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

August 2018 Imaging Pearls - Educational Tools | CT Scanning | CT Imaging | CT Scan Protocols - CTisus
Imaging Pearls ❯ August 2018

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Adrenal

    • "Adrenalectomy is the standard of care for management of many adrenal tumor types and, in the United States alone, approximately 6000 adrenal surgeries are performed annually. Two general approaches to adrenalectomy have been described; (1) the open approach, in which a diseased adrenal is removed through a large (10-20 cm) abdominal wall incision, and (2) the minimally invasive approach, in which laparoscopy is used to excise the gland through incisions generally no longer than 1-2 cm. Given these disparate technique options, clear preoperative characterization of those specific disease features that inform selection of adrenalectomy approach is critically important to the surgeon.”

      
What the radiologist needs to know: the role of preoperative computed tomography in selection of operative approach for adrenalectomy and review of operative techniques.
Rowe SP1, Lugo-Fagundo C1, Ahn H1, Fishman EK1, Prescott JD2.
Abdom Radiol (NY). 2018 Jul 2. doi: 10.1007/s00261-018-1669-y. (in press)
Deep Learning

    • “Second, machine learning will displace much of the work of radiologists and anatomical pathologists. These physicians focus largely on interpreting digitized images, which can easily be fed directly to algorithms instead. Massive imaging data sets, com- bined with recent advances in computer vision, will drive rapid improvements in performance, and machine accuracy will soon exceed that of humans. Indeed, radiology is already partway there: algorithms can replace a second radiologist reading mammograms and will soon exceed human accuracy.”


      Predicting the Future — Big Data, Machine Learning, and Clinical Medicine
Obermeyer Z, Emanuel EJ
N Engl J Med 375;13 September 29, 2016
    • “The patient- safety movement will increasingly advocate the use of algorithms over humans — after all, algorithms need no sleep, and their vigilance is the same at 2 a.m. as at 9 a.m. Algorithms will also monitor and interpret streaming physiological data, replacing aspects of anesthesiology and criti- cal care. The time scale for these disruptions is years, not decades.”

      
Predicting the Future — Big Data, Machine Learning, and Clinical Medicine
Obermeyer Z, Emanuel EJ
N Engl J Med 375;13 September 29, 2016
    • “Machine learning will become an indispensable tool for clinicians seeking to truly understand their patients. As patients’ conditions and medical technologies become more complex, the role of machine learning will grow, and clinical medicine will be challenged to grow with it.”


      Predicting the Future — Big Data, Machine Learning, and Clinical Medicine
Obermeyer Z, Emanuel EJ
N Engl J Med 375;13 September 29, 2016
    • “As in other industries, this challenge will create winners and losers in medicine. But we are optimistic that patients, whose lives and medical histories shape the algorithms, will emerge as the biggest winners as machine learning transforms clinical medicine.”


      Predicting the Future — Big Data, Machine Learning, and Clinical Medicine
Obermeyer Z, Emanuel EJ
N Engl J Med 375;13 September 29, 2016
    • “But where machine learning shines is in handling enormous numbers of predictors — sometimes, remarkably, more predictors than observations — and combining them in nonlinear and highly interactive ways.This capacity al- lows us to use new kinds of data, whose sheer volume or complexity would previously have made analyzing them unimaginable.”


      Predicting the Future — Big Data, Machine Learning, and Clinical Medicine
Obermeyer Z, Emanuel EJ
N Engl J Med 375;13 September 29, 2016
    • “Another key issue is the quantity and quality of input data. Machine learning algorithms are highly data hungry, often re- quiring millions of observations to reach acceptable performance levels.In addition, biases in data collection can substantially affect both performance and generalizability. Lactate might be a good predictor of the risk of death, for example, but only a small, nonrepresentative sample of patients have their lactate levels checked.”

