Search
CTisus CT Scanning CTisus CT Scanning CTisus CT Scanning CTisus CT Scanning CTisus CT Scanning CTisus CT Scanning CTisus CT Scanning Ask the Fish

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

February 2018 Imaging Pearls - Learning Modules | CT Scanning | CT Imaging | CT Scan Protocols - CTisus
Imaging Pearls ❯ February 2018

-- OR --

3D and Workflow

    • Great Technology is
      • Transparent
      • Intuitive
      • Intimate
      • Constant
    • Smart Device Progress
      • Remote control
      • Efficiency
      • Interoperability
      • Learning (AI)
    • The Big Three of CES 2018
      • Voice in everything whether it is built in or communicates with other devices
      • Mixed reality (still mixed) with improvements in hardware but the use cases are not quite there
      • Health and Medical is the biggest thing in tech but still has the most hurdles
    • “Medical errors are a leading cause of morbidity and mortality in the medical field and are substantial contributors to medical costs. Radiologists play an integral role in the diagnosis and care of patients and, given that those in this field interpret millions of examinations annually, may therefore contribute to diagnostic errors. Errors can be categorized as a “miss” when a primary or critical finding is not observed or as a “misinterpretation” when errors in interpretation lead to an incorrect diagnosis.”


      Bias in Radiology: The How and Why of Misses and Misinterpretations 
 Busby LP et al.
 RadioGraphics 2018; 38:236–247
    • “Diagnostic errors can be defined as errors that result in incorrect, delayed, or missed diagnoses. In malpractice lawsuits led against radiologists, approximately 75% of lawsuits relate to diagnostic errors, and 38% of money paid in general malpractice lawsuits results from diagnostic errors.”


      Bias in Radiology: The How and Why of Misses and Misinterpretations 
 Busby LP et al.
 RadioGraphics 2018; 38:236–247
    • “Systemic sources of error that can impact cognitive processes should be addressed through institutional measures, including limiting unnecessary interruptions during imaging interpretation and providing radiologists with diagnostic feedback through peer-review programs, quality improvement, and radiologic-pathologic correlation.”


      Bias in Radiology: The How and Why of Misses and Misinterpretations 
 Busby LP et al.
 RadioGraphics 2018; 38:236–247
    • “Satisfaction of search refers to an individual’s decreased vigilance and/or awareness of additional abnormalities after the first abnormality has been identified This is a bias that plagues radiologists. In a study classifying types of radiologic diagnostic errors, 22% were related to satisfaction of search, which was second only to errors classified as underdiagnoses or misses, making this the most common cognitive bias in diagnostic radiology.” 


      Bias in Radiology: The How and Why of Misses and Misinterpretations 
 Busby LP et al.
 RadioGraphics 2018; 38:236–247 

    • “Radiologists can use a systematic approach to ensure all relevant findings are identified, particularly common and commonly missed diagnoses. After completing the primary search and identifying the first finding or responding to the clinical question, one should initiate a secondary search. Related diagnoses and common diagnostic combinations should be kept in the forefront of the search.” 


      Bias in Radiology: The How and Why of Misses and Misinterpretations 
 Busby LP et al.
 RadioGraphics 2018;38:236–247
    • “Prevalent in all medical specialties, hindsight bias is the tendency to retrospectively de-emphasize the difficulty in making the initial diagnosis after it has been confirmed particularly in morbidity and mortality conferences. This bias is also referred to as the “I knew it all along” bias, retrospectoscope bias, or the “how could he/she miss that?” bias. It is distinct from previously described biases due to its retrospective nature. Hindsight bias prevents the realistic assessment of past events, distorts the evaluation of prior decision making, and discounts the scenario under which the decision making occurred. This bias is related to self-serving bias in that individuals are more likely to take credit for their correct decisions and discount their mistakes.” 


      Bias in Radiology: The How and Why of Misses and Misinterpretations 
 Busby LP et al.
 RadioGraphics 2018; 38:236–247
    • “Prevalent in all medical specialties, hindsight bias is the tendency to retrospectively de-emphasize the difficulty in making the initial diagnosis after it has been confirmed particularly in morbidity and mortality conferences. This bias is also referred to as the “I knew it all along” bias, retrospectoscope bias, or the “how could he/she miss that?” bias. It is distinct from previously described biases due to its retrospective nature. Hindsight bias prevents the realistic assessment of past events, distorts the evaluation of prior decision making, and discounts the scenario under which the decision making occurred.” 


      Bias in Radiology: The How and Why of Misses and Misinterpretations 
 Busby LP et al.
 RadioGraphics 2018; 38:236–247
    • Systemic Sources and Solutions
      • Workplace Interruptions
      • Quality Assurance and Peer Review.
      • Radiologic-Pathologic Correlation
    • “As such, interruptions can contribute to all cognitive biases, particularly satisfaction of search and premature closure. Implementing strategies to reduce work flow disruption, such as designating an individual to manage noninterpretive tasks, has been successful in increasing interpretation time and decreasing disruptions.” 


      Bias in Radiology: The How and Why of Misses and Misinterpretations 
 Busby LP et al.
 RadioGraphics 2018; 38:236–247
    • “Creating a peer-review program with a positive culture is essential to correct cognitive bias. Peer-review programs establish an environment where errors are instructive rather than punitive and support an atmosphere of cognitive debiasing, ensuring that hindsight bias does not overwhelm the retrospective analyses. It is important to acknowledge both hits and misses throughout this process, as sharing and discussing positive calls, in addition to missed findings, helps foster a positive environment for analyzing clinical decisions.” 


      Bias in Radiology: The How and Why of Misses and Misinterpretations 
 Busby LP et al.
 RadioGraphics 2018; 38:236–247
    • “Routine radiologic-pathologic correlation is the current standard of care in breast imaging. It has the potential for positive implications on cognitive training for all involved when radiologic diagnoses have pathologic correlates. Radiologic-pathologic correlation gives a diagnostic radiologist accurate feedback on his or her disease detection rates, positive predictive values, and abnormal interpretation rates. This establishes the radiologist’s knowledge of base rates of disease and therefore helps increase awareness and prevention of availability bias.” 


      Bias in Radiology: The How and Why of Misses and Misinterpretations 
 Busby LP et al.
 RadioGraphics 2018; 38:236–247
    • “Cognitive forcing strategies and metacognition can help disrupt and reduce the impact of cognitive bias on decision making and decrease rates of diagnostic error. Systemic sources of error that can impact cognitive processes should be addressed through institutional measures, including limiting unnecessary interruptions during imaging interpretation and providing radiologists with diagnostic feedback through peer-review programs, quality improvement, and radiologic-pathologic correlation. Being aware of the limitations in one’s judgment can lead to more thoughtful deliberation of imaging findings and improve the quality of decision making.” 


      Bias in Radiology: The How and Why of Misses and Misinterpretations 
 Busby LP et al.
 RadioGraphics 2018; 38:236–247
    • “The mean percentage of time spent on data entry was 43% (95% confidence interval, 39%-47%). The mean percentage of time spent in direct contact with patients was 28%. The pooled weighted average time allocations were 44% on data entry, 28% in direct patient care, 12% reviewing test results and records, 13% in discussion with colleagues, and 3% on other activities. Tabulation was made of the number of mouse clicks necessary for several common emergency department charting functions and for selected patient encounters. Total mouse clicks approach 4000 during a busy 10-hour shift.”


      4000 clicks: a productivity analysis of electronic medical records in a community hospital ED.
Hill RG Jr, Sears LM, Melanson SW
 Am J Emerg Med. 2013 Nov;31(11):1591-4.
    • “Emergency department physicians spend significantly more time entering data into electronic medical records than on any other activity, including direct patient care. Improved efficiency in data entry would allow emergency physicians to devote more time to patient care, thus increasing hospital revenue.”


