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

November 2020 Imaging Pearls - Educational Tools | CT Scanning | CT Imaging | CT Scan Protocols - CTisus
Imaging Pearls ❯ November 2020

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3D and Workflow

  • “Although 3D imaging can be applied to all anatomical regions and used with all imaging techniques, its most varied and rel- evant applications are found with computed tomography (CT) data in musculoskeletal imaging. These new applications include global illumination rendering (GIR), unfolded rib reformations, subtracted CT angiography for bone analysis, dynamic studies, temporal subtraction and image fusion. In all of these tasks, registration and segmentation are two basic processes that affect the quality of the results. GIR simulates the complete interaction of photons with the scanned object, providing photorealistic volume rendering. Reformations to unfold the rib cage allow more accurate and faster diagnosis of rib lesions. Dynamic CT can be applied to cinematic joint evaluations a well as to perfusion and angiographic studies.”
    3D reconstructions, 4D imaging and postprocessing with CT in musculoskeletal disorders: Past, present and future
    A. Blum et al.
    Diagnostic and Interventional Imaging 2020 (in press)
  • "Maximum intensity projection (MIP) is a volume-rendering technique that generates a projection of the volume of interest into a viewing plane by displaying the maximum CT numbers encountered along the projection direction. The thickness of the volume of interest can be modified to include or exclude various objects from the projection. MIP images are used primarily with CT angiography and they usually require bone segmentation to be applied.”
    3D reconstructions, 4D imaging and postprocessing with CT in musculoskeletal disorders: Past, present and future
    A. Blum et al.
    Diagnostic and Interventional Imaging 2020 (in press)
  • "GIR has been used in multiple domains, including the film and design industries, for many years, but it has only relatively recently been adopted in medical imaging. GIR simulates the complete inter- action of photons with the scanned object, providing photorealistic volume rendering. This technique is also known as cinematic rendering (Siemens Healthineers), global illumination rendering (Canon Medical Systems) or volume illumination (GE Healthcare).”
    3D reconstructions, 4D imaging and postprocessing with CT in musculoskeletal disorders: Past, present and future
    A. Blum et al.
    Diagnostic and Interventional Imaging 2020 (in press)
  • “As a result, this technique is able to computes the complex physics of lighting effects in real-time, leading to a natural illumination of the rendered data (in contrast to the synthetic light sources used in VRT). GIR mod- els light propagation, absorption, scattering and eventually color transmission under multiple light sources. As with VRT, the transfer function assigns a color and an opacity property to each voxel. In addition, segmentation processes can also be used.”
    3D reconstructions, 4D imaging and postprocessing with CT in musculoskeletal disorders: Past, present and future
    A. Blum et al.
    Diagnostic and Interventional Imaging 2020 (in press)
Adrenal

  • “Spontaneous adrenal bleeding is a rare clinical event with non-specific clinical features. Life-threatening bleeding in the adrenal glands may be promptly diagnosed with imaging. Computed tomography (CT) is generally the first imaging modality to be used in these patients. However, in the acute phase of bleeding, it may be difficult to detect the underlying mass from the large hematoma. In these patients, additional imaging studies such as magnetic resonance imaging or positron emission tomography/CT may be utilized to rule out a neoplastic mass as the source of bleeding. In patients where an underlying neoplastic mass could not be identified at the time of initial diagnosis, follow-up imaging may be helpful after the acute phase subsides.”
    Can we differentiate neoplastic and non‐neoplastic spontaneous adrenal bleeding? Imaging findings with radiopathologic correlation
    Karaosmanoglu AD et al.
    Abdominal Radiology 2020 (in press)
  • "Acute adrenal bleeding is seen as a mass with heterogenous internal texture in one or both adrenal glands and contrast enhancement is unusual in these pseudomasses. The hemorrhage distorts the shape of the adrenal gland and adreniform shape is typically not preserved. The size of the hematoma is also highly variable and may range from a few centimeters to over 10 cm [3]. The adrenal hematomas characteristically appear as hyperattenuating in the acute phase and measure between 50 and 90 HU. Infiltration in the periadrenal fat tissue is another suggestive finding, which can be appreciated with CT.”
    Can we differentiate neoplastic and non‐neoplastic spontaneous adrenal bleeding? Imaging findings with radiopathologic correlation
    Karaosmanoglu AD et al.
    Abdominal Radiology 2020 (in press)
  • "Anticoagulation, especially heparin, is the most common underlying reason for non-traumatic SAH. Heparin may act in two ways: (1) It may increase the bleeding risk when used in the setting of acute illness, Heparin induced thrombocytopenia (HIT), where heparin platelet factor 4 antibodies are stimulated, may also contribute. As HIT may induce thrombocytopenia and paradoxic thromboembolism, the coagulative occlusion of the central adrenal vein may be the underlying pathophysiology for bleeding. Despite the fact that most reported cases are heparin related, other anticoagulant agents may also cause non-traumatic SAH.”
    Can we differentiate neoplastic and non‐neoplastic spontaneous adrenal bleeding? Imaging findings with radiopathologic correlation
    Karaosmanoglu AD et al.
    Abdominal Radiology 2020 (in press)
  • “Spontaneous adrenal bleeding is a relatively rare clinical event, especially beyond the neonatal age. Imaging studies should be liberally used to search for an underlying mass for early treatment. However, it should be considered that any underlying mass may be obscured in certain cases by the large hematoma and, in these patients, close clinical follow- up with imaging studies is mandatory.”
    Can we differentiate neoplastic and non‐neoplastic spontaneous adrenal bleeding? Imaging findings with radiopathologic correlation
    Karaosmanoglu AD et al.
    Abdominal Radiology 2020 (in press)
  • Adrenal Hemorrhage: Etiology
    - Trauma
    - Anticoagulation
    - Antiphospholipid syndrome
    - Metabolic stress
    - Tumors
    --- Adrenocortical carcinoma
    --- Pheochromocytoma
    --- Metastases
  • Purpose: To investigate the performance of modified criteria to distinguish pheochromocytoma from adrenal adenoma by using adrenal protocol computed tomography (CT).
    Methods: We retrospectively included consecutive 199 patients who underwent adrenal CT and surgically proven pheochromocytoma (n = 66) or adenoma (n = 133). Two independent radiologists analyzed two CT criteria for pheochromocytoma. Conventional criteria were as follows: (a) lesion attenuation on unenhanced CT > 10 Hounsfield unit (HU); (b) absolute percentage washout < 60%; and (c) relative percentage washout < 40%. Modified criteria were as follows: (a) conventional criteria or (b) one of the following findings: (i) lesion attenuation on unenhanced CT ≥ 40 HU, (ii) 1-min enhanced CT ≥ 160 HU, (iii) 15-min enhanced CT ≥ 70 HU, , or (iv) intralesional cystic degeneration seen on both 1-min and 15-min enhanced CT. We analyzed area under the curve (AUC) and inter-reader agreement.
    Distinguishing pheochromocytoma from adrenal adenoma by using modified computed tomography criteria
    Sohi Kang et al
    Abdominal Radiology (2020) https://doi.org/10.1007/s00261-020-02764-4
  • Results: Proportion of pheochromocytoma was 33.2% (66/199). AUC of modified criteria was consistently higher than that of conventional criteria for distinguishing pheochromocytoma from adenoma (reader 1, 0.864 versus 0.746 for raw data set and 0.865 versus 0.746 for internal validation set; reader 2, 0.872 versus 0.758 for raw data set and 0.872 versus 0.757 for internal validation set) (p < 0.05 for all comparisons). Inter-reader agreement was excellent in interpreting any criteria (weighted kappa > 0.800).
    Conclusion: Our modified criteria seem to improve diagnostic performance of adrenal CT in distinguishing pheochromocytoma from adrenal adenoma.
    Distinguishing pheochromocytoma from adrenal adenoma by using modified computed tomography criteria
    Sohi Kang et al
    Abdominal Radiology (2020) https://doi.org/10.1007/s00261-020-02764-4
  • “Thus, our modified criteria for pheochromocytoma (i.e., criteria 2) were as follows: (a) conventional criteria; OR (b) one of following findings: (i) lesion attenuation on UCT ≥ 40 HU, (ii) 1-min CECT ≥ 160 HU, (iii) 15-min CECT ≥ 70 HU, OR (iv) intralesional cystic degeneration seen on both 1-min and 15-min CECT.”
    Distinguishing pheochromocytoma from adrenal adenoma by using modified computed tomography criteria
    Sohi Kang et al
    Abdominal Radiology (2020) https://doi.org/10.1007/s00261-020-02764-4 
Deep Learning