      
Predicting the Future — Big Data, Machine Learning, and Clinical Medicine
Obermeyer Z, Emanuel EJ
N Engl J Med 375;13 September 29, 2016
    • “Machine learning has become ubiquitous and indispensable for solving complex problems in most sciences. In astronomy, algorithms sift through millions of images from telescope surveys to classify galaxies and find supernovas.”


      Predicting the Future — Big Data, Machine Learning, and Clinical Medicine
Obermeyer Z, Emanuel EJ
N Engl J Med 375;13 September 29, 2016
    • “Increasingly, the ability to transform data into knowledge will disrupt at least three areas of medicine. First, machine learning will dramatically improve the ability of health professionals to es- tablish a prognosis. Current prognostic models (e.g., the Acute Physiology and Chronic Health Evaluation [APACHE] score and the Sequential Organ Failure Assessment [SOFA] score) are restricted to only a handful of vari- ables, because humans must enter and tally the scores. But data could instead be drawn directly from EHRs or claims databases, allow- ing models to use thousands of rich predictor variables.”


      Predicting the Future — Big Data, Machine Learning, and Clinical Medicine
Obermeyer Z, Emanuel EJ
N Engl J Med 375;13 September 29, 2016
    • “If AI is an unreimbursed business expense, it changes the potential return on investment for all of the outside money that continues to pour into companies creating products using AI. When even the troglodytes of radiology see a future with AI benefiting both patients and the specialty, we should perhaps temper our enthusiasm because of these financial realities. The barriers to entry for new products and services in health care are high, and for good reason. But without the promise of governmental largesse or large inflows of reimbursements from private payers, vendors may take a pass on investing resources in radiology or health care-specific applications for AI.”


      Artificial Intelligence: Who Pays and How?
Schoppe, Kurt
Journal of the American College of Radiology (in press)
    • “By now, it’s almost old news: big data will transform medicine. It’s essential to remember, however, that data by themselves are useless. To be useful, data must be analyzed, interpreted, and acted on. Thus, it is algorithms — 
not data sets — that will prove transformative.”


      Predicting the Future — Big Data, Machine Learning, and Clinical Medicine
Obermeyer Z, Emanuel EJ
N Engl J Med 375;13 September 29, 2016 


    • “Most computer-based algorithms in medicine are “expert systems” — rule sets encoding knowledge on a given topic, which are applied to draw conclusions about specific clinical scenarios, such as detecting drug interactions or judging the appropriateness of obtaining imaging. Expert systems work the way an ideal medical student would: they take general principles about medicine and apply them to new patients.”


      Predicting the Future — Big Data, Machine Learning, and Clinical Medicine
Obermeyer Z, Emanuel EJ
N Engl J Med 375;13 September 29, 2016
    • “Machine learning, conversely, approaches problems as a doctor progressing through residency might: by learning rules from data. Starting with patient-level observations, algorithms sift through vast numbers of variables, looking for combinations that reliably predict outcomes.”


      Predicting the Future — Big Data, Machine Learning, and Clinical Medicine
Obermeyer Z, Emanuel EJ
N Engl J Med 375;13 September 29, 2016


    • Canadian Association of Radiologists White Paper on Artificial Intelligence in Radiology
An Tang et al. 
Canadian Association of Radiologists Journal 69 (2018) 120e135


    • Canadian Association of Radiologists White Paper on Artificial Intelligence in Radiology
An Tang et al. 
Canadian Association of Radiologists Journal 69 (2018) 120e135
    • “One of the first and most significant hurdles to getting a CPT code is the need for peer-reviewed research in the United States that demonstrates both the efficacy and safety of the procedure. The second hurdle is the need for the procedure to be widely performed by a large number of physicians in the United States. These two requirements will prevent many AI software programs from achieving a CPT code. But, let us presume that at least one AI tool makes the cut and gets a CPT code. It will then have to be valued by the Relative Value Scale Update Committee (RUC) to get assigned RVUs.”