      4000 clicks: a productivity analysis of electronic medical records in a community hospital ED.
Hill RG Jr, Sears LM, Melanson SW
 Am J Emerg Med. 2013 Nov;31(11):1591-4.
    • “The nationwide implementation of electronic medical records (EMRs) resulted in many unanticipated consequences, even as these systems enabled most of a patient’s data to be gathered in one place and made those data readily accessible to clinicians caring for that patient. The redundancy of the notes, the burden of alerts, and the overflowing inbox has led to the “4000 keystroke a day” problem and has contributed to, and perhaps even accelerated, physician reports of symptoms of burnout. Even though the EMR may serve as an efficient administrative business and billing tool, and even as a powerful research warehouse for clinical data, most EMRs serve their front-line users quite poorly. The unanticipated consequences include the loss of important social rituals (between physicians and between physicians and nurses and other health care workers) around the chart rack and in the radiology suite, where all specialties converged to discuss patients.”


      What This Computer Needs Is a Physician: Humanism and Artificial Intelligence
Abraham Verghese, MD1; Nigam H. Shah, MBBS, PhD1; Robert A. Harrington, MD
JAMA (in press) doi:10.1001/jama.2017.19198
    • “The lessons learned with the EMR should serve as a guide as artificial intelligence and machine learning are developed to help process and creatively use the vast amounts of data being generated in the health care system. Outside of medicine, the use of artificial intelligence in predictive policing, bail decisions, and credit scoring has shown that artificial intelligence can actually exaggerate racial and other bias.”


      What This Computer Needs Is a Physician: Humanism and Artificial Intelligence
Abraham Verghese, MD1; Nigam H. Shah, MBBS, PhD1; Robert A. Harrington, MD
JAMA (in press) doi:10.1001/jama.2017.19198
    • “Similar concerns around artificial intelligence predictive models in health care have been discussed: clearly, in the 3-step process of selecting a dataset, creating an appropriate predictive model, and evaluating and refining the model, there is nothing more critical than the data. Bad data (such as from the EMR) can be amplified into worse models. For example, a model might classify patients with a history of asthma who present with pneumonia as having a lower risk of mortality than those with pneumonia alone, not registering the context that this is an artifact of clinicians admitting and treating such patients earlier and more aggressively. Since machine learning presents no human interface and cannot be interrogated, even if its predictions are extraordinarily accurate, some clinicians are likely to view the “black box” with suspicion.”


      What This Computer Needs Is a Physician: Humanism and Artificial Intelligence
Abraham Verghese, MD1; Nigam H. Shah, MBBS, PhD1; Robert A. Harrington, MD
JAMA (in press) doi:10.1001/jama.2017.19198
    • “The missing piece in the dialectic around artificial intelligence and machine learning in health care is understanding the key step of separating prediction from action and recommendation. Such separation of prediction from action and recommendation requires a change in how clinicians think about using models developed using machine learning. In 2001, the statistician Breiman suggested the need to move away from the culture of assuming that models that are not causal and cannot explain the underlying process are useless. Instead, clinicians should seek a partnership in which the machine predicts (at a demonstrably higher accuracy), and the human explains and decides on action.”


      What This Computer Needs Is a Physician: Humanism and Artificial Intelligence
Abraham Verghese, MD1; Nigam H. Shah, MBBS, PhD1; Robert A. Harrington, MD
JAMA (in press) doi:10.1001/jama.2017.19198
    • “The 2 cultures—computer and the physician—must work together. For example, clinicians are biased toward optimistic prediction, often overestimating life expectancy by a factor of 5, while predictive models trained from vast amounts of data do better; using these well-calibrated probability estimates of an outcome, clinicians can then can act appropriately for patients at the highest risk. The lead time a predictive model can offer to allow for an alternative action matters a great deal. Well-calibrated levels of risk for each outcome, and the timely execution of an alternative action, are needed for a model to be useful.”

      
What This Computer Needs Is a Physician: Humanism and Artificial Intelligence
Abraham Verghese, MD1; Nigam H. Shah, MBBS, PhD1; Robert A. Harrington, MD
JAMA (in press) doi:10.1001/jama.2017.19198
    • “Better diagnosis, and diagnostic algorithms providing more accurate differential diagnoses, might reshape the traditional CPC (clinical problem solving) exercise, just as the development of imaging modalities and sophisticated laboratory testing made the autopsy less relevant.”


      What This Computer Needs Is a Physician: Humanism and Artificial Intelligence
Abraham Verghese, MD1; Nigam H. Shah, MBBS, PhD1; Robert A. Harrington, MD
JAMA (in press) doi:10.1001/jama.2017.19198
    • “In the same manner that automated blood pressure measurement and automated blood cell counts freed clinicians from some tasks, artificial intelligence could bring back meaning and purpose in the practice of medicine while providing new levels of efficiency and accuracy. Physicians must proactively guide, oversee, and monitor the adoption of artificial intelligence as a partner in patient care.”


      What This Computer Needs Is a Physician: Humanism and Artificial Intelligence
Abraham Verghese, MD1; Nigam H. Shah, MBBS, PhD1; Robert A. Harrington, MD
JAMA (in press) doi:10.1001/jama.2017.19198
    • “In the care of the sick, there is a key function played by physicians, referred to by Tinsley Harrison as the “priestly function of the physician.” Human intelligence working with artificial intelligence—a well-informed, empathetic clinician armed with good predictive tools and unburdened from clerical drudgery—can bring physicians closer to fulfilling Peabody’s maxim that the secret of care is in “caring for the patient.”


      What This Computer Needs Is a Physician: Humanism and Artificial Intelligence
Abraham Verghese, MD1; Nigam H. Shah, MBBS, PhD1; Robert A. Harrington, MD
JAMA (in press) doi:10.1001/jama.2017.19198
    • “It is likely that machine learning applications will soon transform some sectors of health care in ways that may be valuable but may have unintended consequences. Use of ML-DSS could create problems in contemporary medicine and lead to misuse. The quality of any ML-DSS and subsequent regulatory decisions about its adoption should not be grounded only in performance metrics, but rather should be subject to proof of clinically important improvements in relevant outcomes compared with usual care, along with the satisfaction of patients and physicians.”


      Unintended Consequences of Machine Learning in Medicine.
Cabitza F, Rasoini R, Gensini GF
JAMA. 2017 Aug 8;318(6):517-518
    • “In the setting of a challenge competition, some deep learning algorithms achieved better diagnostic performance than a panel of 11 pathologists participating in a simulation exercise designed to mimic routine pathology workflow; algorithm performance was comparable with an expert pathologist interpreting whole-slide images without time constraints. Whether this approach has clinical utility will require evaluation in a clinical setting.”


      Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer
Bejnordi BE et al.
 JAMA. 2017;318(22):2199–2210
    • Question
      What is the discriminative accuracy of deep learning algorithms compared with the diagnoses of pathologists in detecting lymph node metastases in tissue sections of women with breast cancer?

      
Finding
      In cross-sectional analyses that evaluated 32 algorithms submitted as part of a challenge competition, 7 deep learning algorithms showed greater discrimination than a panel of 11 pathologists in a simulated time-constrained diagnostic setting, with an area under the curve of 0.994 (best algorithm) vs 0.884 (best pathologist).


      Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer
Bejnordi BE et al.
 JAMA. 2017;318(22):2199–2210
    • “Radiology, having converted to digital images more than 25 years ago, is well-positioned to deploy AI for diagnostics. Several studies have shown considerable opportunity to sup- port radiologists in evaluating a variety of scan types including mammography for breast lesions, computed tomographic scans for pulmonary nodules and infections, and magnetic resonance images for brain tumors including the molecular classification of brain tumors.”


      Deep Learning Algorithms for Detection of Lymph Node Metastases From Breast Cancer: Helping Artificial Intelligence Be Seen. 
Golden JA
JAMA. 2017;318(22):2184–2186
    • “Another challenge to deploying digital pathology was recently addressed. In April 2017, Philips received US Food and Drug Administration clearance for its Philips IntelliSite Pathology Solution to be used for primary pathology diagnostics. This device is used for scanning glass pathology slides and for reviewing these slides on computer monitors. Furthermore, the Philips IntelliSite Pathology Solution has been established as a predicate device that could pave the way for a host of other whole-slide scanners available today to use a 510(k) process for approval rather than a premarket analysis. Many new Food and Drug Administration–approved scanners for primary diagnosis are expected to become avail- able in the coming years.”


