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

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

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3D and Workflow

    • “In this pictorial essay, we provide demonstrations of the appearance of a number of renal pathologies as visualized with CR. While this will not be a comprehensive review of all possible renal pathologic conditions, it will serve to demonstrate the potential utility of CR in evaluating the kidney—potential that must still be borne out in prospective studies evaluating the technique.”
      3D CT of renal pathology: initial experience with cinematic rendering
      Steven P. Rowe, Alexa R. Meyer, Michael A. Gorin, Pamela T. Johnson, and Elliot K. Fishman
      Abdom Radiol (2018) 43:3445–3455
    • “VR has demonstrated accuracy in identifying accessary renal arteries and variant renal venous anatomy in the pre- operative setting, and may be of particular value in patients with complex underlying anatomy. While a study outlining the utility of CR for evaluation of renal vascular anatomy has not yet been reported, the highly detailed vascular maps produced by the CR technique may provide similar information to that obtained from VR but with a more photorealistic appearance.”
      3D CT of renal pathology: initial experience with cinematic rendering
      Steven P. Rowe, Alexa R. Meyer, Michael A. Gorin, Pamela T. Johnson, and Elliot K. Fishman
      Abdom Radiol (2018) 43:3445–3455
    • “While older 3D CT methods have previously been successfully applied to identify the cause of obstruction and appropriately guide surgical intervention, the added anatomic detail and realistic shadowing effects of CR may be particularly helpful in preoperative planning prior to treatment of UPJ obstruction.”
      3D CT of renal pathology: initial experience with cinematic rendering
      Steven P. Rowe, Alexa R. Meyer, Michael A. Gorin, Pamela T. Johnson, and Elliot K. Fishman
      Abdom Radiol (2018) 43:3445–3455
    • “Both the arterial and venous vasculature of the kidneys are susceptible to a number of pathologic conditions. On the arterial side, any number of pathologic conditions such as atherosclerosis, dissection, transection, and aneurysms can be encountered in the renal arteries just as they can be in other arterial systems. 3D CT angiography can play a role in the evaluation of all of these entities. In addition, 3D visualizations of volumetric CT data can be utilized to evaluate for fibromuscular dysplasia, an uncommon cause of renovascular hypertension, but one that is amenable to treatment with antihypertensives and/or angioplasty.”
      3D CT of renal pathology: initial experience with cinematic rendering
      Steven P. Rowe, Alexa R. Meyer, Michael A. Gorin, Pamela T. Johnson, and Elliot K. Fishman
      Abdom Radiol (2018) 43:3445–3455
    • “Although CR as a means to visualize fibromuscular dysplasia has not been studied, the highly detailed vascular anatomy that can be displayed with this technique is likely to readily allow for the identification of patients with this condition.”
      3D CT of renal pathology: initial experience with cinematic rendering
      Steven P. Rowe, Alexa R. Meyer, Michael A. Gorin, Pamela T. Johnson, and Elliot K. Fishman
      Abdom Radiol (2018) 43:3445–3455
    • “Potential advantages of CR in this context include improved preoperative planning via better understanding of the relative positions of anatomic objects within the imaged volume and facilitation of patient engagement and education as these images may be more intuitive for those without a formal medical background.”
      3D CT of renal pathology: initial experience with cinematic rendering
      Steven P. Rowe, Alexa R. Meyer, Michael A. Gorin, Pamela T. Johnson, and Elliot K. Fishman
      Abdom Radiol (2018) 43:3445–3455
    • “Additionally, the anatomic detail provided in CR images may allow for the perception of tumoral textures that are otherwise not appreciated, which could lead to more accurate differentiation of benign from malignant tumors without the need for complex statistical textural analysis.”
      3D CT of renal pathology: initial experience with cinematic rendering
      Steven P. Rowe, Alexa R. Meyer, Michael A. Gorin, Pamela T. Johnson, and Elliot K. Fishman
      Abdom Radiol (2018) 43:3445–3455
    • “Similar to traditional 3D methodologies, CR may prove to be a useful adjunct to 2D imaging in the evaluation of the upper urinary tract. Indeed, the ability of CR to accentuate textural features could provide added diagnostic yield for the detection of subtle and/or infiltrative tumors. Again, the ultimate utility of CR in this context will need to be explored in dedicated studies.”
      3D CT of renal pathology: initial experience with cinematic rendering
      Steven P. Rowe, Alexa R. Meyer, Michael A. Gorin, Pamela T. Johnson, and Elliot K. Fishman
      Abdom Radiol (2018) 43:3445–3455
    • “CR allows for a new level of anatomic detail with 3D CT visualization. The role of CR in renal pathology has yet to be thoroughly explored, although the potential suggested by the photorealism of the figures in this review indicates that further study would be warranted. Ultimately, studies that explore the utility of CR in a wide range of conditions and that are backed by extensive surgical and/or pathological correlation are needed to establish the potential diagnostic benefits of this new technique.”
      3D CT of renal pathology: initial experience with cinematic rendering
      Steven P. Rowe, Alexa R. Meyer, Michael A. Gorin, Pamela T. Johnson, and Elliot K. Fishman
      Abdom Radiol (2018) 43:3445–3455
Chest

    • Summary
      A deep learning algorithm was used to assess mammographic breast density at the level of an experienced mammographer during routine clinical practice.
      Implications for Patient Care:
      * A deep learning algorithm was used to reliably and accurately assess mammographic breast density in a large clinical practice.
      * Given the high level of agreement between the deep learning algorithm and experienced mammographers, this algorithm has the potential to standardize and automate routine breast density assessment.
      Mammographic Breast Density Assessment Using Deep Learning: Clinical Implementation
      Lehman CD et al.
      Radiology 2019; 290:52–58
    • “Inconsistency in density assessment of mammograms has been widely recognized for the potential to cause patient anxiety and result in unnecessary procedures. To address this issue, we developed a DL model to assess mammographic breast density that was trained by using the assessments of experienced breast imagers. Our DL model was deployed in the mammography clinic to assess performance and acceptance in a large academic breast imaging practice. In this setting, the DL model density assessment was accepted as the final reading in 90% of mammograms by an experienced breast imager.”
      Mammographic Breast Density Assessment Using Deep Learning: Clinical Implementation
      Lehman CD et al.
      Radiology 2019; 290:52–58
    • “Also, this model was trained on mammograms at one academic center that used mammography units from one vendor (Hologic), and further testing on diverse mammograms acquired with machines from multiple vendors and from different institutions is needed. Finally, during the clinical implementation of our project, acceptance of the DL density assessment was measured in an unblinded manner."
      Mammographic Breast Density Assessment Using Deep Learning: Clinical Implementation
      Lehman CD et al.
      Radiology 2019; 290:52–58
    • In summary, we present an analysis of clinical implementation of a DL model used to assess breast density in women undergoing screening digital mammography. Our DL model provides efficient and reliable density assessments, both at the patient level and at the population level, and it is designed to be widely available, simple to use, and cost effective. It can be used to measure breast density in a diverse set of patients, without limitations based on prior surgery or other breast interventions. Our tool can potentially address concerns for current breast density legislation, and it can help providers supply more accurate information to patients and help health systems optimize the use of supplemental screening resources. To this end, we have made our tool publicly available for research use at http://learningtocure. csail.mit.edu.
      Mammographic Breast Density Assessment Using Deep Learning: Clinical Implementation
      Lehman CD et al.
      Radiology 2019; 290:52–58
    • “Our tool can potentially address concerns for current breast density legislation, and it can help providers supply more accurate information to patients and help health systems optimize the use of supplemental screening resources. To this end, we have made our tool publicly available for research use at http://learningtocure. csail.mit.edu.
      Mammographic Breast Density Assessment Using Deep Learning: Clinical Implementation
      Lehman CD et al.
      Radiology 2019; 290:52–58
Deep Learning

    • “Moreover, radiomics-predicted lymph node metastasis emerged as a preoperative predictor of both disease-specific survival and recurrence-free survival after curative intent resection of biliary tract cancers (hazard ratios, 3.37 and 1.98, respectively). Overall, there was important personalized information for medical decision support.”
      CT-based Radiomics for Biliary Tract Cancer: A Possible Solution for Predicting Lymph Node Metastases
      Laghi A, Voena C
      Radiology 2019; 290:99–100
    • There are limitations. Although the model was built with rigorous methodologic structure, a multicentric study collecting a larger number of patients would be necessary to check for the generalizability of the radiomics signature. The influence of different CT parameters (eg, kilovolt, milliampere-seconds, and reconstruction filters) on extraction of radiomics features was not among the objectives of this study, although this is a relevant variable that might affect data consistency and limit the extensive use of the model.