      Artificial Intelligence: Who Pays and How?
Schoppe, Kurt
Journal of the American College of Radiology (in press)
    • “The RUC values the professional component of a medical procedure based upon the work of a physician. The primary components of physi- cian work include the time it takes to perform the service, the level of technical skill required, and the mental effort and judgment necessary. For most AI tools I have seen, there is minimal to no physician work. Some AI processes run in the background and “prioritize” CT scans based on characteristics that may indicate an emergent finding. There is no physician work in this. Some AI processes may highlight specific imaging findings for the radiologist. This type of operation would be considered similar to computer-aided detection, and so would be valued similarly to prior CPT codes for computer-aided detection used in chest radiographs or mammography, though much of this work is either unreimbursed or bundled into the actual diagnostic procedure (eg, mammography and breast MRI).”

      
Artificial Intelligence: Who Pays and How?
Schoppe, Kurt
Journal of the American College of Radiology (in press)
    • “My opinion is that neither the government nor private payers will reimburse physicians and hospitals for using AI-driven software products. I believe that we will all purchase AI tools and treat them as an unreimbursed business expense. We will invest in AI software to ensure we are delivering high- quality work, to increase our efficiency, and to simplify clerical type tasks. In this way, paying for AI tools will merely be a cost of doing business like other operational expenses we incur.”


      Artificial Intelligence: Who Pays and How?
Schoppe, Kurt
Journal of the American College of Radiology (in press)
    • “Radiomics is a process that extracts a large number of quantitative features from medical images. It can potentially be applied to any medical condition, but it is currently applied mostly in oncology for quantification of tumour phenotype and for development of decision support tools. Deep learning and convolutional neural networks have the potential to automatically extract the significant features from images to help predict an important outcome (eg, cancer-specific mortality).”

      
Canadian Association of Radiologists White Paper 
on Artificial Intelligence in Radiology
An Tang et al. 
Canadian Association of Radiologists Journal 69 (2018) 120e135 

    • “With technological advances in computer science, it is anticipated that an increasing number of repetitive tasks will be automated over time. The PACS of all hospitals contain large imaging datasets with matching descriptions within radiology reports that can be used to perform ML on very large scale. The interactions between radiology images and their reports have been used to train ML for automated detection of disease in images [56]. Of note, a recent review of deep learning revealed that many recent applications in medical image analysis focus on 2D convolutional neural networks which do not directly leverage 3D information [57]. While 3D convolutional neural networks are emerging for analysis of multiplanar imaging (eg, CT), further research will be required to analyze multiparametric imaging examinations (eg, MRI).”


      Canadian Association of Radiologists White Paper 
on Artificial Intelligence in Radiology
An Tang et al. 
Canadian Association of Radiologists Journal 69 (2018) 120e135 

    • “AI techniques have been steadily developed since 1955 but recently have undergone a resurgence due to breakthrough performance arising from a combination of factors: wide availability of labeled data, advances in neural network architectures, and availability of parallel computing hardware. In radiology, AI applications currently focus on anomaly detection, segmentation, and classification of images. Familiarity with the terminology and key concepts in this field will allow the radiology community to critically analyze the opportunities, pitfalls, and challenges associated with the introduction of these new tools. Radiologists should become actively involved in research and development in collaboration with key stakeholders, scientists, and industrial partners to ensure radiologist oversight in the definition of use cases and validation process, and in the clinical application for patient care. Residency programs should integrate health informatics and computer science courses in AI in their curriculum.”


      Canadian Association of Radiologists White Paper on Artificial Intelligence in Radiology
An Tang et al. 
Canadian Association of Radiologists Journal 69 (2018) 120e135 

    • ”ML algorithms evolve as they are exposed to more data. Nearly all ML algorithms used to analyze the pixel data of radiology examinations ‘‘learn’’ to give a specific answer by evaluating a large number of exams that have been hand-labeled. For example, a ML algorithm to detect lung nodules on chest radiographics will be trained by analyzing thousands of chest radiographs that humans have labeled as being normal, or as having nodules in the lungs.” 