      Deep Learning Algorithms for Detection of Lymph Node Metastases From Breast Cancer: Helping Artificial Intelligence Be Seen. 
Golden JA
JAMA. 2017;318(22):2184–2186
    • “Even though some reimbursement codes exist for computational analyses, they are not widely used and often are rejected. With national health care reimbursement trends moving to quality and safety metrics for value-based care rather than fee for service, the recognition of AI as part of reimbursement strategies that reward value-based care would provide important incentives to develop and implement validated algorithms.”

      
Deep Learning Algorithms for Detection of Lymph Node Metastases From Breast Cancer: Helping Artificial Intelligence Be Seen. 
Golden JA
JAMA. 2017;318(22):2184–2186
    • “AI may be just what pathology has been waiting for. While still requiring evaluation within a normal surgical pathology workflow, deep learning has the opportunity to assist pathologists by improving the efficiency of their work, standardizing quality, and providing better prognostication. Like electron microscopy, immunohistochemistry, and molecular diagnostics ahead of AI, there is little risk of pathologists being replaced. Although their workflow is likely to change, the contributions of pathologists to patient care will continue to be critically important.”


      Deep Learning Algorithms for Detection of Lymph Node Metastases From Breast Cancer: Helping Artificial Intelligence Be Seen. 
Golden JA
JAMA. 2017;318(22):2184–2186
    • “A prudent attitude toward research on unintended consequences could help reduce the odds of negative consequences. Moreover, if such consequences occur despite these efforts, research could help manage and reduce the related effects of these consequences.”


      Unintended Consequences of Machine Learning in Medicine.
Cabitza F, Rasoini R, Gensini GF
JAMA. 2017 Aug 8;318(6):517-518.
    • “The process of achieving value in terms of medical decision support does not remove the clinician or radiologist, but instead, provides easier access to information that might otherwise be inaccessible, inefficient, or difficult to integrate in real-time for the consulting physician. When this information is distilled in a way available to the radiologist, it becomes knowledge that can positively impact the clinician’s judgment in a personalized way in real-time.”

      
Reinventing Radiology: Big Data and the Future of Medical Imaging 
Morris MA et al.
J Thorac Imaging 2018;33:4–16
    • “Many tools have been developed to risk stratify patients into categories of pretest probability for CAD by generalizing patients into low-risk, medium-risk, and high- risk categories. Examples such as the Diamond and Forrester method, the Duke Clinical Score, and the Framingham Risk Score incorporate prior clinical history of cardiac events, certain characteristics of the chest pain, family history, medical history, age, sex, and results of a lipid panel. Imaging findings have been used in this type of risk stratification as well, with coronary calcium scoring.”

      
Reinventing Radiology: Big Data and the Future of Medical Imaging 
Morris MA et al.
J Thorac Imaging 2018;33:4–16
    • “Importantly for radiologists, machine learning algorithms can help address many problems in current-day radiology practices that do not involve image interpretation. Although much of the attention in the machine learning space has focused on the ability of machines to classify image findings, there are many other useful applications of machine learning that will improve efficiency and utilization of radiology practices today. Moreover, we may see a world where a symbiosis of subspecialty experts and machines lead to better care than could be provided by either one alone. Those practices that implement these technologies today are likely to better position themselves for the future.” 


      Machine Learning in Radiology: 
Applications Beyond Image Interpretation 
Paras Lakhani et al.
J Am Coll Radiol (in press)
    • “Advances in natural language processing (NLP) and machine learning can be used to better interpret and classify reports from image-based procedures such that more accurate claims can be submitted for reimbursement. Insurance denials have been reported to cost health care organizations as much as 3% to 5%. Denials may be related to a combination of inputs, rather than due to just one alone, which can be difficult to decipher. As a result, hospitals and health care systems are turning to artificial intelligence to reduce denials, prioritize work queues for claims resubmissions, and alter processes to help prevent future denials.”

      
Machine Learning in Radiology: 
Applications Beyond Image Interpretation 
Paras Lakhani et al.
J Am Coll Radiol (in press)
    • “Radiomics (or radiogenomics) is the correlation between the imaging appearance of cancer and the genomics of such. Advances in traditional machine learning and more novel deep learning approaches in this area have shown promising results. Moreover, deep learning techniques has achieved state-of-the-art results in biomedical image segmentation, which can be used to automatically segment and extract volumes of organs, specific tissues, and regions of interest. The radiology report of the future may automatically include such quantitative information, which could be used to assess disease and guide treatment decisions.”


      Machine Learning in Radiology: 
Applications Beyond Image Interpretation 
Paras Lakhani et al.
J Am Coll Radiol (in press)
    • “Ultimately, machine learning has the potential to dramatically improve patient care. Importantly for radiologists, machine learning algorithms can help address many problems in current-day radiology practices that do not involve image interpretation. Although much of the attention in the machine learning space has focused on the ability of machines to classify image findings, there are many other useful applications of machine learning that will improve efficiency and utilization of radiology practices today.”


      Machine Learning in Radiology: 
Applications Beyond Image Interpretation 
Paras Lakhani et al.
J Am Coll Radiol (in press)
    • “Human experts and machines have different strengths. Accordingly, there are tasks that are better suited for machines and others for humans. Some advantages of machines are that they can work 24 hours per day and contemporaneously. Also, machines may be designed to provide consistent analysis for a given input or series of input parameters. This allows for precision and potential for quantification in results reporting. Machines can analyze large volumes of data and find complex associations hidden within these data that may be otherwise difficult for a human to do.”


      Machine Learning in Radiology: 
Applications Beyond Image Interpretation 
Paras Lakhani et al.
J Am Coll Radiol (in press)
    • “There are a number of ways in which machine learning can help radiology practices today, including many tasks that are frequently performed by radiologists and ordering clinicians, such as imaging appropriateness assessment, creating study protocols, and standardization of radiology reporting, that could benefit from automation. Although many of these examples could be implemented using conventional procedural programming methodologies, the machine learning approach holds the promise to perform these tasks with a higher level of proficiency that can improve over time as the system “learns” new data.”

      
Machine Learning in Radiology: 
Applications Beyond Image Interpretation 
Paras Lakhani et al.
J Am Coll Radiol (in press)
    • “Many large capital corporations in the digital world including Microsoft (Microsoft Corp, Redmond, Washington, USA), Google (Menlo Park, California, USA), Apple (Apple Inc, Cupertino, California, USA), Facebook (Facebook, Inc, Menlo Park, California, USA), Baidu (Baidu Inc, Beijing, China), and Amazon (Amazon Inc, Seattle, Washington, USA) incorporate machine learning in their products.”

      Machine Learning in Radiology: 
Applications Beyond Image Interpretation 
Paras Lakhani et al.
J Am Coll Radiol (in press)
    • “Machine learning has been used across many industries, including banking and finance, manufacturing, marketing, and telecommunications. Some more common every day examples include e-mail spam filters, face recognition, search engines, speech recognition, and language translation. Many large capital corporations in the digital world including Microsoft (Microsoft Corp, Redmond, Washington, USA), Google (Menlo Park, California, USA), Apple (Apple Inc, Cupertino, California, USA), Facebook (Facebook, Inc, Menlo Park, California, USA), Baidu (Baidu Inc, Beijing, China), and Amazon (Amazon Inc, Seattle, Washington, USA) incorporate machine learning in their products.”

      
Machine Learning in Radiology: 
Applications Beyond Image Interpretation 
Paras Lakhani et al.
J Am Coll Radiol (in press)
    • “The ultimate clinical verification of a diagnostic or predictive artificial intelligence tools requires a demonstration of their value through effect on patient outcomes, beyond performance metrics; this can be achieved through clinical trials or well- designed observational outcome research.” 


      Methodologic Guide for Evaluating Clinical Performance and Effect of Artificial Intelligence Technology for Medical Diagnosis and Prediction 
 Seong Ho Park, Kyunghwa Han
Radiology (in press) 2018
    • “Evaluation of the clinical performance of a diagnostic or predictive artificial intelligence model built with high-dimensional data requires use of external data from a clinical cohort that ade- quately represents the target patient population to avoid over-estimation of the results due to over fitting and spectrum bias.” 