  • OBJECTIVE. The objective of our study was to systematically review the literature about the application of artificial intelligence (AI) to renal mass characterization with a focus on the methodologic quality items.
    MATERIALS AND METHODS. A systematic literature search was conducted using PubMed to identify original research studies about the application of AI to renal mass characterization. Besides baseline study characteristics, a total of 15 methodologic quality items were extracted and evaluated on the basis of the following four main categories: modeling, performance evaluation, clinical utility, and transparency items. The qualitative synthesis was presented using descriptive statistics with an accompanying narrative.
    CONCLUSION. To bring AI-based renal mass characterization from research to practice, future studies need to improve modeling and performance evaluation strategies and pay attention to clinical utility and transparency issues.
    Artificial Intelligence in Renal Mass Characterization: A Systematic Review of Methodologic Items Related to Modeling, Performance Evaluation, Clinical Utility, and Transparency
    Kocak B et al.
    AJR 2020; 215:1113–1122
  • OBJECTIVE. The objective of our study was to systematically review the literature about the application of artificial intelligence (AI) to renal mass characterization with a focus on the methodologic quality items.
    CONCLUSION. To bring AI-based renal mass characterization from research to practice, future studies need to improve modeling and performance evaluation strategies and pay attention to clinical utility and transparency issues.
    Artificial Intelligence in Renal Mass Characterization: A Systematic Review of Methodologic Items Related to Modeling, Performance Evaluation, Clinical Utility, and Transparency
    Kocak B et al.
    AJR 2020; 215:1113–1122
  • “As a broad concept, artificial intelligence (AI) covers a wide variety of machine learning (ML) methods or algorithms that create models without strict rule-based programming beforehand. These algorithms can improve and correct themselves through experience. The goal of AI tools is to predict certain outcomes using multiple variables. In the field of medical imaging, there has been extensive interest in AI tools.”
    Artificial Intelligence in Renal Mass Characterization: A Systematic Review of Methodologic Items Related to Modeling, Performance Evaluation, Clinical Utility, and Transparency
    Kocak B et al.
    AJR 2020; 215:1113–1122
  • "In this study, we systematically reviewed 30 studies about the application of AI to re- nal mass characterization. Our focus was on the methodologic quality items related to modeling, performance evaluation, clinical utility, and transparency. The quality items were favorable for modeling and perfor- mance evaluation categories for most stud- ies. On the other hand, they were poor in terms of clinical utility evaluation and transparency for most studies.”
    Artificial Intelligence in Renal Mass Characterization: A Systematic Review of Methodologic Items Related to Modeling, Performance Evaluation, Clinical Utility, and Transparency
    Kocak B et al.
    AJR 2020; 215:1113–1122
  • Background: The diagnostic performance of CT for pancreatic cancer is interpreter-dependent, and approximately 40% of tumours smaller than 2 cm evade detection. Convolutional neural networks (CNNs) have shown promise in image analysis, but the networks’ potential for pancreatic cancer detection and diagnosis is unclear. We aimed to investigate whether CNN could distinguish individuals with and without pancreatic cancer on CT, compared with radiologist interpretation. Interpretation CNN could accurately distinguish pancreatic cancer on CT, with acceptable generalisability to images of patients from various races and ethnicities. CNN could supplement radiologist interpretation.
    Deep learning to distinguish pancreatic cancer tissue from non-cancerous pancreatic tissue: a retrospective study with cross-racial external validation
    Kao-Lang Liu et al.
    Lancet Digital Health 2020; 2: e303–13
  • “Findings Between Jan 1, 2006, and Dec 31, 2018, we obtained CT images. In local test set 1, CNN-based analysis had a sensitivity of 0·973, specificity of 1·000, and accuracy of 0·986 (area under the curve [AUC] 0·997 (95% CI 0·992–1·000). In local test set 2, CNN-based analysis had a sensitivity of 0·990, specificity of 0·989, and accuracy of 0·989 (AUC 0·999 [0·998–1·000]). In the US test set, CNN-based analysis had a sensitivity of 0·790, specificity of 0·976, and accuracy of 0·832 (AUC 0·920 [0·891–0·948)]. CNN-based analysis achieved higher sensitivity than radiologists did (0·983 vs 0·929, difference 0·054 [95% CI 0·011–0·098]; p=0·014) in the two local test sets combined. CNN missed three (1·7%) of 176 pancreatic cancers (1·1–1·2 cm). Radiologists missed 12 (7%) of 168 pancreatic cancers (1·0–3·3 cm), of which 11 (92%) were correctly classified using CNN. The sensitivity of CNN for tumours smaller than 2 cm was 92·1% in the local test sets and 63·1% in the US test set.”
    Deep learning to distinguish pancreatic cancer tissue from non-cancerous pancreatic tissue: a retrospective study with cross-racial external validation
    Kao-Lang Liu et al.
    Lancet Digital Health 2020; 2: e303–13
  • Findings: CNN-based analysis achieved higher sensitivity than radiologists did (0·983 vs 0·929, difference 0·054 [95% CI 0·011–0·098]; p=0·014) in the two local test sets combined. CNN missed three (1·7%) of 176 pancreatic cancers (1·1–1·2 cm). Radiologists missed 12 (7%) of 168 pancreatic cancers (1·0–3·3 cm), of which 11 (92%) were correctly classified using CNN. The sensitivity of CNN for tumours smaller than 2 cm was 92·1% in the local test sets and 63·1% in the US test set.
    Deep learning to distinguish pancreatic cancer tissue from non-cancerous pancreatic tissue: a retrospective study with cross-racial external validation
    Kao-Lang Liu et al.
    Lancet Digital Health 2020; 2: e303–13
  • “CNN can accurately differentiate pancreatic cancer from non-cancerous pancreas, and with improvements might accommodate variations in patient race and ethnicity and imaging parameters that are inevitable in real-world clinical practice. CNN holds promise for developing computer-aided detection and diagnosis tools for pancreatic cancer to supplement radiologist interpretation.”
    Deep learning to distinguish pancreatic cancer tissue from non-cancerous pancreatic tissue: a retrospective study with cross-racial external validation
    Kao-Lang Liu et al.
    Lancet Digital Health 2020; 2: e303–13
  • “In conclusion, this study provided a proof of concept that CNN can accurately distinguish pancreatic cancer on portal venous CT images. The CNN model holds promise as a compute r­aided diagnostic tool to assist radiologists and clinicians in diagnosing pancreatic cancer.”