      CT-based Radiomics for Biliary Tract Cancer: A Possible Solution for Predicting Lymph Node Metastases
      Laghi A, Voena C
      Radiology 2019; 290:99–100
    • A correlation with genomic profile of biliary tract cancers may have been desirable, especially in the era of target therapy where specific genomic profiles are associated with either response or resistance to a specific drug. Nevertheless, radiomics approaches seem to have a bright future, especially if collaborative multidisciplinary teams are involved. Ultimately, to achieve personalized medicine, personalized imaging must be involved.
      CT-based Radiomics for Biliary Tract Cancer: A Possible Solution for Predicting Lymph Node Metastases
      Laghi A, Voena C
      Radiology 2019; 290:99–100
    • “Ultimately, to achieve personalized medicine, personalized imaging must be involved."
      CT-based Radiomics for Biliary Tract Cancer: A Possible Solution for Predicting Lymph Node Metastases
      Laghi A, Voena C
      Radiology 2019; 290:99–100
    • “The further goal of radiomics analytics is to develop decision support tools, such as predictive models, by incorporating radiomics signature and other morphologic features. Radiomics models providing individualized risk estimation of LN metastasis have been developed and validated in studies focused on esophageal, colorectal, and bladder cancers with good results."
      CT-based Radiomics for Biliary Tract Cancer: A Possible Solution for Predicting Lymph Node Metastases
      Laghi A, Voena C
      Radiology 2019; 290:99–100
    • The use of artificial intelligence, and the deep-learning subtype in particular, has been enabled by the use of labeled big data, along with markedly enhanced computing power and cloud storage, across all sectors. In medicine, this is beginning to have an impact at three levels: for clinicians, predominantly via rapid, accurate image interpretation; for health systems, by improving workflow and the potential for reducing medical errors; and for patients, by enabling them to process their own data to promote health. The current limitations, including bias, privacy and security, and lack of transparency, along with the future directions of these applications will be discussed in this article. Over time, marked improvements in accuracy, productivity, and workflow will likely be actualized, but whether that will be used to improve the patient–doctor relationship or facilitate its erosion remains to be seen.
      High-performance medicine: the convergence of human and artificial intelligence
      Eric J. Topol
      NATURE MEDICINE | VOL 25 | January 2019 | 44–56 |
    • The use of artificial intelligence, and the deep-learning subtype in particular, has been enabled by the use of labeled big data, along with markedly enhanced computing power and cloud storage, across all sectors. In medicine, this is beginning to have an impact at three levels: for clinicians, predominantly via rapid, accurate image interpretation; for health systems, by improving workflow and the potential for reducing medical errors; and for patients, by enabling them to process their own data to promote health. The current limitations, including bias, privacy and security, and lack of transparency, along with the future directions of these applications will be discussed in this article.
      High-performance medicine: the convergence of human and artificial intelligence
      Eric J. Topol
      NATURE MEDICINE | VOL 25 | January 2019 | 44–56 |
    • The second is the generation of data in massive quantities, from sources such as high-resolution medical imaging, biosensors with continuous output of physiologic metrics, genome sequenc- ing, and electronic medical records. The limits on analysis of such data by humans alone have clearly been exceeded, necessitating an increased reliance on machines. Accordingly, at the same time that there is more dependence than ever on humans to provide healthcare, algorithms are desperately needed to help.
      High-performance medicine: the convergence of human and artificial intelligence
      Eric J. Topol
      NATURE MEDICINE | VOL 25 | January 2019 | 44–56 |
    • “Similarly, DNNs have been applied across a wide variety of medical scans, including bone films for fractures and estimation of aging, classification of tuberculosis, and vertebral compression fractures; computed tomography scans for lung nodule, liver masses, pancreatic cancer, and coronary calcium score; brain scans for evidence of hemorrhage, head trauma, and acute referrals; magnetic resonance imaging; echocardiograms; and mammographies. “
      High-performance medicine: the convergence of human and artificial intelligence
      Eric J. Topol
      NATURE MEDICINE | VOL 25 | January 2019 | 44–56 |
    • “Similarly, DNNs have been applied across a wide variety of medical scans, including bone films for fractures and estimation of aging, classification of tuberculosis, and vertebral compression fractures; computed tomography scans for lung nodule, liver masses, pancreatic cancer, and coronary calcium score; brain scans for evidence of hemorrhage, head trauma, and acute referrals; magnetic resonance imaging; echocardiograms; and mammographies.”
      High-performance medicine: the convergence of human and artificial intelligence
      Eric J. Topol
      NATURE MEDICINE | VOL 25 | January 2019 | 44–56 |
    • “Furthermore, the lack of large datasets of carefully annotated images has been limiting across various disciplines in medicine. Ironically, to compensate for this deficiency, generative adversarial networks have been used to synthetically produce large image datasets at high resolution, including mammograms, skin lesions, echocardiograms, and brain and retina scans, that could be used to help train DNNs.”
      High-performance medicine: the convergence of human and artificial intelligence
      Eric J. Topol
      NATURE MEDICINE | VOL 25 | January 2019 | 44–56 |


    • High-performance medicine: the convergence of human and artificial intelligence
      Eric J. Topol
      NATURE MEDICINE | VOL 25 | January 2019 | 44–56 |
    • Summary
      A deep learning algorithm was used to assess mammographic breast density at the level of an experienced mammographer during routine clinical practice.
      Implications for Patient Care:
      * A deep learning algorithm was used to reliably and accurately assess mammographic breast density in a large clinical practice.
      * Given the high level of agreement between the deep learning algorithm and experienced mammographers, this algorithm has the potential to standardize and automate routine breast density assessment.
      Mammographic Breast Density Assessment Using Deep Learning: Clinical Implementation
      Lehman CD et al.
      Radiology 2019; 290:52–58
    • “Inconsistency in density assessment of mammograms has been widely recognized for the potential to cause patient anxiety and result in unnecessary procedures. To address this issue, we developed a DL model to assess mammographic breast density that was trained by using the assessments of experienced breast imagers. Our DL model was deployed in the mammography clinic to assess performance and acceptance in a large academic breast imaging practice. In this setting, the DL model density assessment was accepted as the final reading in 90% of mammograms by an experienced breast imager.”
      Mammographic Breast Density Assessment Using Deep Learning: Clinical Implementation
      Lehman CD et al.
      Radiology 2019; 290:52–58
    • “Also, this model was trained on mammograms at one academic center that used mammography units from one vendor (Hologic), and further testing on diverse mammograms acquired with machines from multiple vendors and from different institutions is needed. Finally, during the clinical implementation of our project, acceptance of the DL density assessment was measured in an unblinded manner."
      Mammographic Breast Density Assessment Using Deep Learning: Clinical Implementation
      Lehman CD et al.
      Radiology 2019; 290:52–58
    • In summary, we present an analysis of clinical implementation of a DL model used to assess breast density in women undergoing screening digital mammography. Our DL model provides efficient and reliable density assessments, both at the patient level and at the population level, and it is designed to be widely available, simple to use, and cost effective. It can be used to measure breast density in a diverse set of patients, without limitations based on prior surgery or other breast interventions. Our tool can potentially address concerns for current breast density legislation, and it can help providers supply more accurate information to patients and help health systems optimize the use of supplemental screening resources. To this end, we have made our tool publicly available for research use at http://learningtocure. csail.mit.edu.
      Mammographic Breast Density Assessment Using Deep Learning: Clinical Implementation
      Lehman CD et al.
      Radiology 2019; 290:52–58
    • “Our tool can potentially address concerns for current breast density legislation, and it can help providers supply more accurate information to patients and help health systems optimize the use of supplemental screening resources. To this end, we have made our tool publicly available for research use at http://learningtocure. csail.mit.edu.
      Mammographic Breast Density Assessment Using Deep Learning: Clinical Implementation
      Lehman CD et al.
      Radiology 2019; 290:52–58
    • OBJECTIVE. Artificial intelligence (AI) neural networks rapidly convert disparate facts and data into highly predictive analytic models. Machine learning maps image-patient phenotype correlations opaque to standard statistics. Deep learning performs accurate image-derived tissue characterization and can generate virtual CT images from MRI datasets. Natural language processing reads medical literature and efficiently reconfigures years of PACS and electronic medical record information.
      CONCLUSION. AI logistics solve radiology informatics workflow pain points. Imaging professionals and companies will drive health care AI technology insertion. Data science and computer science will jointly potentiate the impact of AI applications for medical imaging.
      How Cognitive Machines Can Augment Medical Imaging
      Miller DD, Brow EW
      AJR 2019; 212:9–14
    • “AI is not a mindless black-box technology passively fixing the world’s data explosion problems; however, under varying degrees of human supervision, superfast computers can process massive datasets through convolu- tional neural networks (CNNs) of layered algorithms to produce predictive models that would defy standard statistical analyses.”
      How Cognitive Machines Can Augment Medical Imaging
      Miller DD, Brow EW
      AJR 2019; 212:9–14
    • "Generative adversarial networks (GANs), first described in 2014, are a computing framework for explaining how deep CNNs can make mistakes in correctly predicting images of objects, speech patterns, and natural language symbols from rich datasets. Successful deep CNNs apply discriminative models that back-propagate derivatives and apply dropout algorithms to estimate the probability that an output sample has been derived from training data.”