      Methodologic Guide for Evaluating Clinical Performance and Effect of Artificial Intelligence Technology for Medical Diagnosis and Prediction 
 Seong Ho Park, Kyunghwa Han
Radiology (in press) 2018
    • “Development of an algorithm for medical diagnosis or prediction, especially an algorithm in which deep neural networks are used, typically requires a huge dataset, often referred to as “big data.” Therefore, unlike prospective clinical trials, in which subjects are typically recruited uniformly and consecutively according to eligibility criteria explicitly defined for a particular clinical setting, the data used to develop a deep learning algorithm for medical diagnosis or prediction often must be collected from multiple heterogeneous sources in various ways.” 


      Methodologic Guide for Evaluating Clinical Performance and Effect of Artificial Intelligence Technology for Medical Diagnosis and Prediction 
 Seong Ho Park, Kyunghwa Han
Radiology (in press) 2018
    • “Robust clinical verification of the performance of a diagnostic or predictive artificial intelligence model requires external validation (validation here means verification of a model’s performance) in a clinical cohort that adequately represents the target patient population, and the use of prospectively collected data is desirable. This procedure is crucial for avoiding overestimation of the performance as a result of over fitting in a high-dimensional or overparameterized classification model and spectrum bias.” 


      Methodologic Guide for Evaluating Clinical Performance and Effect of Artificial Intelligence Technology for Medical Diagnosis and Prediction 
 Seong Ho Park, Kyunghwa Han
Radiology (in press) 2018
    • AI in Healthcare

    • AI in Healthcare

    • “As such, interruptions can contribute to all cognitive biases, particularly satisfaction of search and premature closure. Implementing strategies to reduce work flow disruption, such as designating an individual to manage noninterpretive tasks, has been successful in increasing interpretation time and decreasing disruptions.” 


      Bias in Radiology: The How and Why of Misses and Misinterpretations 
 Busby LP et al.
 RadioGraphics 2018; 38:236–247
    • “Creating a peer-review program with a positive culture is essential to correct cognitive bias. Peer-review programs establish an environment where errors are instructive rather than punitive and support an atmosphere of cognitive debiasing, ensuring that hindsight bias does not overwhelm the retrospective analyses. It is important to acknowledge both hits and misses throughout this process, as sharing and discussing positive calls, in addition to missed findings, helps foster a positive environment for analyzing clinical decisions.” 


      Bias in Radiology: The How and Why of Misses and Misinterpretations 
 Busby LP et al.
 RadioGraphics 2018; 38:236–247
    • “Routine radiologic-pathologic correlation is the current standard of care in breast imaging. It has the potential for positive implications on cognitive training for all involved when radiologic diagnoses have pathologic correlates. Radiologic-pathologic correlation gives a diagnostic radiologist accurate feedback on his or her disease detection rates, positive predictive values, and abnormal interpretation rates. This establishes the radiologist’s knowledge of base rates of disease and therefore helps increase awareness and prevention of availability bias.” 


      Bias in Radiology: The How and Why of Misses and Misinterpretations 
 Busby LP et al.
 RadioGraphics 2018; 38:236–247
    • “Cognitive forcing strategies and metacognition can help disrupt and reduce the impact of cognitive bias on decision making and decrease rates of diagnostic error. Systemic sources of error that can impact cognitive processes should be addressed through institutional measures, including limiting unnecessary interruptions during imaging interpretation and providing radiologists with diagnostic feedback through peer-review programs, quality improvement, and radiologic-pathologic correlation. Being aware of the limitations in one’s judgment can lead to more thoughtful deliberation of imaging findings and improve the quality of decision making.” 


      Bias in Radiology: The How and Why of Misses and Misinterpretations 
 Busby LP et al.
 RadioGraphics 2018; 38:236–247
    • “The process of achieving value in terms of medical decision support does not remove the clinician or radiologist, but instead, provides easier access to information that might otherwise be inaccessible, inefficient, or difficult to integrate in real-time for the consulting physician. When this information is distilled in a way available to the radiologist, it becomes knowledge that can positively impact the clinician’s judgment in a personalized way in real-time.”


      Reinventing Radiology: Big Data and the Future of Medical Imaging 
Morris MA et al.
J Thorac Imaging 2018;33:4–16
    • “Many tools have been developed to risk stratify patients into categories of pretest probability for CAD by generalizing patients into low-risk, medium-risk, and high- risk categories. Examples such as the Diamond and Forrester method, the Duke Clinical Score, and the Framingham Risk Score incorporate prior clinical history of cardiac events, certain characteristics of the chest pain, family history, medical history, age, sex, and results of a lipid panel. Imaging findings have been used in this type of risk stratification as well, with coronary calcium scoring.”


      Reinventing Radiology: Big Data and the Future of Medical Imaging 
Morris MA et al.
J Thorac Imaging 2018;33:4–16
    • “Importantly for radiologists, machine learning algorithms can help address many problems in current-day radiology practices that do not involve image interpretation. Although much of the attention in the machine learning space has focused on the ability of machines to classify image findings, there are many other useful applications of machine learning that will improve efficiency and utilization of radiology practices today. Moreover, we may see a world where a symbiosis of subspecialty experts and machines lead to better care than could be provided by either one alone. Those practices that implement these technologies today are likely to better position themselves for the future.” 


      Machine Learning in Radiology: 
Applications Beyond Image Interpretation 
Paras Lakhani et al.
J Am Coll Radiol (in press)
    • “Radiomics (or radiogenomics) is the correlation between the imaging appearance of cancer and the genomics of such. Advances in traditional machine learning and more novel deep learning approaches in this area have shown promising results. Moreover, deep learning techniques has achieved state-of-the-art results in biomedical image segmentation, which can be used to automatically segment and extract volumes of organs, specific tissues, and regions of interest. The radiology report of the future may automatically include such quantitative information, which could be used to assess disease and guide treatment decisions.”


      Machine Learning in Radiology: 
Applications Beyond Image Interpretation 
Paras Lakhani et al.
J Am Coll Radiol (in press)
    • “Ultimately, machine learning has the potential to dramatically improve patient care. Importantly for radiologists, machine learning algorithms can help address many problems in current-day radiology practices that do not involve image interpretation. Although much of the attention in the machine learning space has focused on the ability of machines to classify image findings, there are many other useful applications of machine learning that will improve efficiency and utilization of radiology practices today.”


      Machine Learning in Radiology: 
Applications Beyond Image Interpretation 
Paras Lakhani et al.
J Am Coll Radiol (in press)
    • “Human experts and machines have different strengths. Accordingly, there are tasks that are better suited for machines and others for humans. Some advantages of machines are that they can work 24 hours per day and contemporaneously. Also, machines may be designed to provide consistent analysis for a given input or series of input parameters. This allows for precision and potential for quantification in results reporting. Machines can analyze large volumes of data and find complex associations hidden within these data that may be otherwise difficult for a human to do.”


      Machine Learning in Radiology: 
Applications Beyond Image Interpretation 
Paras Lakhani et al.
J Am Coll Radiol (in press)
    • “There are a number of ways in which machine learning can help radiology practices today, including many tasks that are frequently performed by radiologists and ordering clinicians, such as imaging appropriateness assessment, creating study protocols, and standardization of radiology reporting, that could benefit from automation. Although many of these examples could be implemented using conventional procedural programming methodologies, the machine learning approach holds the promise to perform these tasks with a higher level of proficiency that can improve over time as the system “learns” new data.”


      Machine Learning in Radiology: 
Applications Beyond Image Interpretation 
Paras Lakhani et al.
J Am Coll Radiol (in press)
    • “Machine learning has been used across many industries, including banking and finance, manufacturing, marketing, and telecommunications. Some more common every day examples include e-mail spam filters, face recognition, search engines, speech recognition, and language translation. Many large capital corporations in the digital world including Microsoft (Microsoft Corp, Redmond, Washington, USA), Google (Menlo Park, California, USA), Apple (Apple Inc, Cupertino, California, USA), Facebook (Facebook, Inc, Menlo Park, California, USA), Baidu (Baidu Inc, Beijing, China), and Amazon (Amazon Inc, Seattle, Washington, USA) incorporate machine learning in their products.”


