathy that, although more common in infants, can develop in adults. It has a mortality rate of 10–37%.”
    Bleeding Liver Masses: Imaging Features With Pathologic Correlation and Impact on Management
    Thomas AJ et al.
    AJR 2019; 213:1–9
  • “Aneurysms of the hepatic artery are the second most common visceral aneurysm, after splenic artery aneurysms. The most common cause is atherosclerosis, but aneurysms can also occur in vasculopathies such as fibromuscular dysplasia and polyarteritis nodosa and in patients with systemic infection, which can cause mycotic aneurysm. Pseudo- aneurysms more commonly result from trau- ma and liver transplant.”
    Bleeding Liver Masses: Imaging Features With Pathologic Correlation and Impact on Management
    Thomas AJ et al.
    AJR 2019; 213:1–9
  • ”Both aneurysms and pseudoaneurysms carry risk of rupture. Aneurysms are usually considered for elective repair if they are larger than 2 cm in diameter or are symptomatic. Pseudoaneurysms have a high propensity for bleeding and are generally repaired endovascularly regardless of size.”
    Bleeding Liver Masses: Imaging Features With Pathologic Correlation and Impact on Management
    Thomas AJ et al.
    AJR 2019; 213:1–9
  • ”HELLP syndrome (hemolysis, elevated liver enzyme levels, and low platelets) is a complication that occurs most often in the third trimester or soon after birth. It is associated with severe preeclampsia. It is thought to be a placenta-induced disease with resulting inflammation and coagulation involving the liver. Sinusoidal thrombi cause periportal hematoma, which can expand to result in subcapsular hematoma or hemoperitoneum. Imaging features in addition to hemorrhage that suggest this diagnosis are hepatomegaly, steatosis, and periportal edema.”
    Bleeding Liver Masses: Imaging Features With Pathologic Correlation and Impact on Management
    Thomas AJ et al.
    AJR 2019; 213:1–9
  • “Peliosis is a rare rare condition that occurs in association with a variety of conditions, including Bartonella infection in patients with HIV infection, chronic wasting diseases, solid organ transplant, and use of certain medications. Peliosis is usually asymptomatic and thus is often incidentally diagnosed. Peliosis consists of dilated hepatic sinuses that enlarge into blood-filled lacunes that may eventually rupture. CT images may show peliotic cavities in the liver that may contain variable amounts of hemorrhage. A characteristic enhancing central dot (target sign) and progressive centrifugal enhancement are seen at multiphase CT and MRI. Similar findings can be seen in other organs, most commonly the spleen.”
    Bleeding Liver Masses: Imaging Features With Pathologic Correlation and Impact on Management
    Thomas AJ et al.
    AJR 2019; 213:1–9
  • Patients with cirrhosis may also experience spontaneous hemorrhage even in the absence of HCC; regenerative nodules, likely in combination with coagulopathy, have been reported to hemorrhage spontaneously Because of the rarity of hemorrhage with these causes, they are not discussed in detail herein but may be considered in the appropriate clinical setting.”
    Bleeding Liver Masses: Imaging Features With Pathologic Correlation and Impact on Management
    Thomas AJ et al.
    AJR 2019; 213:1–9
  • “A broad spectrum of neoplastic and other pathologic conditions can result in hepatic hemorrhage. They have the potential for dev- astating consequences in cases of rupture, including hemorrhagic shock, abdominal compartment syndrome, and intraperitoneal tumor spillage. One can often narrow the differential diagnosis considerably by taking into account both the imaging features and aspects of the clinical history that may suggest a particular diagnosis.”
    Bleeding Liver Masses: Imaging Features With Pathologic Correlation and Impact on Management
    Thomas AJ et al.
    AJR 2019; 213:1–9
Musculoskeletal


  • AI and MR of the Knee
  • Purpose: To investigate the feasibility of using a deep learning–based approach to detect an anterior cruciate ligament (ACL) tear within the knee joint at MRI by using arthroscopy as the reference standard.