      How Cognitive Machines Can Augment Medical Imaging
      Miller DD, Brow EW
      AJR 2019; 212:9–14
    • “A multilayered deep CNN can discriminate pixel depths of 32 bits, far exceeding the typical human visual resolution capacity of 8 bits. This allows AI scientists to apply GANs to attack deep CNN layers by modifying the 32-bit pixel information to the point where a computer erroneously perceives a picture of a panda as a gibbon, while humans still clear- ly see a panda. This CNN vulnerability can be exploited for medical applications: GANs can create medical records of patient characteristics to determine new drug efficacy in an uncommon disease phenotype or to derive virtual images from another entirely different digital imaging modality.”
      How Cognitive Machines Can Augment Medical Imaging
      Miller DD, Brow EW
      AJR 2019; 212:9–14
    • "Global imaging system and software companies have access to diverse imaging data repositories. They are actively entering the cognitive marketplace, either alone (e.g., Philips with Illumeo) or in partnership with AI industry leaders (e.g., Agfa with IBM Watson) . Public-private partnerships in the United Kingdom (National Health Service, Cancer Research UK Imperial Centre and OPTIMAM, DeepMind Health, Google) and the United States (University of California San Francisco, Western Digital, NVIDIA) are compiling big digital mammography databases to train AI machines for accurate breast cancer screening.”
      How Cognitive Machines Can Augment Medical Imaging
      Miller DD, Brow EW
      AJR 2019; 212:9–14
    • “The potential applications of AI to the field of medical imaging remain to be fully elucidated because the underlying computing technology continues to rapidly improve and to be tested in the clinical environment. One feature that is unique to this field of computer science and to AI in particular is the propensity for re- searchers from the public and private sectors to orally present and discuss their findings at scientific sessions well in advance of or in lieu of publishing full manuscripts in the peer-reviewed literature. Much of what is typically done create a solid scientific evidence basis for the use of (and reimbursement for) a new medical technology is missing from this AI orthopraxy.”
      How Cognitive Machines Can Augment Medical Imaging
      Miller DD, Brow EW
      AJR 2019; 212:9–14
    • ”Soon, powerful third-wave AI technologies will seamlessly link NLP skills with vision tasks, greatly enhancing human understanding of information and images. Humans informed by intelligent machines will compute novel insights from diverse digital images in big data repositories. At some future uncertain time, data science and AI applications will enhance human under- standing of the veracity of all things digital. Although this augmented future approaches, imperfect humans and machines remain purposefully and necessarily juxtaposed.”
      How Cognitive Machines Can Augment Medical Imaging
      Miller DD, Brow EW
      AJR 2019; 212:9–14
GU Misc

    • Objective: To evaluate whether specific clinical or radiographic factors predict inferior vena cava (IVC) or abdominal aortic (AA) resection or reconstruction (RoR) at the time of postchemotherapy retroperitoneal lymph node dissection (RPLND) for germ cell tumors of the testicle.
      Conclusion: Degree of circumferential involvement of the great vessels is an independent predictor for resection or reconstruction of the IVC or AA at postchemotherapy RPLND. Patients at high risk of great vessel reconstruction should be informed accordingly and have the proper teams available for complex vascular reconstruction.
      Clinical and Radiographic Predictors of Great Vessel Resection or Reconstruction During Retroperitoneal Lymph Node Dissection for Testicular Cancer
      Johnson SC, Smith ZL. Fishman EK et al.
      UROLOGY 123; 186-190, 2019
    • “The clinical factors identified in our study may potentially aid urologists in preoperative identification of patients at high risk for requiring vascular intervention, allowing for preoperative consultation of additional surgical services, through patient counseling, and referral to high-volume centers if appropriate.”
      Clinical and Radiographic Predictors of Great Vessel Resection or Reconstruction During Retroperitoneal Lymph Node Dissection for Testicular Cancer
      Johnson SC, Smith ZL. Fishman EK et al.
      UROLOGY 123; 186-190, 2019
    • "There are important limitations to our study. The deci- sion to perform RoR can be subjective and affected by surgeon experience and comfort level with vascular surgical procedures. That being said, we do think that these objective findings on preoperative imaging may help identify patients at high risk for needing these adjunct vascular procedures. Further, our findings are based on the results of 2 institutions only and due to the low event rate, we were not able to externally validate the model but encourage others to do so.”
      Clinical and Radiographic Predictors of Great Vessel Resection or Reconstruction During Retroperitoneal Lymph Node Dissection for Testicular Cancer
      Johnson SC, Smith ZL. Fishman EK et al.
      UROLOGY 123; 186-190, 2019
    • "The degree of circumferential tumor involvement of the aorta ( > 330°) and IVC ( > 135°) is highly associated with the need for RoR during PC RPLND, irrespective of other clinical or radiographic findings. All patients under- going RPLND should be counseled on possible need for vascular intervention, however patients with these tumor characteristics on preoperative imaging should be considered at high risk and planned for accordingly.”
      Clinical and Radiographic Predictors of Great Vessel Resection or Reconstruction During Retroperitoneal Lymph Node Dissection for Testicular Cancer
      Johnson SC, Smith ZL. Fishman EK et al.
      UROLOGY 123; 186-190, 2019
Kidney


    • Differentiation of pancreatic neuroendocrine tumors from pancreas renal cell carcinoma metastases on CT using qualitative and quantitative features
      van der Pol CB et al.
      Abdominal Radiology 2019 (in press)
    • “PNETs often appear as solid hypervascular neoplasms on arterial or occasionally portal venous phase imaging. Prior work suggests that approximately 22% of PNETs contain calcification, similar to our study. Although MPD dilation is more commonly seen secondary to pancreatic adenocarcinoma rather than PNETs, a minority of PNETs have been found to cause MPD dilation, which may be due to mass effect from the tumor itself or from fibrotic stricture formation secondary to serotonin or related metabolites released by the tumor. In our study, we found that up to one-quarter of PNETs had associated MPD dilation, whereas this finding was not present for any pancreatic RCC metastases.”
      Differentiation of pancreatic neuroendocrine tumors from pancreas renal cell carcinoma metastases on CT using qualitative and quantitative features
      van der Pol CB et al.
      Abdominal Radiology2019 (in press)
    • In conclusion, our study demonstrates that subjective imaging features and quantitative texture analysis may differentiate PNET from pancreatic RCC metastases. Tumor calcification, solitary masses, and MPD dilation were specific features for PNET but lacked sensitivity, whereas the quantitative texture analysis feature entropy improved sensitivity for diagnosis with moderate overall accuracy.”
      Differentiation of pancreatic neuroendocrine tumors from pancreas renal cell carcinoma metastases on CT using qualitative and quantitative features
      van der Pol CB et al.
      Abdominal Radiology2019 (in press)
    • Our results suggest that imaging features at enhanced CT may accurately differentiate between pancreatic RCC metastases and PNET and could potentially obviate the need for his- tological confirmation, especially to confirm the presence of metastatic disease in patients with a history of RCC, although will require confirmation in larger sample sizes.
      Differentiation of pancreatic neuroendocrine tumors from pancreas renal cell carcinoma metastases on CT using qualitative and quantitative features
      van der Pol CB et al.
      Abdominal Radiology2019 (in press)
    • OBJECTIVE. The purpose of this study is to evaluate the potential value of machine learning (ML)–based high-dimensional quantitative CT texture analysis in predicting the mutation status of the gene encoding the protein polybromo-1 (PBRM1) in patients with clear cell renal cell carcinoma (RCC).
      CONCLUSION. ML-based high-dimensional quantitative CT texture analysis might be a feasible and potential method for predicting PBRM1 mutation status in patients with clear cell RCC.
      Radiogenomics in Clear Cell
      Renal Cell Carcinoma: Machine Learning–Based High-Dimensional Quantitative CT Texture Analysis in Predicting PBRM1 Mutation Status
      Kocak B et al.
      AJR 2019; 212:1–9
    • “Although the field of high-dimensional QCT TA is still under development, the literature suggests that QCT TA can be used for characterizing lesions or tumors, predicting staging, nuclear grading, assessing the response to treatment, and predicting survival.”
      Radiogenomics in Clear Cell
      Renal Cell Carcinoma: Machine Learning–Based High-Dimensional Quantitative CT Texture Analysis in Predicting PBRM1 Mutation Status
      Kocak B et al.
      AJR 2019; 212:1–9
    • “First-order texture features were extracted by histogram analysis, specifically; Kurtosis (a measure of histogram flatness), Skewness (a measure of histogram asymmetry), and Entropy (a measure of histogram irregularity) as described previously. Manual contouring of tumors was independently repeated in 17% of patients (10/60) for 17 tumors by a second fellowship-trained abdominal radiologist (NS), to assess reproducibility of segmentation.”