    Deep learning to distinguish pancreatic cancer tissue from non-cancerous pancreatic tissue: a retrospective study with cross-racial external validation
    Kao-Lang Liu et al.
    Lancet Digital Health 2020; 2: e303–13
  • “Interpretation CNN could accurately distinguish pancreatic cancer on CT, with acceptable generalisability to images of patients from various races and ethnicities. CNN could supplement radiologist interpretation.”
    Deep learning to distinguish pancreatic cancer tissue from non-cancerous pancreatic tissue: a retrospective study with cross-racial external validation
    Kao-Lang Liu et al.
    Lancet Digital Health 2020; 2: e303–13
  • IMPORTANCE Machine learning (ML) algorithms can identify patients with cancer at risk of short-term mortality to inform treatment and advance care planning. However, no ML mortality risk prediction algorithm has been prospectively validated in oncology or compared with routinely used prognostic indices.
    OBJECTIVE To validate an electronic health record–embedded ML algorithm that generated real-time predictions of 180-day mortality risk in a general oncology cohort
    CONCLUSIONS AND RELEVANCE In this prognostic study, an ML algorithm was feasibly integrated into the electronic health record to generate real-time, accurate predictions of short-term mortality for patients with cancer and outperformed routinely used prognostic indices. This algorithm may be used to inform behavioral interventions and prompt earlier conversations about goals of care and end-of-life preferences among patients with cancer.
    Validation of a Machine Learning Algorithm to Predict 180-Day Mortality for Outpatients With Cancer
    Christopher R. Manz et al.
    JAMA Oncol. doi:10.1001/jamaoncol.2020.4331 Published online September 24, 2020.
  • OBJECTIVE To validate an electronic health record–embedded ML algorithm that generated real-time predictions of 180-day mortality risk in a general oncology cohort
    CONCLUSIONS AND RELEVANCE In this prognostic study, an ML algorithm was feasibly integrated into the electronic health record to generate real-time, accurate predictions of short-term mortality for patients with cancer and outperformed routinely used prognostic indices. This algorithm may be used to inform behavioral interventions and prompt earlier conversations about goals of care and end-of-life preferences among patients with cancer.
    Validation of a Machine Learning Algorithm to Predict 180-Day Mortality for Outpatients With Cancer
    Christopher R. Manz et al.
    JAMA Oncol. doi:10.1001/jamaoncol.2020.4331 online September 24, 2020.
  • OBJECTIVE To validate an electronic health record–embedded ML algorithm that generated real-time predictions of 180-day mortality risk in a general oncology cohort.
    DESIGN, SETTING, AND PARTICIPANTS This prognostic study comprised a prospective cohort of patients with outpatient oncology encounters between March 1, 2019, and April 30, 2019. An ML algorithm, trained on retrospective data from a subset of practices, predicted 180-day mortality risk between 4 and 8 days before a patient’s encounter. Patient encounters took place in 18 medical or gynecologic oncology practices, including 1 tertiary practice and 17 general oncology practices, within a large US academic health care system. Patients aged 18 years or older with outpatient oncology or hematology and oncology encounters were included in the analysis. Patients were excluded if their appointment was scheduled after weekly predictions were generated and if they were only evaluated in benign hematology, palliative care, or rehabilitation practices.
    Validation of a Machine Learning Algorithm to Predict 180-Day Mortality for Outpatients With Cancer
    Christopher R. Manz et al.
    JAMA Oncol. doi:10.1001/jamaoncol.2020.4331 online September 24, 2020.
  • CONCLUSIONS AND RELEVANCE In this prognostic study, an ML algorithm was feasibly integrated into the electronic health record to generate real-time, accurate predictions of short-term mortality for patients with cancer and outperformed routinely used prognostic indices. This algorithm may be used to inform behavioral interventions and prompt earlier conversations about goals of care and end-of-life preferences among patients with cancer.
    Validation of a Machine Learning Algorithm to Predict 180-Day Mortality for Outpatients With Cancer
    Christopher R. Manz et al.
    JAMA Oncol. doi:10.1001/jamaoncol.2020.4331 online September 24, 2020.
  • Key Points
    Question: Can a machine learning algorithm prospectively identify patients with cancer at risk of 180-day mortality?
    Findings: In this prognostic cohort study of 24582 patients seen in oncology practices within a large health care system, a machine learning algorithm integrated into the electronic health record accurately identified the risk of 180-day mortality with good discrimination and positive predictive value of 45.2%. When added to performance status– and comorbidity-based classifiers, the algorithm favorably reclassified patients.
    Meaning: An integrated machine learning algorithm demonstrated good prospective performance compared with traditional prognostic classifiers and may inform clinician and patient decision-making in oncology.
    Validation of a Machine Learning Algorithm to Predict 180-Day Mortality for Outpatients With Cancer
    Christopher R. Manz et al.
    JAMA Oncol. doi:10.1001/jamaoncol.2020.4331 online September 24, 2020.
  • “In summary, research on AI-powered technologies in the medical domain was at early stage in the 1970s. However, associated deep learning algorithms significantly attracted and revolutionized the scientific community with subsequent evolution of research and exponential growth of multidisciplinary publications since that time. Work in this field has impacted radiology as an area of predominant interest and has been led by institutions in the United States, Spain, France, China, and England. The bibliometric study reported herein can provide a broad overview and valuable guidance to help medical researchers gain insights into key points and trace the global trends regarding the status of AI research in medicine, particularly in radiology and other relevant multispecialty areas.”
    Artificial Intelligence in Various Medical Fields With Emphasis on Radiology: Statistical Evaluation of the Literature
    Emre Pakdemirli, Urszula Wegner
    Cureus 12(10): e10961. DOI 10.7759/cureus.10961
  • "Academic literature on AI in medicine had little room for optimism through the late 1970s. However, AI- driven software soon influenced and inspired qualified staff across the globe with subsequent increase of numerous associated publications in the 1990s. Importantly, this positive research trend demonstrates continuous dramatic growth pattern over the recent years.”
    Artificial Intelligence in Various Medical Fields With Emphasis on Radiology: Statistical Evaluation of the Literature
    Emre Pakdemirli, Urszula Wegner
    Cureus 12(10): e10961. DOI 10.7759/cureus.10961