    Results: The sensitivity and specificity of the ACL tear detection system at the optimal threshold were 0.96 and 0.96, respectively. In comparison, the sensitivity of the clinical radiologists ranged between 0.96 and 0.98, while the specificity ranged between 0.90 and 0.98. There was no statistically significant difference in diagnostic performance between the ACL tear detection system and clinical radiologists at P < .05. The area under the ROC curve for the ACL tear detection system was 0.98, indicating high overall diagnostic accuracy.
    Conclusion: There was no significant difference between the diagnostic performance of the ACL tear detection system and clinical radiologists for determining the presence or absence of an ACL tear at MRI.
    Fully Automated Diagnosis of Anterior Cruciate Ligament Tears on Knee MR Images by Using Deep Learning
    Fang Liu et al.
    Radiology: Artificial Intelligence 2019; 1(3):e180091 • https://doi.org/10.1148/ryai.2019180091
  • Purpose: To investigate the feasibility of using a deep learning–based approach to detect an anterior cruciate ligament (ACL) tear within the knee joint at MRI by using arthroscopy as the reference standard. 0.98, indicating high overall diagnostic accuracy.
    Conclusion: There was no significant difference between the diagnostic performance of the ACL tear detection system and clinical radiologists for determining the presence or absence of an ACL tear at MRI.
    Fully Automated Diagnosis of Anterior Cruciate Ligament Tears on Knee MR Images by Using Deep Learning
    Fang Liu et al.
    Radiology: Artificial Intelligence 2019; 1(3):e180091 • https://doi.org/10.1148/ryai.2019180091
  • Results: The sensitivity and specificity of the ACL tear detection system at the optimal threshold were 0.96 and 0.96, respectively. In comparison, the sensitivity of the clinical radiologists ranged between 0.96 and 0.98, while the specificity ranged between 0.90 and 0.98. There was no statistically significant difference in diagnostic performance between the ACL tear detection system and clinical radiologists at P < .05. The area under the ROC curve for the ACL tear detection system was 0.98, indicating high overall diagnostic accuracy.
    Conclusion: There was no significant difference between the diagnostic performance of the ACL tear detection system and clinical radiologists for determining the presence or absence of an ACL tear at MRI.
    Fully Automated Diagnosis of Anterior Cruciate Ligament Tears on Knee MR Images by Using Deep Learning
    Fang Liu et al.
    Radiology: Artificial Intelligence 2019; 1(3):e180091 • https://doi.org/10.1148/ryai.2019180091
  • Summary
    * There was no statistically significant difference between the anterior cruciate ligament (ACL) tear detection system and clinical radiologists with varying levels of experience for determining the presence or absence of a full-thickness ACL tear using sagittal proton density–weighted and fat-suppressed T2-weighted fast spin-echo MR images.
    Key Points
    * There was no significant difference between the diagnostic performance of a fully automated deep learning–based diagnosis system and clinical radiologists for detecting a full-thickness anterior cruciate ligament (ACL) tear at MRI.
    * Sensitivity and specificity of the ACL tear detection system at the optimal threshold were 0.96 and 0.96, respectively; the sensitivity of the clinical radiologists ranged between 0.96 and 0.98 and specificity ranged between 0.90 and 0.98.
    Fully Automated Diagnosis of Anterior Cruciate Ligament Tears on Knee MR Images by Using Deep Learning
    Fang Liu et al.
    Radiology: Artificial Intelligence 2019; 1(3):e180091 • https://doi.org/10.1148/ryai.2019180091
  • Key Points
    * There was no significant difference between the diagnostic performance of a fully automated deep learning–based diagnosis system and clinical radiologists for detecting a full-thickness anterior cru- ciate ligament (ACL) tear at MRI.
    * Sensitivity and specificity of the ACL tear detection system at the optimal threshold were 0.96 and 0.96, respectively; the sensitivity of the clinical radiologists ranged between 0.96 and 0.98 and specificity ranged between 0.90 and 0.98.