      Differentiation of pancreatic neuroendocrine tumors from pancreas renal cell carcinoma metastases on CT using qualitative and quantitative features
      van der Pol CB et al.
      Abdominal Radiology 2019 (in press)
    • “With respect to texture analysis features studied, entropy was significantly higher in PNETs compared to RCC metastases (6.32 ± 0.49 vs. 5.96 ± 0.53, P = 0.004) with a trend towards higher levels of kurtosis and skewness, although the difference in the latter two features did not reach statistical significance between groups (P = 0.067 and 0.099, respectively).”
      Differentiation of pancreatic neuroendocrine tumors from pancreas renal cell carcinoma metastases on CT using qualitative and quantitative features
      van der Pol CB et al.
      Abdominal Radiology 2019 (in press)
    • “The presence of tumor calcification and main pancreatic duct dilation were specific features for PNETs, whereas pancreatic RCC metastases tended to be smaller and were more frequently multiple. PNETs appeared subjectively and quantitatively more heterogeneous using texture analysis. Our results suggest that enhanced CT imaging features may accurately differentiate between PNET and pancreatic RCC metastases which may potentially obviate the need for histological sampling in select cases.”
      Differentiation of pancreatic neuroendocrine tumors from pancreas renal cell carcinoma metastases on CT using qualitative and quantitative features
      van der Pol CB et al.
      Abdominal Radiology 2019 (in press)
    • “Quantitative CT (QCT) texture analysis (TA) is an image processing method for measuring repetitive pixel or voxel gray-level patterns that may not be perceptible with the human eye. Several texture parameters can be produced by this method, which makes QCT TA high-dimensional.”
      Radiogenomics in Clear Cell
      Renal Cell Carcinoma: Machine Learning–Based High-Dimensional Quantitative CT Texture Analysis in Predicting PBRM1 Mutation Status
      Kocak B et al.
      AJR 2019; 212:1–9
    • The second most commonly identified mutation in clear cell RCC involves the tumor suppressor PBRM1 gene. A recent meta-analysis of 2942 patients from seven studies reported that a mutation in PBRM1 or decreased expression of the gene is associated with poor survival, advanced TNM categories and tumor stage, and a higher Fuhrman nuclear grade in patients with RCC.
      Radiogenomics in Clear Cell
      Renal Cell Carcinoma: Machine Learning–Based High-Dimensional Quantitative CT Texture Analysis in Predicting PBRM1 Mutation Status
      Kocak B et al.
      AJR 2019; 212:1–9
    • “In the present study, we investigated the potential value of ML-based high-dimensional QCT TA in predicting the PBRM1 mutation status of patients with clear cell RCC. The results of our study suggest that high-dimensional QCT TA using different ML classifiers (ANN and RF algorithms) has potential in distinguishing clear cell RCCs with and without the PBRM1 mutation. “
      Radiogenomics in Clear Cell
      Renal Cell Carcinoma: Machine Learning–Based High-Dimensional Quantitative CT Texture Analysis in Predicting PBRM1 Mutation Status
      Kocak B et al.
      AJR 2019; 212:1–9
    • “In conclusion, ML-based high-dimension- al QCT TA is a feasible and potential method for predicting PBRM1 mutation status in patients with clear cell RCC. Nonetheless, more studies with more labeled data are absolutely required for further validation and improvement of the method for clinical use. We hope that the present study will provide the basis for new research.”
      Radiogenomics in Clear Cell
      Renal Cell Carcinoma: Machine Learning–Based High-Dimensional Quantitative CT Texture Analysis in Predicting PBRM1 Mutation Status
      Kocak B et al.
      AJR 2019; 212:1–9
    • “In this pictorial essay, we provide demonstrations of the appearance of a number of renal pathologies as visualized with CR. While this will not be a comprehensive review of all possible renal pathologic conditions, it will serve to demonstrate the potential utility of CR in evaluating the kidney—potential that must still be borne out in prospective studies evaluating the technique.”
      3D CT of renal pathology: initial experience with cinematic rendering
      Steven P. Rowe, Alexa R. Meyer, Michael A. Gorin, Pamela T. Johnson, and Elliot K. Fishman
      Abdom Radiol (2018) 43:3445–3455
    • “VR has demonstrated accuracy in identifying accessary renal arteries and variant renal venous anatomy in the pre- operative setting, and may be of particular value in patients with complex underlying anatomy. While a study outlining the utility of CR for evaluation of renal vascular anatomy has not yet been reported, the highly detailed vascular maps produced by the CR technique may provide similar information to that obtained from VR but with a more photorealistic appearance.”
      3D CT of renal pathology: initial experience with cinematic rendering
      Steven P. Rowe, Alexa R. Meyer, Michael A. Gorin, Pamela T. Johnson, and Elliot K. Fishman
      Abdom Radiol (2018) 43:3445–3455
    • “While older 3D CT methods have previously been successfully applied to identify the cause of obstruction and appropriately guide surgical intervention, the added anatomic detail and realistic shadowing effects of CR may be particularly helpful in preoperative planning prior to treatment of UPJ obstruction.”
      3D CT of renal pathology: initial experience with cinematic rendering
      Steven P. Rowe, Alexa R. Meyer, Michael A. Gorin, Pamela T. Johnson, and Elliot K. Fishman
      Abdom Radiol (2018) 43:3445–3455
    • “Both the arterial and venous vasculature of the kidneys are susceptible to a number of pathologic conditions. On the arterial side, any number of pathologic conditions such as atherosclerosis, dissection, transection, and aneurysms can be encountered in the renal arteries just as they can be in other arterial systems. 3D CT angiography can play a role in the evaluation of all of these entities. In addition, 3D visualizations of volumetric CT data can be utilized to evaluate for fibromuscular dysplasia, an uncommon cause of renovascular hypertension, but one that is amenable to treatment with antihypertensives and/or angioplasty.”
      3D CT of renal pathology: initial experience with cinematic rendering
      Steven P. Rowe, Alexa R. Meyer, Michael A. Gorin, Pamela T. Johnson, and Elliot K. Fishman
      Abdom Radiol (2018) 43:3445–3455
    • “Although CR as a means to visualize fibromuscular dysplasia has not been studied, the highly detailed vascular anatomy that can be displayed with this technique is likely to readily allow for the identification of patients with this condition.”
      3D CT of renal pathology: initial experience with cinematic rendering
      Steven P. Rowe, Alexa R. Meyer, Michael A. Gorin, Pamela T. Johnson, and Elliot K. Fishman
      Abdom Radiol (2018) 43:3445–3455
    • “Potential advantages of CR in this context include improved preoperative planning via better understanding of the relative positions of anatomic objects within the imaged volume and facilitation of patient engagement and education as these images may be more intuitive for those without a formal medical background.”
      3D CT of renal pathology: initial experience with cinematic rendering
      Steven P. Rowe, Alexa R. Meyer, Michael A. Gorin, Pamela T. Johnson, and Elliot K. Fishman
      Abdom Radiol (2018) 43:3445–3455
    • “Additionally, the anatomic detail provided in CR images may allow for the perception of tumoral textures that are otherwise not appreciated, which could lead to more accurate differentiation of benign from malignant tumors without the need for complex statistical textural analysis.”
      3D CT of renal pathology: initial experience with cinematic rendering
      Steven P. Rowe, Alexa R. Meyer, Michael A. Gorin, Pamela T. Johnson, and Elliot K. Fishman
      Abdom Radiol (2018) 43:3445–3455
    • “Similar to traditional 3D methodologies, CR may prove to be a useful adjunct to 2D imaging in the evaluation of the upper urinary tract. Indeed, the ability of CR to accentuate textural features could provide added diagnostic yield for the detection of subtle and/or infiltrative tumors. Again, the ultimate utility of CR in this context will need to be explored in dedicated studies.”
      3D CT of renal pathology: initial experience with cinematic rendering
      Steven P. Rowe, Alexa R. Meyer, Michael A. Gorin, Pamela T. Johnson, and Elliot K. Fishman
      Abdom Radiol (2018) 43:3445–3455
    • “CR allows for a new level of anatomic detail with 3D CT visualization. The role of CR in renal pathology has yet to be thoroughly explored, although the potential suggested by the photorealism of the figures in this review indicates that further study would be warranted. Ultimately, studies that explore the utility of CR in a wide range of conditions and that are backed by extensive surgical and/or pathological correlation are needed to establish the potential diagnostic benefits of this new technique.”
      3D CT of renal pathology: initial experience with cinematic rendering
      Steven P. Rowe, Alexa R. Meyer, Michael A. Gorin, Pamela T. Johnson, and Elliot K. Fishman
      Abdom Radiol (2018) 43:3445–3455
    • Purpose: To assess qualitative and quantitative imaging features on enhanced CT that may differentiate pancreatic neuroendocrine tumors (PNETs) from pancreatic renal cell carcinoma (RCC) metastases.