  • Artificial Intelligence in Various Medical Fields With Emphasis on Radiology: Statistical Evaluation of the Literature
    Emre Pakdemirli, Urszula Wegner
    Cureus 12(10): e10961. DOI 10.7759/cureus.10961
Kidney

  • OBJECTIVE. The objective of our study was to systematically review the literature about the application of artificial intelligence (AI) to renal mass characterization with a focus on the methodologic quality items.
    MATERIALS AND METHODS. A systematic literature search was conducted using PubMed to identify original research studies about the application of AI to renal mass characterization. Besides baseline study characteristics, a total of 15 methodologic quality items were extracted and evaluated on the basis of the following four main categories: modeling, performance evaluation, clinical utility, and transparency items. The qualitative synthesis was presented using descriptive statistics with an accompanying narrative.
    CONCLUSION. To bring AI-based renal mass characterization from research to practice, future studies need to improve modeling and performance evaluation strategies and pay attention to clinical utility and transparency issues.
    Artificial Intelligence in Renal Mass Characterization: A Systematic Review of Methodologic Items Related to Modeling, Performance Evaluation, Clinical Utility, and Transparency
    Kocak B et al.
    AJR 2020; 215:1113–1122
  • OBJECTIVE. The objective of our study was to systematically review the literature about the application of artificial intelligence (AI) to renal mass characterization with a focus on the methodologic quality items.
    CONCLUSION. To bring AI-based renal mass characterization from research to practice, future studies need to improve modeling and performance evaluation strategies and pay attention to clinical utility and transparency issues.
    Artificial Intelligence in Renal Mass Characterization: A Systematic Review of Methodologic Items Related to Modeling, Performance Evaluation, Clinical Utility, and Transparency
    Kocak B et al.
    AJR 2020; 215:1113–1122
  • “As a broad concept, artificial intelligence (AI) covers a wide variety of machine learning (ML) methods or algorithms that create models without strict rule-based programming beforehand. These algorithms can improve and correct themselves through experience. The goal of AI tools is to predict certain outcomes using multiple variables. In the field of medical imaging, there has been extensive interest in AI tools.”
    Artificial Intelligence in Renal Mass Characterization: A Systematic Review of Methodologic Items Related to Modeling, Performance Evaluation, Clinical Utility, and Transparency
    Kocak B et al.
    AJR 2020; 215:1113–1122
  • "In this study, we systematically reviewed 30 studies about the application of AI to re- nal mass characterization. Our focus was on the methodologic quality items related to modeling, performance evaluation, clinical utility, and transparency. The quality items were favorable for modeling and perfor- mance evaluation categories for most stud- ies. On the other hand, they were poor in terms of clinical utility evaluation and transparency for most studies.”
    Artificial Intelligence in Renal Mass Characterization: A Systematic Review of Methodologic Items Related to Modeling, Performance Evaluation, Clinical Utility, and Transparency
    Kocak B et al.
    AJR 2020; 215:1113–1122
  • “Embolic disease of the renal arteries is a rare clinical situation. The heart appears to be the most common source of embolism with atrial fibrillation, valvular diseases, and myocardial infarction among the common predisposing situations. Renal arteries are the least commonly affected arterial structure with arterial thrombosis. In a series of 621 patients with peripheral arterial embolism, the renal arteries were affected in only 2% of these patients. Acute flank pain with associated hematuria, nausea, vomiting, and hypertension are the common presenting symptoms. Serum lactate dehydrogenase elevation in the serum is reported to be the most sensitive biomarker for renal infarction.”
    Role of imaging in visceral vascular emergencies. 
    Karaosmanoglu, A.D., Uysal, A., Akata, D. et al. 
    Insights Imaging 11, 112 (2020). https://doi.org/10.1186/s13244-020-00913-3
  • "CT plays an important role in diagnosing this rare acute clinical situation. Proper timing is important as renal arteries are much better appreciated on arterial phase images. Coronal and sagittally reformatted images may be extremely helpful, in addition axial plane images, for detecting the endoluminal filling defects. Venous and nephrogram phase images should also be included in clinically suspected cases to detect associated renal infarcts. The typical appearance of renal infarction on post-contrast CT is single or multiple foci of non-enhancement areas in the corticomedullary region. These infarcted areas are typically wedge-shaped with extension to the renal capsule. In patients with total occlusion of the main renal artery, the whole kidney may appear as a completely non-enhancing organ with only capsular enhancements due to collateral capsular circulation. This capsular enhancement is also known as the “cortical rim” sign.”
    Role of imaging in visceral vascular emergencies.
    Karaosmanoglu, A.D., Uysal, A., Akata, D. et al.
    Insights Imaging 11, 112 (2020). https://doi.org/10.1186/s13244-020-00913-3
  • "Acute renal artery dissections are mostly traumatic in origin. However, despite being rare, spontaneous renal artery dissection (SRAD) has also been reported in the literature. Among the predisposing factors to SRAD, atherosclerosis, intimal fibroplasia, severe hypertension, Marfan syndrome, and Ehlers–Danlos syndrome have been mentioned.”
    Role of imaging in visceral vascular emergencies.
    Karaosmanoglu, A.D., Uysal, A., Akata, D. et al.
    Insights Imaging 11, 112 (2020). https://doi.org/10.1186/s13244-020-00913-3
  • “The majority of the RAAs are detected in asymptomatic patients. Atherosclerosis and fibromuscular dysplasia are the most common underlying reasons for RAA formation. Hereditary intrinsic collagen deficiencies may also be worked up in select patients as potential underlying risk factors. Hypertension was reported to be the most common presenting symptom (90%) which is thought to occur secondary to altered blood flow due to kinking or twisting of the renal artery with subsequent increased renin secretion induced hypertension. Rupture of the aneurysm is the most dramatic presentation which may cause life-threatening internal bleeding with a mortality rate of 10%.”
    Role of imaging in visceral vascular emergencies. 
    Karaosmanoglu, A.D., Uysal, A., Akata, D. et al. 
    Insights Imaging 11, 112 (2020). https://doi.org/10.1186/s13244-020-00913-3
  • "Rupture of the aneurysm is the most dramatic presentation which may cause life-threatening internal bleeding with a mortality rate of 10%. Unruptured large renal artery aneurysms may also cause severe flank pain and may mimic other more common kidney problems such as pyelonephritis or nephrolithiasis. Pregnancy, polyarteritis nodosa, and a history of liver disease are the most commonly encountered risk factors for spontaneous rupture.”
    Role of imaging in visceral vascular emergencies.
    Karaosmanoglu, A.D., Uysal, A., Akata, D. et al.
    Insights Imaging 11, 112 (2020). https://doi.org/10.1186/s13244-020-00913-3
  • "Renal artery pseudoaneurysms (RAPs) arise from arterial injuries and with subsequent loss of vessel wall integrity. Surgical and percutaneous procedures in addition to penetrating trauma and infectious causes are among the common underlying causes of RAP formation. The sac is contained by the media or adventitia of the vessel or by the perivascular tissues. Through the neck of this perfused sac, the pseudoaneurysm is in direct communication with the arterial lumen. Vasculitis may also cause several pseudoaneurysms within the renal parenchyma. In contrast to the rare occurrence of spontaneous rupture in RAAs, RAPs may spontaneously rupture more frequently.”
    Role of imaging in visceral vascular emergencies.
    Karaosmanoglu, A.D., Uysal, A., Akata, D. et al.
    Insights Imaging 11, 112 (2020). https://doi.org/10.1186/s13244-020-00913-3
  • “Renal arteriovenous fistulas (AVFs) are typically caused by penetrating or blunt traumas and iatrogenic procedures such as surgery and open/percutaneous biopsy. After kidney biopsies, the reported rates of renal AVFs are 7.4–11%. Renal AVFs forming after biopsy typically resolve spontaneously but massive life-threatening hematuria may also be detected in certain patients.”
    Role of imaging in visceral vascular emergencies. 
    Karaosmanoglu, A.D., Uysal, A., Akata, D. et al. 
    Insights Imaging 11, 112 (2020). https://doi.org/10.1186/s13244-020-00913-3 
  • “Multilocular cystic nephroma is a rare and benign renal neoplasm with an excellent prognosis. It has a bimodal distribution in children within 2 years of age with a male predominance and in adults over 30 years of age with a female predominance.Most cases are asymptomatic and found incidentally during imaging investigation.”
    Multilocular cystic nephroma: A case report and review of the literature
    Chih-Peng Chang et al.
    Urological Science Volume 25, Issue 4, December 2014, Pages 109-111
  • “On the image study, a multilocular, cystic nephroma appears as a multilocular cyst with multiple septa, using contrast enhancement, and watery fluid inside the cyst. Sometimes, extension into the renal pelvis may be seen, which results in hydronephrosis and hemorrhage.”
    Multilocular cystic nephroma: A case report and review of the literature
    Chih-Peng Chang et al.
    Urological Science Volume 25, Issue 4, December 2014, Pages 109-111
  • “Multilocular cystic renal cell carcinoma cannot reliably be distinguished from cystic nephroma neither by physical examination nor by radiologic evaluation; immunohistochemical staining assay is useful to differentiate between these conditions allowing an accurate diagnosis and proper follow-up.”
    Multilocular Cystic Renal Cell Carcinoma or Cystic Nephroma?
    Adolfo González-Serrano  
    Case Reports in Urology, vol. 2016, Article ID 5304324, 4 pages, 2016.
Musculoskeletal