      Machine Learning in Radiology: 
Applications Beyond Image Interpretation 
Paras Lakhani et al.
J Am Coll Radiol (in press)
    • Systemic Sources and Solutions
      • Workplace Interruptions
      • Quality Assurance and Peer Review.
      • Radiologic-Pathologic Correlation 

    • “Radiologists may not be aware of additional resources available at the scanner and the workstation to increase lesion conspicuity and detection as image quality and quantity decrease, including virtual noncontrast data sets from dual-energy CT, 3-D rendering (maximum intensity projection [MIP], volume rendering [VR], and cinematic rendering [CR]), computer-assisted diagnosis, and texture analysis.” 


      Enhancing Image Quality in the Era of Radiation Dose Reduction: Postprocessing Techniques for Body CT
Pamela T. Johnson, Elliot K. Fishman 
JACR (in press)

    • “Use of MIP renderings at the workstation can improve detection of lung nodules and renal calculi on noncontrast scans performed with low-dose techniques. For contrast-enhanced scans, MIP renderings increase conspicuity of small hypervascular hepatic metastases (such as neuro- endocrine tumor) on the arterial phase scan. 
MIP rendering of arterial phase data sets aids in characterization of solitary hepatic masses by confirming neovascularity in malignant tumors and depicting the classic feeding artery in focal nodular hyperplasia.”

      
Enhancing Image Quality in the Era of Radiation Dose Reduction: Postprocessing Techniques for Body CT
Pamela T. Johnson, Elliot K. Fishman 
JACR (in press)
    • “The newest 3-D rendering tool, CR, holds promise for even greater diagnostic capabilities with respect to tumor characterization. The enhanced anatomic detail made possible from CR provides greater textural information about solid organs and tumors than conventional VR.” 


      Enhancing Image Quality in the Era of Radiation Dose Reduction: Postprocessing Techniques for Body CT
Pamela T. Johnson, Elliot K. Fishman 
JACR (in press)
    • “A postprocessing tool that improves tumor characterization and the radiologist’s role in patient management is texture analysis. The technique provides detailed information about pathology that is beyond the discriminatory capability of the hu- man eye by evaluating pixel heterogeneity and, similar to 3-D rendering, requiring no additional radiation to generate diagnostically useful data. A number of studies have demonstrated that texture analysis correlates with tumor grade, angiogenesis, and other predictors of treatment response.”

      
Enhancing Image Quality in the Era of Radiation Dose Reduction: Postprocessing Techniques for Body CT
Pamela T. Johnson, Elliot K. Fishman 
JACR (in press)
    • “Computer-aided detection is an important adjuvant for low-dose chest CT, harnessing automated lung nodule detection to augment radiologists’ interpretations. Ultra low-dose screening CT with computer-aided detection has been shown to be equivalent in sensitivity to standard dose for identification of lung nodules.” 


      Enhancing Image Quality in the Era of Radiation Dose Reduction: Postprocessing Techniques for Body CT
Pamela T. Johnson, Elliot K. Fishman 
JACR (in press)
    • “Body CT imagers must embrace the pledge for responsible patient selection and protocol design put forth by the Image Wisely Campaign and the ACR’s newest Choosing Wisely recommendations. The tools described herein can be used to enhance interpretative performance in the face of reductions in image quality that result from low- dose techniques. Innovations such as texture mapping and CR are equally important, because they advance management guidance and further poise radiologists to serve as valuable members of the patient care team.” 


      Enhancing Image Quality in the Era of Radiation Dose Reduction: Postprocessing Techniques for Body CT
Pamela T. Johnson, Elliot K. Fishman 
JACR (in press)
Adrenal

    • Bilateral Adrenal Masses
      • pheochromocytoma (40%)
      • tuberculosis (27.1%)
      • primary adrenal lymphoma (PAL) (10%)
      • metastases (5.7%)
      • non-functioning adenomas (4.3%)
      • primary bilateral macronodular adrenal hyperplasia (4.3%)
      • others (8.6%)
    • Bilateral Adrenal Masses (NEJM)
      • adrenal hyperplasia
         - micronodular adrenal hyperplasia
         - macronodular adrenal hyperplasia
                - adrenocorticotropin (ACTH)-independent macronodular adrenocortical hyperplasia (AIMAH)
      • adrenal metastases
      • adrenal haemorrhage
    • Bilateral Adrenal Masses (NEJM)
      • adrenal involvement with granulomatous diseases
      • adrenal lymphoma
      • primary pigmented nodular adrenal dysplasia (PPNAD): usually the glands are normal in size, but nodular. In older patients they may be enlarged 1
Chest

    • “A diameter ≤3 cm and an unenhanced CT value >20 Hu were independent factors of incorrect diagnosis of chest CT. VATS is a reliable approach for the surgical resection of thymic cysts. We think that local resection is adequate for simple thymic cysts. However, thymectomy is necessary when there is suspicion of a thymoma or multilocular thymic cyst, and radical thymectomy is advisable for patients with autoimmune diseases.”

      
Clinical features, diagnosis and thoracoscopic surgical treatment of thymic cysts.
Wang X et al.
J Thorac Dis. 2017 Dec;9(12):5203-5211

    • “Thymic cysts are rare benign developmental anomalies and there is no consensus management for thymic cysts. The aim of this study was to disclose the efficacy of perioperative diagnosis for thymic cysts by chest computerized tomography (CT) and to elucidate the surgical procedure by video-assisted thoracic surgery (VATS) in the management of thymic cysts.”


      Clinical features, diagnosis and thoracoscopic surgical treatment of thymic cysts.
Wang X et al.
J Thorac Dis. 2017 Dec;9(12):5203-5211

    • “It is difficult to distinguish thymic cysts from the solid neoplasm of the thymus in some patients, and there are few large-scale studies that analyzed the preoperative diagnoses of thymic cysts. Preoperative diagnosis of a thymic cyst mainly depends on an imaging examination. In our study, only 37.0% patients presented with a widened mediastinum or mediastinal mass in the chest X-ray, and none were diagnosed with a thymic cyst. The chest X-ray can only be considered a screening examination. The chest CT is the most widely used imaging examination for mediastinal disease. For thymic cysts, the chest CT can describe the shape, contour, CT value, relationship to adjacent tissue, and the contrast-enhanced CT appearance. The typical imaging performance of the thymic cyst was oval in shape, and had a smooth contour, cystic density, homogeneous attenuation, and thin or imperceptible walls.”


      Clinical features, diagnosis and thoracoscopic surgical treatment of thymic cysts.
Wang X et al.
J Thorac Dis. 2017 Dec;9(12):5203-5211
    • “ A diameter ≤3 cm and an unenhanced CT value >20 Hu were independent factors of incorrect diagnosis of chest CT. VATS resection should be considered the primary therapeutic option for the management of thymic cysts. We think that local resection is adequate for simple thymic cysts. However, thymectomy is necessary when there is suspicion of a thymoma or multilocular thymic cyst. For patients with autoimmune diseases, a radical extended thymectomy is advisable.”

      
Clinical features, diagnosis and thoracoscopic surgical treatment of thymic cysts.
Wang X et al.
J Thorac Dis. 2017 Dec;9(12):5203-5211
Colon

    • “Colorectal cancer is a disease that is curable if detected early and preventable if precursor adenomas are detected and removed. Approximately 130,000 new cases were diagnosed in the United States in 2000, and approximately 56,000 deaths were attributed to the disease. The typical age at which most patients are diagnosed is during the sixth and seventh decades of life.”


      Imaging in the Diagnosis, Staging, and Follow-Up of Colorectal Cancer 
 Iyer RB et al.
 AJR 2002;179:3–13
    • “Evolution in the treatment of metastatic colorectal cancer (mCRC) has led to significant improvement in the survival of these patients. Surgery is useful in patients with resectable disease. Liver-directed therapies such as hepatic arterial infusion, transarterial radio- and chemoembolization, and percutaneous ablation are sometimes used by oncologists when the liver is the only site of metastatic disease. Unresectable mCRC is typically treated with systemic chemotherapy.”