      Conclusions: Compared to pancreatic RCC metastases, PNETs are larger, more frequently solitary, contain calcification, show MPD dilation, and are subjectively and quantitatively more heterogeneous tumors.
      Differentiation of pancreatic neuroendocrine tumors from pancreas renal cell carcinoma metastases on CT using qualitative and quantitative features
      van der Pol CB et al.
      Abdominal Radiology 2019 (in press)
    • “PNETs were larger than RCC metastases (37 ± 23 mm vs. 26 ± 21 mm, P = 0.038), more frequently solitary (P < 0.001), subjectively more heterogeneous (P = 0.033/0.144, R1/R2), and associated with calcification (P = 0.002/0.004) and MPD dilation (P = 0.025/0.006). Agreement for subjective features was moderate-to-almost perfect (K = 0.4879–0.9481). Quantitative texture analysis showed higher entropy in PNETs (6.32 ± 0.49 versus 5.96 ± 0.53; P = 0.004) with no difference in other features studied (P > 0.05). Entropy had ROC area under the curve for diagnosis of PNET of 0.77 ± 0.06, with optimal sensitivity/specificity of 71.4/79.1%.”
      Differentiation of pancreatic neuroendocrine tumors from pancreas renal cell carcinoma metastases on CT using qualitative and quantitative features
      van der Pol CB et al.
      Abdominal Radiology 2019 (in press)
    • “Renal cell carcinoma (RCC) is among the most common primary malignancies that metastasize to the pancreas and typically appears as a hypervascular pancreatic mass on CT or MRI [. RCC metastases can resemble primary pancreatic neuroendocrine tumors (PNETs), which are also commonly hypervascular masses.”
      Differentiation of pancreatic neuroendocrine tumors from pancreas renal cell carcinoma metastases on CT using qualitative and quantitative features
      van der Pol CB et al.
      Abdominal Radiology 2019 (in press)
    • “They found that the CAIX score has prog- nostic significance in high-risk clear cell RCC with regard to disease-free survival, overall survival, and lymph node involvement. Clear cell RCCs with a CAIX score of 200–300 had improved overall survival and disease-free survival in comparison with clear cell RCCs with a CAIX score of 0–100. Most patients without lymphatic spread had a CAIX score of 200–300, whereas most patients with lymphatic spread had a CAIX score of 0–199.”
      Association of the Gross Appearance of Intratumoral Vascularity at MDCT With the Carbonic Anhydrase IX Score in Clear Cell Renal Cell Carcinoma
      Young JR et al.
      AJR 2018; 211:1254–1258
    • “Clear cell RCCs with higher CAIX scores were more likely to have the gross appearance of intratumoral vascularity at MDCT. The gross appearance of intratumoral vascularity was present in 78% (43/55) of clear cell RCCs with a CAIX score of 200–300, compared with 60% (24/40) of clear cell RCCs with a CAIX score of 101–199 and 30% (3/10) of clear cell RCCs with a CAIX score of 0–100.”
      Association of the Gross Appearance of Intratumoral Vascularity at MDCT With the Carbonic Anhydrase IX Score in Clear Cell Renal Cell Carcinoma
      Young JR et al.
      AJR 2018; 211:1254–1258
    • "To our knowledge, our study is the first proof of concept study to show an association between the gross appearance of intratumoral vascularity on MDCT images and CAIX score at pathologic analysis. Because of the prognostic significance of a CAIX score, a noninvasive means of predicting the CAIX score preoperatively may help guide clinical decision making and patient counseling in the future, if validated prospectively.”
      Association of the Gross Appearance of Intratumoral Vascularity at MDCT With the Carbonic Anhydrase IX Score in Clear Cell Renal Cell Carcinoma
      Young JR et al.
      AJR 2018; 211:1254–1258
    • “Papillary RCCs are rarely both subjectively homogeneous and less than 20 HU at unenhanced CT and less than 30 HU at portal venous or nephrographic phase CT.”
      Prevalence of Low-Attenuation Homogeneous Papillary Renal Cell Carcinoma Mimicking Renal Cysts on CT
      Corwin MT et al.
      AJR 2018; 211:1259–1263
    • “Mean lesion size was 2.8 cm (range, 1.2–11.0 cm). The attenuation range for each CT phase was as follows: unenhanced, 14.7–50.7 HU; corticomedullary, 32.2–99.5 HU; portal venous, 40.8–95.1 HU; nephrographic, 17.9–90.8 HU; and excretory, 18.0–73.0 HU. Two of 114 (1.8%; 95% CI, 0.2–6.5%) RCCs were homogeneous and less than 30 HU on the portal venous or nephrographic phase. One of these RCCs was a solid hypoenhancing mass, and the other was a homogeneous cystic RCC. Of the cases with an unenhanced phase, three of 107 (2.8%; 95% CI, 0.6–8.8%) were both homogeneous and were less than 20 HU in attenuation.
      Prevalence of Low-Attenuation Homogeneous Papillary Renal Cell Carcinoma Mimicking Renal Cysts on CT
      Corwin MT et al.
      AJR 2018; 211:1259–1263
    • “Two of 114 (1.8%; 95% CI, 0.2–6.5%) RCCs were homogeneous and less than 30 HU on the portal venous or nephrographic phase. One of these RCCs was a solid hypoenhancing mass, and the other was a homogeneous cystic RCC. Of the cases with an unenhanced phase, three of 107 (2.8%; 95% CI, 0.6–8.8%) were both homogeneous and were less than 20 HU in attenuation.”
      Prevalence of Low-Attenuation Homogeneous Papillary Renal Cell Carcinoma Mimicking Renal Cysts on CT
      Corwin MT et al.
      AJR 2018; 211:1259–1263
    • The 20-HU threshold for follow-up of incidentally detected renal masses at contrast-enhanced CT has recently been revisited. Most RCCs are heterogeneous, and among the RCCs that are homogeneous, most will have attenuation values above 40 HU on contrast-enhanced images.
      Prevalence of Low-Attenuation Homogeneous Papillary Renal Cell Carcinoma Mimicking Renal Cysts on CT
      Corwin MT et al.
      AJR 2018; 211:1259–1263
    • “An incidental renal mass is considered to be a benign cyst if it is both homogeneous and less than 20 HU and is considered indeterminate if it measures above 20 HU on either unenhanced or contrast-enhanced CT.”
      Prevalence of Low-Attenuation Homogeneous Papillary Renal Cell Carcinoma Mimicking Renal Cysts on CT
      Corwin MT et al.
      AJR 2018; 211:1259–1263
    • “The second important finding of our study is that papillary RCC can both be homogeneous and measure less than 20 HU on unenhanced CT in a small percentage of cases (≈ 3% in this study). Schieda et al. found eight of 96 RCCs to have attenuation values less than 20 HU at unenhanced CT. The eight RCCs with attenuation less than 20 HU were inhomogeneous, and all were clear cell RCCs.”
      Prevalence of Low-Attenuation Homogeneous Papillary Renal Cell Carcinoma Mimicking Renal Cysts on CT
      Corwin MT et al.
      AJR 2018; 211:1259–1263
    • “We suggest that the optimal threshold attenuation to trigger further imaging of homogeneous renal masses may be higher than 20 HU for portal venous phase CT. Factors such as lesion size, lesion location, and patient factors such as age and co-morbidities are important to consider in making the decision to follow a lesion.”
      Prevalence of Low-Attenuation Homogeneous Papillary Renal Cell Carcinoma Mimicking Renal Cysts on CT
      Corwin MT et al.
      AJR 2018; 211:1259–1263
    • “In conclusion, papillary RCCs are rarely (0.2–6.5%) both subjectively homogeneous and low attenuation, defined as measuring less than 20 HU at unenhanced CT or less than 30 HU at portal venous or nephrographic phase CT.”
      Prevalence of Low-Attenuation Homogeneous Papillary Renal Cell Carcinoma Mimicking Renal Cysts on CT
      Corwin MT et al.
      AJR 2018; 211:1259–1263
    • “In clear cell RCCs, the gross appearance of intratumoral vascularity at MDCT was significantly associated with CAIX score, a prognostically significant molecular marker. Current assessment of CAIX score requires pathologic tissue sampling and immuno- histochemical analysis. A noninvasive imaging biomarker that may help predict CAIX score may be of great clinical value.”
      Association of the Gross Appearance of Intratumoral Vascularity at MDCT With the Carbonic Anhydrase IX Score in Clear Cell Renal Cell Carcinoma
      Young JR et al.
      AJR 2018; 211:1254–1258
    • “The most common histologic subtype of RCC within the Vancouver and the recently modified World Health Organization classification is clear cell RCC, accounting for 70–80% of RCCs. Clear cell RCC is associated with the worst prognosis, having a 5-year survival rate of 44–69% and accounting for 94% of cases of metastatic RCC.”