  • “Although 3D imaging can be applied to all anatomical regions and used with all imaging techniques, its most varied and rel- evant applications are found with computed tomography (CT) data in musculoskeletal imaging. These new applications include global illumination rendering (GIR), unfolded rib reformations, subtracted CT angiography for bone analysis, dynamic studies, temporal subtraction and image fusion. In all of these tasks, registration and segmentation are two basic processes that affect the quality of the results. GIR simulates the complete interaction of photons with the scanned object, providing photorealistic volume rendering. Reformations to unfold the rib cage allow more accurate and faster diagnosis of rib lesions. Dynamic CT can be applied to cinematic joint evaluations a well as to perfusion and angiographic studies.”
    3D reconstructions, 4D imaging and postprocessing with CT in musculoskeletal disorders: Past, present and future
    A. Blum et al.
    Diagnostic and Interventional Imaging 2020 (in press)
  • "Maximum intensity projection (MIP) is a volume-rendering technique that generates a projection of the volume of interest into a viewing plane by displaying the maximum CT numbers encountered along the projection direction. The thickness of the volume of interest can be modified to include or exclude various objects from the projection. MIP images are used primarily with CT angiography and they usually require bone segmentation to be applied.”
    3D reconstructions, 4D imaging and postprocessing with CT in musculoskeletal disorders: Past, present and future
    A. Blum et al.
    Diagnostic and Interventional Imaging 2020 (in press)
  • "GIR has been used in multiple domains, including the film and design industries, for many years, but it has only relatively recently been adopted in medical imaging. GIR simulates the complete inter- action of photons with the scanned object, providing photorealistic volume rendering. This technique is also known as cinematic rendering (Siemens Healthineers), global illumination rendering (Canon Medical Systems) or volume illumination (GE Healthcare).”
    3D reconstructions, 4D imaging and postprocessing with CT in musculoskeletal disorders: Past, present and future
    A. Blum et al.
    Diagnostic and Interventional Imaging 2020 (in press)
  • “As a result, this technique is able to computes the complex physics of lighting effects in real-time, leading to a natural illumination of the rendered data (in contrast to the synthetic light sources used in VRT). GIR mod- els light propagation, absorption, scattering and eventually color transmission under multiple light sources. As with VRT, the transfer function assigns a color and an opacity property to each voxel. In addition, segmentation processes can also be used.”
    3D reconstructions, 4D imaging and postprocessing with CT in musculoskeletal disorders: Past, present and future
    A. Blum et al.
    Diagnostic and Interventional Imaging 2020 (in press)
OB GYN

  • “Not all ovarian metastases are Krukenberg tumors. Krukenberg tumors are the most common subtype of ovarian metastases, and they are histologically characterized by signet ring cell mucinous features. Common primary tumor sites include the stomach or colon. Although often difficult, distinguishing between Krukenberg tumors and primary ovarian malignancy on imaging is important because of management and prognostic implications.”
    Krukenberg Tumors: Update on Imaging and Clinical Features
    Maria Zulfiqar et al.
    AJR 2020; 215:1020–1029
  • "Approximately 10% of ovarian tu- mors are metastatic masses, almost 50% of which are Krukenberg tumors. Nearly 80% of Krukenberg tumors are bilateral. The estimated incidence of Krukenberg tumors is approximately 0.16 tumors per 100,000 population per year.”
    Krukenberg Tumors: Update on Imaging and Clinical Features
    Maria Zulfiqar et al.
    AJR 2020; 215:1020–1029
  • “ Compared with primary ovarian cancers, Krukenberg tumors more often occur in younger women, possibly because the functioning ovary is prone to metastatic disease as a result of the normal rich ovarian blood supply. Premenopausal women have a higher risk for ovarian metastases, with diagnosis of Krukenberg tumors occurring at a median patient age of 48 years (range, 27–65 years)whereas diagnosis of primary ovarian cancer occurs at a median patient age of 63 years (range, 55–64 years).”
    Krukenberg Tumors: Update on Imaging and Clinical Features
    Maria Zulfiqar et al.
    AJR 2020; 215:1020–1029
  • "Signet ring cells tend to arise in tumors that originate in glands, most commonly the gastrointestinal tract, including the stomach, colon, rectum, appendix, small bowel, pancreas, and biliary tract. Other less common sites of primary adenocarcinomas with signet ring cell features that potentially produce Krukenberg tumors include the breast, lung, contralateral ovary, and endometrium. Of note, primary cancers that metastasize to the ovaries without having signet ring cell features, particularly melanoma or lymphoma, therefore are not Krukenberg tumors.”
    Krukenberg Tumors: Update on Imaging and Clinical Features
    Maria Zulfiqar et al.
    AJR 2020; 215:1020–1029