      Update on the Role of Imaging in Management of Metastatic Colorectal Cancer 
 Tirumani SH et al.
 RadioGraphics 2014; 34:1908–1928
    • “CRC most commonly metastasizes to the liver, with more than one-half of patients developing hepatic metastases either synchronously or metachronously.The lung is the second most common organ to harbor CRC metastases, followed by the peritoneal cavity. Peritoneal involvement is seen as peritoneal carcinomatosis and, in some cases, as pseudomyxoma peritonei, especially with the primary tumor arising from the appendix. Brain and bone metastases are uncommon.”


      Update on the Role of Imaging in Management of Metastatic Colorectal Cancer 
 Tirumani SH et al.
 RadioGraphics 2014; 34:1908–1928
    • “The most reliable phase for the detection of hepatic metastases is the portal venous phase (approximately 60–70 seconds following initiation of intravenous contrast material administration), with a detection rate of 85% and a positive predictive value of 96%. In the arterial phase, CRC metastases can have peripheral rimlike enhancement. Calcification is noted in 11% of CRC liver metastases.”

      Update on the Role of Imaging in Management of Metastatic Colorectal Cancer 
 Tirumani SH et al.
 RadioGraphics 2014; 34:1908–1928
    • “Detection of liver metastases at multidetector CT can be difficult in the presence of fatty liver, which is most often the result of concurrent chemotherapy . Differentiation of small hemangiomas and cysts less than 1 cm in size from metastases can also be difficult at times due to volume averaging. The sensitivity of CT for detect- ing lesions less than 1 cm falls from 65%–95% to 31%–38%. Multidetector CT has a specificity of 67% in characterizing lesions as benign or malignant, compared with 81% for MR imaging.”


      Update on the Role of Imaging in Management of Metastatic Colorectal Cancer 
 Tirumani SH et al.
 RadioGraphics 2014; 34:1908–1928
Esophagus

    • “Fish bone is one of the most common accidentally ingested foreign bodies, and patients commonly present to the emergency department with nonspecific symptoms. Fortunately, most of them are asymptomatic and exit the gastrointestinal tract spontaneously. However, fish bones can get impacted in any part of the aerodigestive tract and cause symptoms. Occasionally, they are asymptomatic initially after ingestion and may present remotely at a later date with serious complications such as gastrointestinal tract perforation, obstruction, and abscess formation.”


      CT findings of accidental fish bone ingestion and its complications
 Venkatesh SH et al.
 Diagn Interv Radiol. 2016 Mar; 22(2): 156–160.
    • “The most common site of impaction in the esophagus is within the cervical portion, mostly within the cricopharyngeus muscle at C5/C6 level . The other sites of impaction within the esophagus are at the level of aortic arch, gastroesophageal junction, where normal extrinsic impression or anatomical narrowing is expected . Such patients with fish bone impaction in the pharynx and esophagus usually present with symptoms like foreign body sensation, pain, and swelling. Hence, diagnosis, especially by CT scan, is not very difficult as definitive clinical history is usually present.”


      CT findings of accidental fish bone ingestion and its complications
 Venkatesh SH et al.
 Diagn Interv Radiol. 2016 Mar; 22(2): 156–160.
    • “Fish bone impaction at these sites can be complicated by esophageal perforation, bleeding, hematoma, and abscess formation. Rarely, fistulation into the adjacent trachea or great vessels can be seen. Thin wall, lack of adventitia, and relatively poor vascularity of the esophagus makes it more susceptible to perforation and necrosis.”


      CT findings of accidental fish bone ingestion and its complications
 Venkatesh SH et al.
 Diagn Interv Radiol. 2016 Mar; 22(2): 156–160.
    • “Fish bone perforations can occasionally simulate malignancy and other acute and chronic inflammatory processes. This is commonly due to significant inflammatory thickening or mass-like appearance of the involved structures, lack of background history of fish bone ingestion and unfamiliarity with varied imaging appearance of the fish bones. CT is highly sensitive, and a definitive diagnosis can be established by identification of the fish bone. Careful attention to technical factors like thin slice thickness [1.5–2 mm], presence of negative bowel contrast and evaluation of reformats in multiple planes can aid accurate diagnosis..”


      CT findings of accidental fish bone ingestion and its complications
 Venkatesh SH et al.
 Diagn Interv Radiol. 2016 Mar; 22(2): 156–160.
    • “Familiarity with various imaging features and relevant clinical history can establish the diagnosis of accidental fish bone ingestion. CT with its multiplanar capability is highly valuable to diagnose and accurately localize the ingested fish bone. In addition, CT can also provide a comprehensive evaluation of the complications of fish bone ingestion including those that may be seen remote from the site of bowel perforation.”


      CT findings of accidental fish bone ingestion and its complications
 Venkatesh SH et al.
 Diagn Interv Radiol. 2016 Mar; 22(2): 156–160.
    • In adults, foreign body impactions are mostly seen in the context of a pre-existing pathology
      • Strictures (about 37%)
      • Malignancy (about 10%)
      • Esophageal rings (about 6%)
      • Achalasia (about 2% of cases)
    • Eosinophilic esophagitis, which has a secondary role in foreign body impaction, has been described in up to 33% of cases of bolus impaction. However, in some cases no pathological predisposition is present. Furthermore, more cases of ingested foreign bodies are reported in patients of advanced age, those with mental retardation, and with psychiatric disorders. The physiologically and anatomically narrow parts of the gastrointestinal tract make the passage of the ingested body difficult and are predilected sites for foreign body impaction  
    • The range of indications for endoscopy should be extensive; bolus impaction with complete occlusion of the esophagus, sharp/pointed foreign bodies, and batteries constitute indications for emergency esophagogastroduodenoscopy, magnets and long (>6 cm) foreign bodies should be removed within 24 hours.

      In the overwhelming majority of patients the ingested body passes without any problems; endoscopic intervention is required in 20% of cases and surgical intervention in less than 1% of cases.
Kidney

    • “CR represents a new level of achievable anatomic detail with 3D CT visualization. The role of CR in renal pathology has yet to be extensively explored, although the figures in this pictorial review suggest an immense potential for this technique. 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: First Experience with Cinematic Rendering
Rowe SP, Gorin MA, Meyer A, Johnson PT, Fishman EK
Abdominal Imaging (in press)
    • “3D computed tomography (CT) visualizations of volumetric data have become an important aspect of diagnostic imaging and their utility in pathologic states of the kidney has been well-described. Recently, a new 3D visualization technique known as cinematic rendering (CR) has become available and provides photorealistic images derived from standard CT acquisitions by use of a complex global lighting model. Herein, we describe important imaging aspects of a number of normal variant and pathologic conditions of the kidney, known applications of traditional methods of 3D imaging in kidney pathology, present the appearance many of those conditions with CR visualizations, and comment on the potential applications of this new method for evaluation of the kidney.” 


      3D CT of Renal Pathology: First Experience with Cinematic Rendering
Rowe SP, Gorin MA, Meyer A, Johnson PT, Fishman EK
Abdominal Imaging (in press)
Liver

    • “Gross fat at CT is less attenuating than other soft tissues, measuring −20 HU or less. The presence of intravoxel fat on CT will reduce the attenuation of that tissue; for example, fatty livers show progressively lower hepatic attenuation values that correspond to worsening grades of steatosis. On unenhanced CT, fat-containing liver lesions are hypoattenuating to liver, provided there is no steatosis; however, fat attenuation values are measured only when fat is present in sufficient quantity.” 


      Fat-Containing Liver Lesions on Imaging: Detection and Differential Diagnosis 
Andreu F. Costa et al. 
AJR 2018; 210:68–77
    • Fat Containing Hepatic Tumors: Differential Dx
      • Hepatocellular Carcinoma and Regenerative Nodules
      • Hepatocellular Adenoma
      • Focal Nodular Hyperplasia
      • Angiomyolipoma
      • Lipoma
      • Fat-Containing Metastases of an Extrahepatic Primary Tumor
      • Hydatid Cyst
    • “Hepatic AMLs are typically solitary but often coexist with renal AMLs when associated with the tuberous sclerosis complex. Although 20% of renal AMLs are associated with tuberous sclerosis complex, hepatic AMLs are associated with tuberous sclerosis complex in only 6–10% of cases.” 