      Association of the Gross Appearance of Intratumoral Vascularity at MDCT With the Carbonic Anhydrase IX Score in Clear Cell Renal Cell Carcinoma
      Young JR et al.
      AJR 2018; 211:1254–1258
    • “Several studies have identified carbonic anhydrase IX (CAIX) as a prognostically significant molecular biomarker of clear cell RCC. Chamie et al. developed the CAIX score, which is calculated by multiplying the positive staining percentage by the staining intensity (on a scale of 1–3), resulting in a score ranging from 0 to 300.”
      Association of the Gross Appearance of Intratumoral Vascularity at MDCT With the Carbonic Anhydrase IX Score in Clear Cell Renal Cell Carcinoma
      Young JR et al.
      AJR 2018; 211:1254–1258
Liver

    • “The hepatic angiosarcoma is a tumour of mesenchymal origin, representing 0.1–2% of all primary tumours of the liver and generally appearing during the sixth or seventh decade of life.”
      Liver Angiosarcoma: Rare tumour associated with a poor prognosis, literature review and case report
      Mauricio Millan et al.
      International Journal of Surgery Case Reports 28 (2016) 165–168
    • “Liver Angiosarcoma may manifest as a single mass with satellite nodules or as a diffuse infiltrative mass throughout the liver tissue due to the atypical proliferation of endothelial cells in the hepatic sinusoids. Creating a high mortality rate due to haemorrhages (secondary to tumour rupture) or acute liver failure with a 2 years survival rate of 3%.”
      Liver Angiosarcoma: Rare tumour associated with a poor prognosis, literature review and case report
      Mauricio Millan et al.
      International Journal of Surgery Case Reports 28 (2016) 165–168
    • “Liver Angiosarcoma is a rare tumour with a poor long-term prognosis due to massive haemorrhages and its high recurrence rate. Hence partial hepatectomy of the affected liver tissue with adjuvant therapy has been recognized to improve patients’ survival when compared to liver transplant.”
      Liver Angiosarcoma: Rare tumour associated with a poor prognosis, literature review and case report
      Mauricio Millan et al.
      International Journal of Surgery Case Reports 28 (2016) 165–168
    • Hepatic Angiosarcoma: E
      - vinyl chloride
      - chronic intake of arsenic
      - anabolic steroids
      - androgens
      - cyclophosphamide
      - oral contraceptives
      - Seventy five percent of the cases are of unknown aetiology
    • “Moreover, radiomics-predicted lymph node metastasis emerged as a preoperative predictor of both disease-specific survival and recurrence-free survival after curative intent resection of biliary tract cancers (hazard ratios, 3.37 and 1.98, respectively). Overall, there was important personalized information for medical decision support.”
      CT-based Radiomics for Biliary Tract Cancer: A Possible Solution for Predicting Lymph Node Metastases
      Laghi A, Voena C
      Radiology 2019; 290:99–100
    • There are limitations. Although the model was built with rigorous methodologic structure, a multicentric study collecting a larger number of patients would be necessary to check for the generalizability of the radiomics signature. The influence of different CT parameters (eg, kilovolt, milliampere-seconds, and reconstruction filters) on extraction of radiomics features was not among the objectives of this study, although this is a relevant variable that might affect data consistency and limit the extensive use of the model.
      CT-based Radiomics for Biliary Tract Cancer: A Possible Solution for Predicting Lymph Node Metastases
      Laghi A, Voena C
      Radiology 2019; 290:99–100
    • A correlation with genomic profile of biliary tract cancers may have been desirable, especially in the era of target therapy where specific genomic profiles are associated with either response or resistance to a specific drug. Nevertheless, radiomics approaches seem to have a bright future, especially if collaborative multidisciplinary teams are involved. Ultimately, to achieve personalized medicine, personalized imaging must be involved.
      CT-based Radiomics for Biliary Tract Cancer: A Possible Solution for Predicting Lymph Node Metastases
      Laghi A, Voena C
      Radiology 2019; 290:99–100
    • “Ultimately, to achieve personalized medicine, personalized imaging must be involved."
      CT-based Radiomics for Biliary Tract Cancer: A Possible Solution for Predicting Lymph Node Metastases
      Laghi A, Voena C
      Radiology 2019; 290:99–100
    • “The further goal of radiomics analytics is to develop decision support tools, such as predictive models, by incorporating radiomics signature and other morphologic features. Radiomics models providing individualized risk estimation of LN metastasis have been developed and validated in studies focused on esophageal, colorectal, and bladder cancers with good results."
      CT-based Radiomics for Biliary Tract Cancer: A Possible Solution for Predicting Lymph Node Metastases
      Laghi A, Voena C
      Radiology 2019; 290:99–100
Pancreas

    • Purpose: To assess qualitative and quantitative imaging features on enhanced CT that may differentiate pancreatic neuroendocrine tumors (PNETs) from pancreatic renal cell carcinoma (RCC) metastases.
      Conclusions: Compared to pancreatic RCC metastases, PNETs are larger, more frequently solitary, contain calcification, show MPD dilation, and are subjectively and quantitatively more heterogeneous tumors.
      Differentiation of pancreatic neuroendocrine tumors from pancreas renal cell carcinoma metastases on CT using qualitative and quantitative features
      van der Pol CB et al.
      Abdominal Radiology 2019 (in press)
    • “PNETs were larger than RCC metastases (37 ± 23 mm vs. 26 ± 21 mm, P = 0.038), more frequently solitary (P < 0.001), subjectively more heterogeneous (P = 0.033/0.144, R1/R2), and associated with calcification (P = 0.002/0.004) and MPD dilation (P = 0.025/0.006). Agreement for subjective features was moderate-to-almost perfect (K = 0.4879–0.9481). Quantitative texture analysis showed higher entropy in PNETs (6.32 ± 0.49 versus 5.96 ± 0.53; P = 0.004) with no difference in other features studied (P > 0.05). Entropy had ROC area under the curve for diagnosis of PNET of 0.77 ± 0.06, with optimal sensitivity/specificity of 71.4/79.1%.”
      Differentiation of pancreatic neuroendocrine tumors from pancreas renal cell carcinoma metastases on CT using qualitative and quantitative features
      van der Pol CB et al.
      Abdominal Radiology 2019 (in press)
    • “Renal cell carcinoma (RCC) is among the most common primary malignancies that metastasize to the pancreas and typically appears as a hypervascular pancreatic mass on CT or MRI [. RCC metastases can resemble primary pancreatic neuroendocrine tumors (PNETs), which are also commonly hypervascular masses.”
      Differentiation of pancreatic neuroendocrine tumors from pancreas renal cell carcinoma metastases on CT using qualitative and quantitative features
      van der Pol CB et al.
      Abdominal Radiology 2019 (in press)
    • “First-order texture features were extracted by histogram analysis, specifically; Kurtosis (a measure of histogram flatness), Skewness (a measure of histogram asymmetry), and Entropy (a measure of histogram irregularity) as described previously. Manual contouring of tumors was independently repeated in 17% of patients (10/60) for 17 tumors by a second fellowship-trained abdominal radiologist (NS), to assess reproducibility of segmentation.”
      Differentiation of pancreatic neuroendocrine tumors from pancreas renal cell carcinoma metastases on CT using qualitative and quantitative features
      van der Pol CB et al.
      Abdominal Radiology 2019 (in press)
    • “With respect to texture analysis features studied, entropy was significantly higher in PNETs compared to RCC metastases (6.32 ± 0.49 vs. 5.96 ± 0.53, P = 0.004) with a trend towards higher levels of kurtosis and skewness, although the difference in the latter two features did not reach statistical significance between groups (P = 0.067 and 0.099, respectively).”
      Differentiation of pancreatic neuroendocrine tumors from pancreas renal cell carcinoma metastases on CT using qualitative and quantitative features
      van der Pol CB et al.
      Abdominal Radiology 2019 (in press)
    • “The presence of tumor calcification and main pancreatic duct dilation were specific features for PNETs, whereas pancreatic RCC metastases tended to be smaller and were more frequently multiple. PNETs appeared subjectively and quantitatively more heterogeneous using texture analysis. Our results suggest that enhanced CT imaging features may accurately differentiate between PNET and pancreatic RCC metastases which may potentially obviate the need for histological sampling in select cases.”
      Differentiation of pancreatic neuroendocrine tumors from pancreas renal cell carcinoma metastases on CT using qualitative and quantitative features
      van der Pol CB et al.
      Abdominal Radiology 2019 (in press)

    • Differentiation of pancreatic neuroendocrine tumors from pancreas renal cell carcinoma metastases on CT using qualitative and quantitative features
      van der Pol CB et al.
      Abdominal Radiology 2019 (in press)
    • “PNETs often appear as solid hypervascular neoplasms on arterial or occasionally portal venous phase imaging. Prior work suggests that approximately 22% of PNETs contain calcification, similar to our study. Although MPD dilation is more commonly seen secondary to pancreatic adenocarcinoma rather than PNETs, a minority of PNETs have been found to cause MPD dilation, which may be due to mass effect from the tumor itself or from fibrotic stricture formation secondary to serotonin or related metabolites released by the tumor. In our study, we found that up to one-quarter of PNETs had associated MPD dilation, whereas this finding was not present for any pancreatic RCC metastases.”