  • Krukenberg Tumors: Update on Imaging and Clinical Features
    Maria Zulfiqar et al.
    AJR 2020; 215:1020–1029
  • "The survival of patients with Krukenberg tumors is very poor (usually less than 2 years) and seems to be associated with the primary tumor site. Frequently, the detection of Krukenberg tumors precedes the diagnosis of the primary tumor, which may be small and asymptomatic and which rarely may even remain undetected for several years after oophorectomy, further complicating the initial diagnosis.”
    Krukenberg Tumors: Update on Imaging and Clinical Features
    Maria Zulfiqar et al.
    AJR 2020; 215:1020–1029
  • "On CT, the ovaries are replaced by bilat- eral pelvic masses that may be solid, mixed solid and cystic, or, less commonly, predominantly cystic. A well-demarcated intratumoral cystic component is often identified. The cyst walls show contrast enhancement, which correlates with compacted epithelial cells on histologic analysis. CT is very useful for establishing the extent of extraovarian involvement, including invasion of the bowel, ureter, urinary bladder, and similar organs, and it also helps evaluate for the presence of any extraovarian primary tumor. On the same CT study, the stomach, colon, appendix, pancreas, and biliary tract should be scrutinized for a mass, and when such a mass is identified, a Krukenberg tumor should be strongly suspected.”
    Krukenberg Tumors: Update on Imaging and Clinical Features
    Maria Zulfiqar et al.
    AJR 2020; 215:1020–1029
  • "Gastric cancer is the most common primary site for Krukenberg tumors. On imaging, metastatic ovarian masses from gastric carcinoma appear more solid, more frequently have dense enhancement of the solid portion, and generally are smaller compared with metastatic ovarian masses from colon cancer.”
    Krukenberg Tumors: Update on Imaging and Clinical Features
    Maria Zulfiqar et al.
    AJR 2020; 215:1020–1029
  • "Pancreatic cancer seems to have more cys- tic Krukenberg metastases than do primary biliary tumors. The metastatic pancreatic Krukenberg tumors often are detected before the primary tumors, and because of their size, they become symptomatic earlier. When such tumors are suspected, dedicated multiphasic CT or MRI may be required for identification of the primary pancreatic neoplasm.”
    Krukenberg Tumors: Update on Imaging and Clinical Features
    Maria Zulfiqar et al.
    AJR 2020; 215:1020–1029
  • “Nearly 10% of ovarian cancers are meta- static from other primary malignancies, including cancer of the stomach, colon, breast, or appendix; melanoma; or lymphoma. Not all ovarian metastases are Krukenberg tumors. Krukenberg tumors are poorly differ- entiated adenocarcinomas with signet ring cell features that have metastasized to one or both ovaries. On imaging, Krukenberg tumor should be suspected when bilateral solid, mixed solid and cystic, or predominantly cystic ovarian masses are seen in the presence of a known or suspected gastrointes- tinal primary malignancy such as gastric or colorectal tumors. Krukenberg tumors should also be considered when tumors show well-demarcated intratumoral cystic foci, particularly if their walls appear to strongly enhance. The prognosis of signet ring cell ovarian metastasis is relatively very poor compared with non–signet ring cell ovarian metastasis or primary ovarian malignancy.”
    Krukenberg Tumors: Update on Imaging and Clinical Features
    Maria Zulfiqar et al.
    AJR 2020; 215:1020–1029
Pancreas

  • Background: The diagnostic performance of CT for pancreatic cancer is interpreter-dependent, and approximately 40% of tumours smaller than 2 cm evade detection. Convolutional neural networks (CNNs) have shown promise in image analysis, but the networks’ potential for pancreatic cancer detection and diagnosis is unclear. We aimed to investigate whether CNN could distinguish individuals with and without pancreatic cancer on CT, compared with radiologist interpretation.
    Interpretation: CNN could accurately distinguish pancreatic cancer on CT, with acceptable generalisability to images of patients from various races and ethnicities. CNN could supplement radiologist interpretation.
    Deep learning to distinguish pancreatic cancer tissue from non-cancerous pancreatic tissue: a retrospective study with cross-racial external validation
    Kao-Lang Liu et al.
    Lancet Digital Health 2020; 2: e303–13
  • “Findings Between Jan 1, 2006, and Dec 31, 2018, we obtained CT images. In local test set 1, CNN-based analysis had a sensitivity of 0·973, specificity of 1·000, and accuracy of 0·986 (area under the curve [AUC] 0·997 (95% CI 0·992–1·000). In local test set 2, CNN-based analysis had a sensitivity of 0·990, specificity of 0·989, and accuracy of 0·989 (AUC 0·999 [0·998–1·000]). In the US test set, CNN-based analysis had a sensitivity of 0·790, specificity of 0·976, and accuracy of 0·832 (AUC 0·920 [0·891–0·948)]. CNN-based analysis achieved higher sensitivity than radiologists did (0·983 vs 0·929, difference 0·054 [95% CI 0·011–0·098]; p=0·014) in the two local test sets combined. CNN missed three (1·7%) of 176 pancreatic cancers (1·1–1·2 cm). Radiologists missed 12 (7%) of 168 pancreatic cancers (1·0–3·3 cm), of which 11 (92%) were correctly classified using CNN. The sensitivity of CNN for tumours smaller than 2 cm was 92·1% in the local test sets and 63·1% in the US test set.”
    Deep learning to distinguish pancreatic cancer tissue from non-cancerous pancreatic tissue: a retrospective study with cross-racial external validation
    Kao-Lang Liu et al.
    Lancet Digital Health 2020; 2: e303–13
  • Findings: CNN-based analysis achieved higher sensitivity than radiologists did (0·983 vs 0·929, difference 0·054 [95% CI 0·011–0·098]; p=0·014) in the two local test sets combined. CNN missed three (1·7%) of 176 pancreatic cancers (1·1–1·2 cm). Radiologists missed 12 (7%) of 168 pancreatic cancers (1·0–3·3 cm), of which 11 (92%) were correctly classified using CNN. The sensitivity of CNN for tumours smaller than 2 cm was 92·1% in the local test sets and 63·1% in the US test set.
    Deep learning to distinguish pancreatic cancer tissue from non-cancerous pancreatic tissue: a retrospective study with cross-racial external validation
    Kao-Lang Liu et al.
    Lancet Digital Health 2020; 2: e303–13
  • “CNN can accurately differentiate pancreatic cancer from non-cancerous pancreas, and with improvements might accommodate variations in patient race and ethnicity and imaging parameters that are inevitable in real-world clinical practice. CNN holds promise for developing computer-aided detection and diagnosis tools for pancreatic cancer to supplement radiologist interpretation.”
    Deep learning to distinguish pancreatic cancer tissue from non-cancerous pancreatic tissue: a retrospective study with cross-racial external validation
    Kao-Lang Liu et al.
    Lancet Digital Health 2020; 2: e303–13
  • “In conclusion, this study provided a proof of concept that CNN can accurately distinguish pancreatic cancer on portal venous CT images. The CNN model holds promise as a compute r­aided diagnostic tool to assist radiologists and clinicians in diagnosing pancreatic cancer.”
    Deep learning to distinguish pancreatic cancer tissue from non-cancerous pancreatic tissue: a retrospective study with cross-racial external validation
    Kao-Lang Liu et al.
    Lancet Digital Health 2020; 2: e303–13
  • “Interpretation CNN could accurately distinguish pancreatic cancer on CT, with acceptable generalisability to images of patients from various races and ethnicities. CNN could supplement radiologist interpretation.”
    Deep learning to distinguish pancreatic cancer tissue from non-cancerous pancreatic tissue: a retrospective study with cross-racial external validation
    Kao-Lang Liu et al.
    Lancet Digital Health 2020; 2: e303–13
Practice Management