      Fat-Containing Liver Lesions on Imaging: Detection and Differential Diagnosis 
Andreu F. Costa et al. 
AJR 2018; 210:68–77
    • “True hepatic lipomas are less common than AMLs. Because they are composed entirely of mature adipose tissue, they are homogeneously echogenic on ultrasound and may exhibit posterior acoustic attenuation . Pure fat attenuation of −20 HU or less is measured on CT. On MRI, lipomas are isointense to subcutaneous fat on all sequences, with homogeneous loss of signal noted on fat-saturated images, and they are circumscribed by etching artifact on opposed-phase GRE T1-weighted MRI. There is little to no enhancement on CT or MRI.” 
Fat-Containing Liver Lesions on Imaging: Detection and Differential Diagnosis 
Andreu F. Costa et al. 
AJR 2018; 210:68–77
    • “Fat-containing liver metastases are uncommon and typically arise from liposarcomas and malignant germ cell tumors. Although clear cell renal cell carcinoma (RCC) likely is the most common fat-containing primary malignancy to metastasize to the liver, only a minority of clear cell RCC metastases show fat signal on chemical-shift MRI. The primary diagnosis is often known at the time of presentation with liver metastases; fat-containing liver metastases also typically occur in the setting of widespread metastatic disease, with multifocal hepatic and extrahepatic metastases.” 


      Fat-Containing Liver Lesions on Imaging: Detection and Differential Diagnosis 
Andreu F. Costa et al. 
AJR 2018; 210:68–77
    • “When present, fat droplets or a fat-fluid level within a liver lesion distinguish hydatid disease from other fat-containing liver lesions. Other imaging features of hydatid disease will depend on the evolutionary stage of the cyst, as outlined by the World Health Organization classification scheme. These features include intraluminal debris corresponding to hydatid sand (the snow flake sign); multivesicular, multiseptated lesions in which daughter cysts partly or completely fill the mother cyst, resembling a honeycomb; detached membranes (the water lily sign); and rim calcification.” 


      Fat-Containing Liver Lesions on Imaging: Detection and Differential Diagnosis 
Andreu F. Costa et al. 
AJR 2018; 210:68–77
Pancreas

    • “CT texture analysis and CT features can be used to predict PNET grade according to the WHO classification. They also can be used to identify patients at risk of early recurrence or progression after surgical resection.”


      Prediction of Pancreatic Neuroendocrine Tumor Grade Based on CT Features and Texture Analysis 
Canellas R et al.
AJR 2018; 210:341–346
    • “The images were evaluated for tumor location, tumor size, tumor pattern, predominantly solid or cystic composition, presence of calcification, presence of heterogeneous enhancement on contrast-enhanced images, presence of pancreatic duct dilatation, presence of pancreatic atrophy, presence of vascular involvement by the tumor, and presence of lymphadenopathy. Texture features were also extracted from CT images. Surgically verified tumors were graded according to the WHO classification, and patients underwent CT or MRI follow-up after surgical resection.”


      Prediction of Pancreatic Neuroendocrine Tumor Grade Based on CT Features and Texture Analysis 
Canellas R et al.
AJR 2018; 210:341–346
    • “The CT features predictive of a more aggressive tumor (grades 2 and 3) were size larger than 2.0 cm (odds ratio [OR], 3.3; p = 0.014), presence of vascular involvement (OR, 25.2; p = 0.003), presence of pancreatic ductal dilatation (OR, 6.0; p = 0.002), and presence of lymphadenopathy (OR, 6.8; p = 0.002). The texture parameter entropy (OR, 3.7; p = 0.008) was also predictive of more aggressive tumors.”


      Prediction of Pancreatic Neuroendocrine Tumor Grade Based on CT Features and Texture Analysis 
Canellas R et al.
AJR 2018; 210:341–346
Radiation Dose

    • “Radiologists may not be aware of additional resources available at the scanner and the workstation to increase lesion conspicuity and detection as image quality and quantity decrease, including virtual noncontrast data sets from dual-energy CT, 3-D rendering (maximum intensity projection [MIP], volume rendering [VR], and cinematic rendering [CR]), computer-assisted diagnosis, and texture analysis.”

      
Enhancing Image Quality in the Era of Radiation Dose Reduction: Postprocessing Techniques for Body CT
Pamela T. Johnson, Elliot K. Fishman 
JACR (in press)

    • “Use of MIP renderings at the workstation can improve detection of lung nodules and renal calculi on noncontrast scans performed with low-dose techniques. For contrast-enhanced scans, MIP renderings increase conspicuity of small hypervascular hepatic metastases (such as neuro- endocrine tumor) on the arterial phase scan. 
MIP rendering of arterial phase data sets aids in characterization of solitary hepatic masses by confirming neovascularity in malignant tumors and depicting the classic feeding artery in focal nodular hyperplasia.”

      
Enhancing Image Quality in the Era of Radiation Dose Reduction: Postprocessing Techniques for Body CT
Pamela T. Johnson, Elliot K. Fishman 
JACR (in press)
    • “The newest 3-D rendering tool, CR, holds promise for even greater diagnostic capabilities with respect to tumor characterization. The enhanced anatomic detail made possible from CR provides greater textural information about solid organs and tumors than conventional VR.”

      
Enhancing Image Quality in the Era of Radiation Dose Reduction: Postprocessing Techniques for Body CT
Pamela T. Johnson, Elliot K. Fishman 
JACR (in press)
    • “A postprocessing tool that improves tumor characterization and the radiologist’s role in patient management is texture analysis. The technique provides detailed information about pathology that is beyond the discriminatory capability of the hu- man eye by evaluating pixel heterogeneity and, similar to 3-D rendering, requiring no additional radiation to generate diagnostically useful data. A number of studies have demonstrated that texture analysis correlates with tumor grade, angiogenesis, and other predictors of treatment response.”

      
Enhancing Image Quality in the Era of Radiation Dose Reduction: Postprocessing Techniques for Body CT
Pamela T. Johnson, Elliot K. Fishman 
JACR (in press)
    • “Computer-aided detection is an important adjuvant for low-dose chest CT, harnessing automated lung nodule detection to augment radiologists’ interpretations. Ultra low-dose screening CT with computer-aided detection has been shown to be equivalent in sensitivity to standard dose for identification of lung nodules.”

      
Enhancing Image Quality in the Era of Radiation Dose Reduction: Postprocessing Techniques for Body CT
Pamela T. Johnson, Elliot K. Fishman 
JACR (in press)
    • “Body CT imagers must embrace the pledge for responsible patient selection and protocol design put forth by the Image Wisely Campaign and the ACR’s newest Choosing Wisely recommendations. The tools described herein can be used to enhance interpretative performance in the face of reductions in image quality that result from low- dose techniques. Innovations such as texture mapping and CR are equally important, because they advance management guidance and further poise radiologists to serve as valuable members of the patient care team.” 


      Enhancing Image Quality in the Era of Radiation Dose Reduction: Postprocessing Techniques for Body CT
Pamela T. Johnson, Elliot K. Fishman 
JACR (in press)
Small Bowel

    • “A lymphangioma is a benign proliferation of lymph vessels, producing fluid filled cysts that result from a blockage of the lymphatic system. ae incidence of abdominal lymphangiomas is unknown; however they account for from 3% to 9.2% of all pediatric lymphangiomas, with retroperitoneal lymphangioma representing less than 1% of abdominal lymphangiomas.”

      
Retroperitoneal Cystic Lymphangioma: A Diagnostic and Surgical Challenge 
Oguzhan Güven Gümüştaş et al.
Case Reports in Pediatrics
Volume 2013 (2013), Article ID 292053 

    • “The incidence of abdominal lymphangiomas is unknown: however they account for from 3% to 9.2% of all pediatric lymphangiomas, with retroperitoneal lymphangioma representing less than 1% of abdominal lymphangiomas. Although retroperitoneal lymphangiomas may sometimes be asymptomatic, they usually present as a palpable abdominal mass and are easily confused with other retroperitoneal cystic tumors including those arising from the liver, kidney and pancreas.”