      Differentiation of pancreatic neuroendocrine tumors from pancreas renal cell carcinoma metastases on CT using qualitative and quantitative features
      van der Pol CB et al.
      Abdominal Radiology2019 (in press)
    • In conclusion, our study demonstrates that subjective imaging features and quantitative texture analysis may differentiate PNET from pancreatic RCC metastases. Tumor calcification, solitary masses, and MPD dilation were specific features for PNET but lacked sensitivity, whereas the quantitative texture analysis feature entropy improved sensitivity for diagnosis with moderate overall accuracy.”
      Differentiation of pancreatic neuroendocrine tumors from pancreas renal cell carcinoma metastases on CT using qualitative and quantitative features
      van der Pol CB et al.
      Abdominal Radiology2019 (in press)
    • Our results suggest that imaging features at enhanced CT may accurately differentiate between pancreatic RCC metastases and PNET and could potentially obviate the need for his- tological confirmation, especially to confirm the presence of metastatic disease in patients with a history of RCC, although will require confirmation in larger sample sizes.
      Differentiation of pancreatic neuroendocrine tumors from pancreas renal cell carcinoma metastases on CT using qualitative and quantitative features
      van der Pol CB et al.
      Abdominal Radiology2019 (in press)
Practice Management

    • “The results suggest that the general public perceives hospital affiliation to indicate an equality of care between centers. Nearly 70% of adults surveyed believed the rates of complications, readmissions, length of stay, and mortality at a top-ranked cancer center and an affiliated local hospital would be the same. In addition, more than 50% of respondents believed treatment recommendations and use of minimally invasive surgical approaches would be simi- lar, while 65% believed their cancer was equally likely to be cured at both hospitals.”
      Hospital Regional Network Formation and ‘Brand Sharing’: Appearances May Be Deceiving
      Bradley N. Reames et al.
      Ann Surg Oncol https://doi.org/10.1245/s10434-018-07129-2
    • As a result, the authors found that more than 30% of respondents initially motivated to travel to a top-ranked cancer center could be ‘demotivated’ to instead prefer a local hospital, if an affiliation were present.
      Hospital Regional Network Formation and ‘Brand Sharing’: Appearances May Be Deceiving
      Bradley N. Reames et al
      Ann Surg Oncol 2019 (in press)
    • These findings have important implications for patient outcomes in the current healthcare environment. Recent work by Sheetz et al.10 indicates that hospital network participation is not associated with improvements in out- comes for patients undergoing four common surgical procedures (colectomy, abdominal aortic aneurysm repair, coronary artery bypass grafting, and total hip replacement), even when the amount of time in-network is considered.
      Hospital Regional Network Formation and ‘Brand Sharing’: Appearances May Be Deceiving
      Bradley N. Reames et al
      Ann Surg Oncol 2019 (in press)
    • “While it is important to realize this study did not differentiate large referral (or academic medical) centers and smaller affiliated hospitals, taken together these two studies suggest that ‘brand sharing’ through hospital network affiliation could have unintended negative consequences on patient outcomes.”
      Hospital Regional Network Formation and ‘Brand Sharing’: Appearances May Be Deceiving
      Bradley N. Reames et al
      Ann Surg Oncol 2019 (in press)
    • Previous work has suggested that hospital networks may improve cancer care through appropriate integration of services, including sharing of expertise for decision mak- ing, as well as selective referral of complex cancer patients to the corresponding high-volume tertiary referral centers. The results of the study by Chiu et al. suggest that complex cancer patients who choose to seek care at an affiliated local hospital may unknowingly forgo the benefits of specialized care at a regional referral center, particularly if established processes for integrated care are not followed.
      Hospital Regional Network Formation and ‘Brand Sharing’: Appearances May Be Deceiving
      Bradley N. Reames et al
      Ann Surg Oncol 2019 (in press)
    • A patient with locally advanced pancreatic cancer receiving neoadjuvant therapy, for example, may unknowingly forgo a chance at exploration and resection if he/she pursues a surgical recommendation at an affiliated local hospital not experienced in the preoperative evaluation and perioperative care of combined pancreas and vascular resections, even if the hospital is considered ‘high volume’ for pancreatectomy (>20 annually).14 Similarly, recent studies have suggested that some patients with early- stage breast cancer may be subjected to an unnecessary axillary lymph node dissection, despite good evidence against its use, when receiving care from low-volume breast surgeons.
      Hospital Regional Network Formation and ‘Brand Sharing’: Appearances May Be Deceiving
      Bradley N. Reames et al
      Ann Surg Oncol 2019 (in press)
    • Is the standardization of care and dissemination of best practices improved by regional network formation? Finally, if a significant impact on outcomes is identified, is it distributed equally throughout the network, or are subsets of patients (such as complex cancer patients) affected differently? These are critically important questions, and we applaud Dr. Chiu et al. for conducting an innovative study that begins to advance our understanding of the complex influence of regional hospital networks on modern healthcare.
      Hospital Regional Network Formation and ‘Brand Sharing’: Appearances May Be Deceiving
      Bradley N. Reames et al
      Ann Surg Oncol 2019 (in press)
    • “In their study, the authors evaluated the influence of ‘brand sharing’ on patient perceptions of care at smaller hospitals that are affiliated with a larger specialty hospital through a regional network. Using a web-based survey, they asked a nation- ally representative cohort of adults to hypothetically differentiate elements of surgical quality and safety at a ‘top-ranked’ cancer hospital, an ‘affiliated’ local hospital, and an ‘unaffiliated’ local hospital. In addition, respondents were asked to hypothetically assess their hospital preference for complex cancer care, first in the absence, and then in the presence, of an affiliation with a top-ranked cancer center.”
      Hospital Regional Network Formation and ‘Brand Sharing’: Appearances May Be Deceiving
      Bradley N. Reames et al.
      Ann Surg Oncol https://doi.org/10.1245/s10434-018-07129-2
    • Introduction. Leading cancer hospitals have increasingly shared their ‘brand’ with smaller hospitals through affiliations. Because each brand evokes a distinct reputation for the care provided, ‘brand-sharing’ has the potential to impact the public’s ability to differentiate the safety and quality within hospital networks. The general public was surveyed to determine the perceived similarities and differences in the safety and quality of complex cancer surgery performed at top cancer hospitals and their smaller affiliate hospitals.
      Conclusions. Approximately half of surveyed Americans did not distinguish the quality and safety of surgical care at top-ranked cancer hospitals from their smaller affiliates, potentially decreasing their motivation to travel to top centers for complex surgical care.
      Why Travel for Complex Cancer Surgery? Americans React to ‘Brand-Sharing’ Between Specialty Cancer Hospitals and Their Affiliates
      Alexander S. Chiu et al.
      Ann Surg Oncol 2019 (in press) https:/doi.or/10.1245/s10434-018-6868-9
    • Results. A total of 1010 surveys were completed (58.1% response rate). Overall, 85% of respondents felt ‘motivated’ to travel an hour for complex surgery at a larger hospital specializing in cancer, over a smaller local hospital. However, if the smaller hospital was affiliated with a top-ranked cancer hospital, 31% of the motivated respondents changed their preference to the smaller hospital. When asked to compare leading cancer hospitals and their smaller affiliates, 47% of respondents felt that surgical safety, 66% felt guideline compliance, and 53% felt cure rates would be the same at both hospitals.
      Conclusions. Approximately half of surveyed Americans did not distinguish the quality and safety of surgical care at top-ranked cancer hospitals from their smaller affiliates, potentially decreasing their motivation to travel to top centers for complex surgical care.
      Why Travel for Complex Cancer Surgery? Americans React to ‘Brand-Sharing’ Between Specialty Cancer Hospitals and Their Affiliates
      Alexander S. Chiu et al.
      Ann Surg Oncol 2019 (in press) https:/doi.or/10.1245/s10434-018-6868-9
    • The quality and safety of cancer care varies considerably from hospital to hospital. As a result, a patient’s choice of hospital for cancer care is a major determinate of their outcome. Complex cancer surgery is particularly prone to outcome variability across hospitals, and the risk of dying after an operation can be up to four times greater at hospitals that perform procedures infrequently. Therefore, the factors that influence patient choice for hospitals not only have the potential to influence the economics of cancer care but they may also influence survivorship.
      Why Travel for Complex Cancer Surgery? Americans React to ‘Brand-Sharing’ Between Specialty Cancer Hospitals and Their Affiliates
      Alexander S. Chiu et al.