  • Background: The diagnostic performance of CT for pancreatic cancer is interpreter-dependent, and approximately 40% of tumours smaller than 2 cm evade detection. Convolutional neural networks (CNNs) have shown promise in image analysis, but the networks’ potential for pancreatic cancer detection and diagnosis is unclear. We aimed to investigate whether CNN could distinguish individuals with and without pancreatic cancer on CT, compared with radiologist interpretation.
    Interpretation: CNN could accurately distinguish pancreatic cancer on CT, with acceptable generalisability to images of patients from various races and ethnicities. CNN could supplement radiologist interpretation.
    Deep learning to distinguish pancreatic cancer tissue from non-cancerous pancreatic tissue: a retrospective study with cross-racial external validation
    Kao-Lang Liu et al.
    Lancet Digital Health 2020; 2: e303–13
  • Findings Between Jan 1, 2006, and Dec 31, 2018, we obtained CT images. In local test set 1, CNN-based analysis had a sensitivity of 0·973, specificity of 1·000, and accuracy of 0·986 (area under the curve [AUC] 0·997 (95% CI 0·992–1·000). In local test set 2, CNN-based analysis had a sensitivity of 0·990, specificity of 0·989, and accuracy of 0·989 (AUC 0·999 [0·998–1·000]). In the US test set, CNN-based analysis had a sensitivity of 0·790, specificity of 0·976, and accuracy of 0·832 (AUC 0·920 [0·891–0·948)]. CNN-based analysis achieved higher sensitivity than radiologists did (0·983 vs 0·929, difference 0·054 [95% CI 0·011–0·098]; p=0·014) in the two local test sets combined. CNN missed three (1·7%) of 176 pancreatic cancers (1·1–1·2 cm). Radiologists missed 12 (7%) of 168 pancreatic cancers (1·0–3·3 cm), of which 11 (92%) were correctly classified using CNN. The sensitivity of CNN for tumours smaller than 2 cm was 92·1% in the local test sets and 63·1% in the US test set.
    Deep learning to distinguish pancreatic cancer tissue from non-cancerous pancreatic tissue: a retrospective study with cross-racial external validation
    Kao-Lang Liu et al.
    Lancet Digital Health 2020; 2: e303–13
  • Findings: CNN-based analysis achieved higher sensitivity than radiologists did (0·983 vs 0·929, difference 0·054 [95% CI 0·011–0·098]; p=0·014) in the two local test sets combined. CNN missed three (1·7%) of 176 pancreatic cancers (1·1–1·2 cm). Radiologists missed 12 (7%) of 168 pancreatic cancers (1·0–3·3 cm), of which 11 (92%) were correctly classified using CNN. The sensitivity of CNN for tumours smaller than 2 cm was 92·1% in the local test sets and 63·1% in the US test set.
    Deep learning to distinguish pancreatic cancer tissue from non-cancerous pancreatic tissue: a retrospective study with cross-racial external validation
    Kao-Lang Liu et al.
    Lancet Digital Health 2020; 2: e303–13
  • “CNN can accurately differentiate pancreatic cancer from non-cancerous pancreas, and with improvements might accommodate variations in patient race and ethnicity and imaging parameters that are inevitable in real-world clinical practice. CNN holds promise for developing computer-aided detection and diagnosis tools for pancreatic cancer to supplement radiologist interpretation.”
    Deep learning to distinguish pancreatic cancer tissue from non-cancerous pancreatic tissue: a retrospective study with cross-racial external validation
    Kao-Lang Liu et al.
    Lancet Digital Health 2020; 2: e303–13
  • “In conclusion, this study provided a proof of concept that CNN can accurately distinguish pancreatic cancer on portal venous CT images. The CNN model holds promise as a computer­aided diagnostic tool to assist radiologists and clinicians in diagnosing pancreatic cancer.”
    Deep learning to distinguish pancreatic cancer tissue from non-cancerous pancreatic tissue: a retrospective study with cross-racial external validation
    Kao-Lang Liu et al.
    Lancet Digital Health 2020; 2: e303–13
  • “The electronic consultation system allowed primary care providers to easily consult with radiologists, was perceived as high value by primary care providers, resulted in altered patient management, and avoided unnecessary imaging tests. We identified follow-up imaging of cystic lesions and imaging workup of pain in patients as opportunities for continuing medical education for primary care providers.”
    Electronic Consultation Between Primary Care Providers and Radiologists
    Walker D et al.
    AJR 2020; 215:929–933
  • “Given that the eConsult platform is aimed at helping PCPs and patients, evaluation of their experience is critical to providing a viable long-term solution for connecting with specialist physicians. In this study, results pertaining to perceived value were over- whelmingly positive, with three-quarters of PCPs rating the value of the service for both themselves and their patients as excel- lent; similar positive results were seen in the study by Shehata et al. Although com- ments from PCPs were exceedingly positive, the negative comment noted earlier suggests that the eConsult platform may not be suit- able for acute care scenarios.”
    Electronic Consultation Between Primary Care Providers and Radiologists
    Walker D et al.
    AJR 2020; 215:929–933
  • “The eConsult platform provides PCPs with easy access to expert opinion by radiologists and promotes collaboration between physicians to improve patient care. As a result of eConsult, patient care was altered and unnecessary imaging tests were avoided, resulting in more efficient resource use. It may be helpful for radiologists to alter their reporting style to include clear follow-up guidelines for incidental findings, and that PCPs may benefit from CME on imaging of cystic lesions and use of imaging in the workup of a patient’s pain."
    Electronic Consultation Between Primary Care Providers and Radiologists
    Walker D et al.
    AJR 2020; 215:929–933
Quotes