    Fully Automated Diagnosis of Anterior Cruciate Ligament Tears on Knee MR Images by Using Deep Learning
    Fang Liu et al.
    Radiology: Artificial Intelligence 2019; 1(3):e180091 • https://doi.org/10.1148/ryai.2019180091

  • Fully Automated Diagnosis of Anterior Cruciate Ligament Tears on Knee MR Images by Using Deep Learning
    Fang Liu et al.
    Radiology: Artificial Intelligence 2019; 1(3):e180091 • https://doi.org/10.1148/ryai.2019180091
Pancreas

  • OBJECTIVE. The objective of our study was to determine the utility of radiomics features in differentiating CT cases of pancreatic ductal adenocarcinoma (PDAC) from normal pancreas.
    RESULTS. Mean tumor size was 4.1 ± 1.7 (SD) cm. The overall accuracy of the random forest binary classification was 99.2% (124/125), and AUC was 99.9%. All PDAC cases (60/60) were correctly classified. One case from a renal donor was misclassified as PDAC (1/65). The sensitivity was 100%, and specificity was 98.5%.
    CONCLUSION. Radiomics features extracted from whole pancreas can be used to differentiate between CT cases from patients with PDAC and healthy control subjects with normal pancreas.
    Utility of CT Radiomics Features in Differentiation of Pancreatic Ductal Adenocarcinoma From Normal Pancreatic Tissue
    Chu LC, Park S, Fishman EK et al
    AJR 2019; 213:1–9
  • RESULTS. Mean tumor size was 4.1 ± 1.7 (SD) cm. The overall accuracy of the random forest binary classification was 99.2% (124/125), and AUC was 99.9%. All PDAC cases (60/60) were correctly classified. One case from a renal donor was misclassified as PDAC (1/65). The sensitivity was 100%, and specificity was 98.5%.
    CONCLUSION. Radiomics features extracted from whole pancreas can be used to differentiate between CT cases from patients with PDAC and healthy control subjects with normal pancreas.
    Utility of CT Radiomics Features in Differentiation of Pancreatic Ductal Adenocarcinoma From Normal Pancreatic Tissue
    Chu LC, Park S et al.
    AJR 2019; 213:1–9
  • “CT features of early PDAC can be subtle and missed by even experienced radiologists. Early signs of PDAC such as pancreatic parenchyma inhomogeneity and loss of normal fatty marbling of the pancreas have been described on retrospective CT review up to 34 months before the diagnosis of PDAC. Quantitative analysis of these imaging features offers the potential for computer-aided diagnosis of PDAC.”
    Utility of CT Radiomics Features in Differentiation of Pancreatic Ductal Adenocarcinoma From Normal Pancreatic Tissue
    Chu LC, Park S, Kawamoto S, Fouladi DF, Shayesteh S, Zinreich ES, Graves JS, Horton KM, Hruban RH, Yuille AL, Kinzler KW, Vogelstein B, Fishman EK.
    AJR Am J Roentgenol. 2019 Apr 23:1-9.
  • “This study aimed to tackle the second goal—differentiation of abnormal from normal pancreatic tissue using segmentation of the entire pancreas (i.e., without relying on separate segmentation of the tumor region). Our results showed that, after manual segmentation of pancreas boundaries, radiomics features and the random forest classifier were highly accurate in differentiating PDAC cases from normal control cases (sensitivity, 100%; specificity, 98.5%; accuracy, 99.2%). The radiomics features most relevant to differentiate PDAC from normal pancreas were based on shape and textural heterogeneity.”
    Utility of CT Radiomics Features in Differentiation of Pancreatic Ductal Adenocarcinoma From Normal Pancreatic Tissue
    Chu LC, Park S, Kawamoto S, Fouladi DF, Shayesteh S, Zinreich ES, Graves JS, Horton KM, Hruban RH, Yuille AL, Kinzler KW, Vogelstein B, Fishman EK.
    AJR Am J Roentgenol. 2019 Apr 23:1-9.