      Ann Surg Oncol 2019 (in press) https:/doi.or/10.1245/s10434-018-6868-9
    • When asked to consider the safety of complex cancer surgery at a top-ranked hospital compared with one of their smaller, affiliated hospitals, 67.5% of respondents believed the rate of complications would be the same, 68.3% thought the readmission rate would be the same, 70.7% believed they would have the same length of stay, and 69.3% believed they would have the same postoperative mortality rate. Of those who identified a safety difference, the vast majority believed the larger, top-ranked hospital was superior. Overall, 46.8% of respondents believed that the surgical care at a top-ranked hospital and its affiliates would be the same across all four safety outcomes presented.
      Why Travel for Complex Cancer Surgery? Americans React to ‘Brand-Sharing’ Between Specialty Cancer Hospitals and Their Affiliates
      Alexander S. Chiu et al.
      Ann Surg Oncol 2019 (in press) https:/doi.or/10.1245/s10434-018-6868-9
    • When given the option between a smaller hospital without affiliations and a larger, top-ranked cancer hospital 1 h further away, 84.7% (n = 859) of respondents were ‘motivated’ to travel to a top-ranked hospital for complex surgical care. However, when given the option of a smaller local hospital that was affiliated with the top-ranked hospital, 31.4% of respondents (n = 273) who were originally motivated to travel, changed their preference in favor of being cared for at the smaller, local hospital. These respondents appeared to have been ‘demotivated’ to travel as a result of the affiliation. In general, those who were demotivated were significantly more likely to believe the quality and safety at the affiliated hospital as equivalent to the top hospital compared with those who remained motivated.
      Why Travel for Complex Cancer Surgery? Americans React to ‘Brand-Sharing’ Between Specialty Cancer Hospitals and Their Affiliates
      Alexander S. Chiu et al.
      Ann Surg Oncol 2019 (in press) https:/doi.or/10.1245/s10434-018-6868-9
    • “A recent evaluation of the impact of hospital network associations on surgical outcomes reported that hospitals that join networks do not demonstrate improvements in quality and in fact possess outcomes equivalent to non-network hospitals; however, the impact of direct hospital affiliations remains unknown.”
      Why Travel for Complex Cancer Surgery? Americans React to ‘Brand-Sharing’ Between Specialty Cancer Hospitals and Their Affiliates
      Alexander S. Chiu et al.
      Ann Surg Oncol 2019 (in press) https:/doi.or/10.1245/s10434-018-6868-9
    • “Respondents most likely to become ‘demotivated’ from traveling to a top-ranked hospital when given the option of utilizing a local, affiliated hospital were those with high levels of education and residing in urban areas. Prior evi- dence has shown that well-educated populations are the hospital affiliations remains unknown. most aware of hospital ranking status, and as a consequence are more likely to be influenced by this characteristic.”
      Why Travel for Complex Cancer Surgery? Americans React to ‘Brand-Sharing’ Between Specialty Cancer Hospitals and Their Affiliates
      Alexander S. Chiu et al.
      Ann Surg Oncol 2019 (in press) https:/doi.or/10.1245/s10434-018-6868-9
    • “Approximately half of respondents in a nationally representative survey believed the quality and safety of surgery to be equivalent at top-ranked cancer hospitals and the smaller hospitals with which they affiliate. Furthermore, nearly one-third of the population who were initially motivated to travel to a specialty hospital could be ‘de-motivated’ to travel if their local hospital developed an affiliation with a top-ranked cancer center. Further study to better understand the impact of brand-sharing on patient choice for complex cancer care is indicated.”
      Why Travel for Complex Cancer Surgery? Americans React to ‘Brand-Sharing’ Between Specialty Cancer Hospitals and Their Affiliates
      Alexander S. Chiu et al.
      Ann Surg Oncol 2019 (in press) https:/doi.or/10.1245/s10434-018-6868-9

    • Why Travel for Complex Cancer Surgery? Americans React to ‘Brand-Sharing’ Between Specialty Cancer Hospitals and Their Affiliates
      Alexander S. Chiu et al.
      Ann Surg Oncol 2019 (in press) https:/doi.or/10.1245/s10434-018-6868-9
Vascular

    • “Superior Mesenteric Artery Aneurysms (SMAAs) SMAAs account for 5.5% of all visceral artery aneurysms. They most commonly present with abdominal pain and GI bleeding. Aneurysms commonly affect the proximal 5 cm of the artery. SMAAs occur more commonly in men and have a rupture rate of 38% in that cohort. Pseudoaneurysms of the SMA are most often caused by arterial dissection while true aneurysms are most often caused by septic emboli.
      Visceral Artery Aneurysms: Diagnosis, Surveillance, and Treatment
      Fady Ibrahim et al.
      Curr Treat Options Cardio Med (2018) 20: 97
    • “HAA’s comprise 20% of all visceral aneurysms and are often solitary at presentation and occur more commonly in the extra-hepatic arteries. HAA’s carry the highest rupture rate of all splanchnic artery aneurysms (30– 80%) and are associated with a mortality rate of 20%. The uniquely high rupture rate likely reflects, at least in part, the high pseudoaneurysm rate noted in HAA.”
      Visceral Artery Aneurysms: Diagnosis, Surveillance, and Treatment
      Fady Ibrahim et al.
      Curr Treat Options Cardio Med (2018) 20: 97
    • “CAAs accounts for 4% of all visceral artery aneurysms. CAAs are associated with atherosclerosis, medial degeneration, trauma, and in conjunction with spontaneous celiac artery dissection. CAAs are also seen in aortic dissection, Takayasu’s arteritis, syphilis, and with peripheral artery aneurysms. When symptoms are present, patients note epigastric pain or dysphagia, which is due to esophageal compression.”
      Visceral Artery Aneurysms: Diagnosis, Surveillance, and Treatment
      Fady Ibrahim et al.
      Curr Treat Options Cardio Med (2018) 20: 97
    • Splenic Artery Aneurysm
      Splenic artery aneurysms, defined as a dilation of greater than 1 cm in diameter, are the most common type of VAAs. Over 80% of patients with splenic aneurysms are asymptomatic. The small subset of symptomatic individuals typically present with epigastric pain, nausea, vomiting, or anorexia. Spontaneous rupture is the presenting complication in up to 10% of all SAAs, but the prevalence increases to 28% in giant SAA (more than 10 cm in size).
    • Splenic Artery Pseudoaneurysms: Causes
      - pancreatitis,
      - blunt abdominal trauma
      - gastric ulcers
      - iatrogenic injury to the splenic artery
    • Splenic Artery Aneurysms: Causes
      - atherosclerosis,
      - fibromuscular dysplasia
      - vasculitis
      - cirrhosis, and portal hypertension
      - systemic hypertension
      - atherosclerosis
    • True aneurysm vs. pseudoaneurysms
      - A true aneurysm is characterized by dilation and thinning of the entirety of the vessel wall [11] greater than 1.5 times the expected vessel diameter and includes all three tunicae of the artery.
      - Pseudoaneurysms are typically the result of a traumatic disruption of the intimal and medial layers of the vessel with the aneurysm contained by the adventitia and/or perivascular tissues only.
    • True aneurysm vs. pseudoaneurysms
      Distinguishing between the two true aneurysms and pseudoaneurysms is important as the clinical course and complication rates differ significantly between them. In particular, the rate of rupture in pseudoaneuryms has been reported to be much higher than true aneurysms.
    • Normal Artery Size
      - Celiac trunk, 0.79 ± 0.06 cm
      - Common hepatic artery, 0.50 ± 0.04 cm
      - Proper hepatic artery, 0.45 ± 0.03 cm
      - Splenic artery, 0.46 ± 0.03 cm
    • Visceral Artery Aneurysms: Facts
      Splenic artery aneurysms are the most common VAAs, representing 60–70% of all VAAs. Significantly less common are hepatic artery aneurysms and celiac/ mesenteric artery aneurysms, which comprise 20 and 10% of VAAs, respectively [2]. Splenic artery aneurysms are most common among multiparous women, while hepatic and gastroduodenal arteries are more common in men. Celiac and superior mesenteric artery aneurysms have been reported equally among both sexes .
    • Visceral Artery Aneurysms: Facts
      Etiologies for VAAs include atherosclerosis (most common, 32%), vasculitis, collagen vascular disease, infection, fibromuscular dysplasia, trauma (22%), and iatrogenic and idiopathic causes. Multiple aneurysms are found in up to one third of patients and are frequently associated connective tissue or collagen vascular diseases.
    • Visceral Artery Aneurysms: Treatment
      - Symptomatic aneurysms are also at high risk for rupture and should be treated whenever possible.
      - Conservative management is reasonable for most asymptomatic aneurysms under 2cm
      - Aneurysms that are greater than 2 cm usually warrant therapy as studies performed in patients with VAAs indicate that rupture is more likely when aneurysm diameter is greater than 2–2.5 cm .
      - Calcified aneurysms are thought to be an indicator of chronicity and stability. As a result, many practitioners manage calcified aneurysms conservatively especially in patients at increased surgical and procedural risk.
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