Syndromes in CT

  • OBJECTIVE. Multiple endocrine neoplasia (MEN) syndromes are autosomal-dominant genetic disorders that predispose two or more organs of the endocrine system to tumor develop- ment. Although the diagnosis relies on clinical and serologic findings, imaging provides criti- cal information for surgical management with the ultimate goal of complete tumor resection.
    CONCLUSION. This article reviews abdominal neoplasms associated with the various subtypes of MEN syndromes, with a focus on clinical presentation and characteristic imaging features.
    Multiple Endocrine Neoplasia: Spectrum of Abdominal Manifestations
    Davila A et al.
    AJR 2020; 215:885–895
  • “Two major subtypes of MEN are recognized, MEN type 1 (MEN 1) and MEN type 2 (MEN 2), which is further subdivided into MEN type 2A (MEN 2A, or Sipple syndrome), and MEN type 2B (MEN 2B, or Wermer syndrome). Previously, MEN 2B and MEN 3 were interchangeable descriptions; however, the MEN 3 designation is no longer used. MEN 4, a relatively new sub- type, has features of MEN 1 but contains a genetically unrelated mutation.”
    Multiple Endocrine Neoplasia: Spectrum of Abdominal Manifestations
    Davila A et al.
    AJR 2020; 215:885–895
  • “At-risk patients have a greater probability for neoplasm development and should undergo screening. For MEN 1, these include patients with two or more MEN 1–associated tumors, a patient younger than 30 with a single MEN 1–associated tumor, or a patient with a family member diagnosed with MEN 1. For MEN 2, these include patients with medullary thyroid cancer, two or more MEN 2–associated tumors, a single MEN 2–associated tumor in a patient less than 30, a patient with clinical or phenotypic features of MEN 2, or a family member of a patient with a diagnosis of MEN 2.”
    Multiple Endocrine Neoplasia: Spectrum of Abdominal Manifestations
    Davila A et al.
    AJR 2020; 215:885–895
  • "The main components of MEN 1 include parathyroid, pancreatic, and pituitary tumors caused by an autosomal-dominant germ- line mutation involving the tumor suppres- sor gene, MEN1. MEN 1 has a prevalence of 2–20 per 100,000 patients with an approxi- mate incidence in randomly selected autopsy cases of 0.2%.”
    Multiple Endocrine Neoplasia: Spectrum of Abdominal Manifestations
    Davila A et al.
    AJR 2020; 215:885–895
  • "Multiple endocrine neoplasia is a diverse group of syndromes with abdominal manifestations that require correlation with laboratory testing, clinical examination findings, and imaging for diagnosis. Specific imaging protocols are necessary for accurate detection of tumors because MEN syndromes can be silent, with lesions discovered only incidentally. Radiologists thus play an essential role in the early detection and follow-up of patients with the various MEN syndromes.”
    Multiple Endocrine Neoplasia: Spectrum of Abdominal Manifestations
    Davila A et al.
    AJR 2020; 215:885–895
Vascular

  • “Embolic disease of the renal arteries is a rare clinical situation. The heart appears to be the most common source of embolism with atrial fibrillation, valvular diseases, and myocardial infarction among the common predisposing situations. Renal arteries are the least commonly affected arterial structure with arterial thrombosis. In a series of 621 patients with peripheral arterial embolism, the renal arteries were affected in only 2% of these patients. Acute flank pain with associated hematuria, nausea, vomiting, and hypertension are the common presenting symptoms. Serum lactate dehydrogenase elevation in the serum is reported to be the most sensitive biomarker for renal infarction.”
    Role of imaging in visceral vascular emergencies. 
    Karaosmanoglu, A.D., Uysal, A., Akata, D. et al. 
    Insights Imaging 11, 112 (2020). https://doi.org/10.1186/s13244-020-00913-3
  • "CT plays an important role in diagnosing this rare acute clinical situation. Proper timing is important as renal arteries are much better appreciated on arterial phase images. Coronal and sagittally reformatted images may be extremely helpful, in addition axial plane images, for detecting the endoluminal filling defects. Venous and nephrogram phase images should also be included in clinically suspected cases to detect associated renal infarcts. The typical appearance of renal infarction on post-contrast CT is single or multiple foci of non-enhancement areas in the corticomedullary region. These infarcted areas are typically wedge-shaped with extension to the renal capsule. In patients with total occlusion of the main renal artery, the whole kidney may appear as a completely non-enhancing organ with only capsular enhancements due to collateral capsular circulation. This capsular enhancement is also known as the “cortical rim” sign.”
    Role of imaging in visceral vascular emergencies.
    Karaosmanoglu, A.D., Uysal, A., Akata, D. et al.
    Insights Imaging 11, 112 (2020). https://doi.org/10.1186/s13244-020-00913-3
  • "Acute renal artery dissections are mostly traumatic in origin. However, despite being rare, spontaneous renal artery dissection (SRAD) has also been reported in the literature. Among the predisposing factors to SRAD, atherosclerosis, intimal fibroplasia, severe hypertension, Marfan syndrome, and Ehlers–Danlos syndrome have been mentioned.”
    Role of imaging in visceral vascular emergencies.
    Karaosmanoglu, A.D., Uysal, A., Akata, D. et al.
    Insights Imaging 11, 112 (2020). https://doi.org/10.1186/s13244-020-00913-3
  • “The majority of the RAAs are detected in asymptomatic patients. Atherosclerosis and fibromuscular dysplasia are the most common underlying reasons for RAA formation. Hereditary intrinsic collagen deficiencies may also be worked up in select patients as potential underlying risk factors. Hypertension was reported to be the most common presenting symptom (90%) which is thought to occur secondary to altered blood flow due to kinking or twisting of the renal artery with subsequent increased renin secretion induced hypertension. Rupture of the aneurysm is the most dramatic presentation which may cause life-threatening internal bleeding with a mortality rate of 10%.”
    Role of imaging in visceral vascular emergencies. 
    Karaosmanoglu, A.D., Uysal, A., Akata, D. et al. 
    Insights Imaging 11, 112 (2020). https://doi.org/10.1186/s13244-020-00913-3 
  • "Rupture of the aneurysm is the most dramatic presentation which may cause life-threatening internal bleeding with a mortality rate of 10%. Unruptured large renal artery aneurysms may also cause severe flank pain and may mimic other more common kidney problems such as pyelonephritis or nephrolithiasis. Pregnancy, polyarteritis nodosa, and a history of liver disease are the most commonly encountered risk factors for spontaneous rupture.”
    Role of imaging in visceral vascular emergencies.
    Karaosmanoglu, A.D., Uysal, A., Akata, D. et al.
    Insights Imaging 11, 112 (2020). https://doi.org/10.1186/s13244-020-00913-3
  • "Renal artery pseudoaneurysms (RAPs) arise from arterial injuries and with subsequent loss of vessel wall integrity. Surgical and percutaneous procedures in addition to penetrating trauma and infectious causes are among the common underlying causes of RAP formation. The sac is contained by the media or adventitia of the vessel or by the perivascular tissues. Through the neck of this perfused sac, the pseudoaneurysm is in direct communication with the arterial lumen. Vasculitis may also cause several pseudoaneurysms within the renal parenchyma. In contrast to the rare occurrence of spontaneous rupture in RAAs, RAPs may spontaneously rupture more frequently.”
    Role of imaging in visceral vascular emergencies.
    Karaosmanoglu, A.D., Uysal, A., Akata, D. et al.
    Insights Imaging 11, 112 (2020). https://doi.org/10.1186/s13244-020-00913-3
  • “Renal arteriovenous fistulas (AVFs) are typically caused by penetrating or blunt traumas and iatrogenic procedures such as surgery and open/percutaneous biopsy. After kidney biopsies, the reported rates of renal AVFs are 7.4–11%. Renal AVFs forming after biopsy typically resolve spontaneously but massive life-threatening hematuria may also be detected in certain patients.”
    Role of imaging in visceral vascular emergencies. 
    Karaosmanoglu, A.D., Uysal, A., Akata, D. et al. 
    Insights Imaging 11, 112 (2020). https://doi.org/10.1186/s13244-020-00913-3 
© 1999-2020 Elliot K. Fishman, MD, FACR. All rights reserved.