      Retroperitoneal Cystic Lymphangioma: A Diagnostic and Surgical Challenge 
Oguzhan Güven Gümüştaş et al.
Case Reports in Pediatrics
Volume 2013 (2013), Article ID 292053 

    • “Retroperitoneal lymphangiomas manifest with clinical symptoms of abdominal pain, fever, fatigue, weight loss, and hematuria, due to their size and occasionally might be complicated by intracystic hemorrhage, cyst rupture, volvulus or infection. Differentiating cystic lymphangiomas from other cystic growths by imaging studies alone is often inconclusive and surgery is most frequently required for definitive diagnosis and to ameliorate the symptoms.”

      
Retroperitoneal Cystic Lymphangioma: A Diagnostic and Surgical Challenge 
Oguzhan Güven Gümüştaş et al.
Case Reports in Pediatrics
Volume 2013 (2013), Article ID 292053 

    • “The lymphatic system is derived during the third or fourth fetal month from 2 paired and unpaired endothelial channels proliferate centrifugally from these sacs, which are located in the neck, mesenteric root, and bifurcation of the femoral and sciatic veins . A lymphangioma is a benign proliferation of lymphatic tissue believed to originate from the early sequestration of lymphatic vessels that fail to establish connections with normal draining lymphatics. Lymphangiomas are therefore considered a congenital rather than an acquired tumor.”


      Retroperitoneal Cystic Lymphangioma: A Diagnostic and Surgical Challenge 
Oguzhan Güven Gümüştaş et al.
Case Reports in Pediatrics
Volume 2013 (2013), Article ID 292053 

    • “Intravenous contrast-enhanced CT may show enhancement of the cyst wall and septa. The fluid component is typically homogeneous with low attenuation values. Occasionally, negative attenuation values occur in the presence of chyle. Calcification may occur but is uncommon .If hemorrhage occurs, the intracystic attenuation values may simulate a solid tumor mass or abscess. ae mass may traverse adjacent retroperitoneal anatomical compartments, displacing organs and vessels . They can compress and infiltrate vital structures or present with complications like intracystic hemorrhage, cyst 
rupture, volvulus, or infection.”


      Retroperitoneal Cystic Lymphangioma: A Diagnostic and Surgical Challenge 
Oguzhan Güven Gümüştaş et al.
Case Reports in Pediatrics
Volume 2013 (2013), Article ID 292053 

    • “Diagnostic important point for cystic lymphangioma: an elongated shape and a crossing from one retroperitoneal compartment to an adjacent one. Also at CT, cystic lymphangioma typically appears as a large, thin-walled, multiseptate cystic mass. A teratoma with a large cystic component can resemble a lymphangioma.”


      Retroperitoneal Cystic Lymphangioma: A Diagnostic and Surgical Challenge 
Oguzhan Güven Gümüştaş et al.
Case Reports in Pediatrics
Volume 2013 (2013), Article ID 292053
    • “The most common cause of villous atrophy is celiac disease. The villous atrophy results from injury to the small intestine and leads to loss of absorptive surface area, reduction of digestive enzymes, and consequential impaired absorption of micronutrients. Negative celiac serology or nonresponse to a gluten-free diet implies a broad and challenging differential diagnosis which includes Crohn's disease, enteric infections (e.g. Giardia lamblia), collagenous sprue, tropical sprue, common variable immunodeficiency, autoimmune enteropathy, hematological malignancies and medication-associated enteropathy. Regarding the latter, olmesartan medoxomil, an angiotensin receptor blocker for the management of hypertension, has been recently recognized as a cause of “sprue-like enteropathy”.”


      Olmesartan-Induced Enteropathy: An Unusual Cause of Villous Atrophy
Marta Eusébio et al.
GE Port J Gastroenterol. 2016 Mar-Apr; 23(2): 91–95.

    • Legal issues with Olmesartan
    • Legal issues with Olmesartan
    • OBJECTIVE. The objective of our study was to evaluate if the feces sign can be used to predict successful nonoperative treatment or progression to ischemia in patients with small- bowel obstruction (SBO) due to adhesions. 


      CONCLUSION. The feces sign is common and helps to identify the TZ. Among the CT signs of SBO, the feces sign does not independently help to predict successful nonoperative treatment or progression to ischemia. 


      Clinical Relevance of the Feces Sign in Small-Bowel Obstruction Due to Adhesions Depends on Its Location 
Wassef Khaled et al.
AJR 2018; 210:78–84
    • “Mechanical small-bowel obstruction (SBO) is a common cause of abdominal pain that accounts for 10–20% of emergency department visits and 20–25% of surgical admissions. Treatment of SBO has shifted toward a more conservative management when there are not any clinicobiologic or imaging findings suggestive of progression to ischemia. Consensus now exists in favor of recommending the use of MDCT in the evaluation of patients with SBO. The main challenges for the radiologist are, first, to rule out an ischemic complication and, second, to look for CT signs predictive of the effectiveness of medical treatment that would enable the surgeon to 
confidently select this management strategy.”


      Clinical Relevance of the Feces Sign in Small-Bowel Obstruction Due to Adhesions Depends on Its Location 
Wassef Khaled et al.
AJR 2018; 210:78–84
    • “In conclusion, in a large population of patients with SBO due to adhesions, we found the existence of several feces sign variants, which have different clinical meanings. However, none of these variants independently improved the prediction of successful nonoperative treatment or ischemia compared with previously validated CT signs.”


      Clinical Relevance of the Feces Sign in Small-Bowel Obstruction Due to Adhesions Depends on Its Location 
Wassef Khaled et al.
AJR 2018; 210:78–84
    • “Sprue-like enteropathy associated with the angiotensin II receptor blocker (ARB) olmesartan was first described in 2012, and a number of cases have since been reported. This syndrome is characterized by severe diarrhea and sprue-like histopathologic findings in the intestine, often with increased subepithelial collagen. The incidence of this adverse drug reaction is not entirely clear, although it is thought to be rare. It is also not well established if other ARBs cause such a syndrome, although case reports suggest they can. The histopathologic features of olmesartan-related injury have only been described in a limited number of cases, and there are no guidelines regarding the histopathologic distinction of olmesartan-associated enteropathy from other causes of sprue (eg, celiac disease, tropical sprue).”


      Olmesartan-associated sprue-like enteropathy: a systematic review with emphasis on histopathology.
Burbure N et al.
Hum Pathol. 2016 Apr;50:127-34
Spleen

    • “Active haemorrhage can be demonstrated by contrast-enhanced CT as an extravasation of intravenously introduced contrast material to the abdominal cavity. However, active extravasation should be evaluated by dual (arterial and venous) phase CT with appropriate contrast injection, since arterial phase images may not demonstrate venous extravasation, and similarly, arterial extravasation may not be differentiated from venous bleeding on venous phase imaging.”


      Imaging findings of splenic emergencies: a pictorial review
Unal E et al.
 Insights Imaging. 2016 Apr; 7(2): 215–222.
    • “Active extravasation can be seen as a focus of linear or nodular hyperdensity within a hematoma or into the abdominal cavity, on arterial phase images. On delayed phase images, accumulation of contrast is seen within or in the dependent portion of hematoma with regard to degree of clot formation. In the presence of acute massive haemorrhage, hemorrhagic fluid may act in a pattern similar to the free fluid due to decreased percentage of clot formation. In subtle extravasation, haemorrhage is commonly restricted to the site of solid organ injury, with the appearance of what is called the “sentinel clot sign”.”


      Imaging findings of splenic emergencies: a pictorial review
Unal E et al.
 Insights Imaging. 2016 Apr; 7(2): 215–222.
    • “The spleen is one of the most commonly injured organs in blunt abdominal trauma. Nevertheless, inflammatory and infectious disorders of the spleen are not uncommon. In addition, less common vascular incidents and splenic torsion can also be seen. Ultrasound (US) is usually used for the follow-up of patients with splenic emergencies, since accurate initial diagnosis based solely on US findings is limited. Differentiation of splenic hematoma from abscess or infarct and detection of an active bleeding may not be reliably made by US. Moreover, presence of subcutaneous air bubbles or air bubbles in an abscess cavity may prevent sonographic evaluation.”


      Imaging findings of splenic emergencies: a pictorial review
Unal E et al.
 Insights Imaging. 2016 Apr; 7(2): 215–222.
© 1999-2018 Elliot K. Fishman, MD, FACR. All rights reserved.