  • “Given the high accuracy of automatic pan- creas segmentation by existing algorithms, these algorithms could be used to generate the boundaries for pancreas segmentation, and then the radiomics feature analysis algorithm could be performed to differentiate PDAC from normal pancreas. Some technical hurdles need to be overcome before these complex algorithms can be combined, but we anticipate that will be possible in the near future.”
    Utility of CT Radiomics Features in Differentiation of Pancreatic Ductal Adenocarcinoma From Normal Pancreatic Tissue
    Chu LC, Park S, Kawamoto S, Fouladi DF, Shayesteh S, Zinreich ES, Graves JS, Horton KM, Hruban RH, Yuille AL, Kinzler KW, Vogelstein B, Fishman EK.
    AJR Am J Roentgenol. 2019 Apr 23:1-9.
  • ”All of the scans in the current study were obtained at a single institution on units manufactured by a single vendor using matched protocols and the same reconstruction algorithm. Differences in image acquisition, reconstruction, segmentation, and feature extraction can affect radiomics features and results. There is currently no standardization in the optimal protocol for imaging acquisition and postprocessing for radiomics analysis.”
    Utility of CT Radiomics Features in Differentiation of Pancreatic Ductal Adenocarcinoma From Normal Pancreatic Tissue
    Chu LC, Park S, Kawamoto S, Fouladi DF, Shayesteh S, Zinreich ES, Graves JS, Horton KM, Hruban RH, Yuille AL, Kinzler KW, Vogelstein B, Fishman EK.
    AJR Am J Roentgenol. 2019 Apr 23:1-9.
  • “This preliminary study showed that the radiomics features extracted from the whole pancreas can be used to differentiate between CT images of patients with PDAC and CT images of healthy control subjects. There is the potential to combine this algorithm with automatic organ segmentation algorithms for automatic detection of PDAC.”
    Utility of CT Radiomics Features in Differentiation of Pancreatic Ductal Adenocarcinoma From Normal Pancreatic Tissue
    Chu LC, Park S, Kawamoto S, Fouladi DF, Shayesteh S, Zinreich ES, Graves JS, Horton KM, Hruban RH, Yuille AL, Kinzler KW, Vogelstein B, Fishman EK.
    AJR Am J Roentgenol. 2019 Apr 23:1-9.
Small Bowel

  • Results Intratumoral hemorrhage was seen in 15.4% and 25.6% of GISTs and in 0% and 0% of non-GISTs (p=0.079 and 0.010), with good interobserver agreement (κ = 0.715). The drainage vein diameter correlated well with the maximum diam- eter of the tumor (r = 0.744, p < 0.001). The CT value of the solid tumor part in the arterial and venous phases (p < 0.01) and the CT value of the drainage vein in the arterial phase (p < 0.05) were higher for GISTs than for non-GISTs (p < 0.01).
    Conclusions Strong parenchymal enhancement with the peak in the arterial phase and the CT value of the drainage vein in the arterial phase was characteristics findings of GIST compared with non-GISTs. The diameter of the drainage vein was proportional to the maximum diameter of GISTs.
    Comparison of characteristic computed tomographic findings of gastrointestinal and non‐gastrointestinal stromal tumors in the small intestine
    Inoue A et al.
    Abdominal Radiology (2019) 44:1237–1245
  • Results Intratumoral hemorrhage was seen in 15.4% and 25.6% of GISTs and in 0% and 0% of non-GISTs (p=0.079 and 0.010), with good interobserver agreement (κ = 0.715). The drainage vein diameter correlated well with the maximum diameter of the tumor (r = 0.744, p < 0.001). The CT value of the solid tumor part in the arterial and venous phases (p < 0.01) and the CT value of the drainage vein in the arterial phase (p < 0.05) were higher for GISTs than for non-GISTs (p < 0.01).
    Comparison of characteristic computed tomographic findings of gastrointestinal and non‐gastrointestinal stromal tumors in the small intestine
    Inoue A et al.
    Abdominal Radiology (2019) 44:1237–1245
© 1999-2019 Elliot K. Fishman, MD, FACR. All rights reserved.