      Canadian Association of Radiologists White Paper 
on Artificial Intelligence in Radiology
An Tang et al. 
Canadian Association of Radiologists Journal 69 (2018) 120e135 

    • ” Representation learning refers to a subtype of ML in which no ‘‘hand-crafted’’ features are provided. Instead, the computer algorithm learns the features required to classify the provided data. The amount of training data has an impact on the performance of ML algorithms: adding data generally improves performance .If provided enough training data, systems based on representation learning may achieve better performance than traditional ML systems that incorporate ‘‘hand-crafted’’ features.” 


      Canadian Association of Radiologists White Paper 
on Artificial Intelligence in Radiology
An Tang et al. 
Canadian Association of Radiologists Journal 69 (2018) 120e135 

    • “ML algorithms evolve as they are exposed to more data. Nearly all ML algorithms used to analyze the pixel data of radiology examinations ‘‘learn’’ to give a specific answer by evaluating a large number of exams that have been hand-labeled. For example, a ML algorithm to detect lung nodules on chest radiographics will be trained by analyzing thousands of chest radiographs that humans have labeled as being normal, or as having nodules in the lungs. 
.” 


      Canadian Association of Radiologists White Paper 
on Artificial Intelligence in Radiology
An Tang et al. 
Canadian Association of Radiologists Journal 69 (2018) 120e135 

    • “Neural networks are the algorithms that are most commonly used for image analysis today. The name refers to their design inspired by neurons in a brain. These neural networks are composed of layers of connected nodes (or neurons) and may contain thousands to millions of nodes. Each node receives information from some pattern of other nodes. If the information that node receives crosses a threshold, that node then sends a signal out to other groups of nodes. These outputs are weighted, in that they send a small signal out when they are weakly stimulated, and a strong signal when they receive appropriate input.” 


      Canadian Association of Radiologists White Paper 
on Artificial Intelligence in Radiology
An Tang et al. 
Canadian Association of Radiologists Journal 69 (2018) 120e135 

    • “The learning process may occur either via supervised learning, in which a training set of data contains annotations by humans to match the desired output of the algorithm, or unsupervised learning, in which the training data do not contain annotations and the algorithm seeks to cluster or organize the data to reveal underlying patterns.” 


      Canadian Association of Radiologists White Paper 
on Artificial Intelligence in Radiology
An Tang et al. 
Canadian Association of Radiologists Journal 69 (2018) 120e135 

    • “Almost all ML for analysis of radiology exams is currently performed via supervised learning which requires appropriately labeled training data. This highlights 2 challenges: 1) adequate labeling of key imaging find- ings, a tedious and time-consuming process, and 2) appropriate definition of ground truth (eg, radiology report, pathology report, clinical outcomes). Proper training of ML algorithms will require new ways to label data or to deal with loosely labeled data. Ground truth, which is often found on a continuum (eg, ranging from normal, probably normal, indeterminate, probably abnormal to definitely abnormal) may require artificial clustering into normal vs abnormal.”

      
Canadian Association of Radiologists White Paper 
on Artificial Intelligence in Radiology
An Tang et al. 
Canadian Association of Radiologists Journal 69 (2018) 120e135 

    • “ML algorithms often require a large amount of data to ‘‘learn’’ to provide useful answers, and processing these data requires significant computing power. The rapid in- crease in power of graphical processing units (GPUs), initially created for accelerating computer graphics, such as used in gaming, have provided flexible hardware for ML purpose. The access to computational power and large training datasets has made these algorithms cost effective.” 


      Canadian Association of Radiologists White Paper 
on Artificial Intelligence in Radiology
An Tang et al. 
Canadian Association of Radiologists Journal 69 (2018) 120e135 

    • “Therefore, it would be necessary to create freely available, modality-specific sandboxes for computer scientists to get a ‘‘feel’’ for the data, which will inform the design process. For example, in the computer vision community, the publicly available ImageNet dataset has been used to produce numerous breakthroughs in machine learning for image recognition, but many of these advances are not directly applicable to medical imaging.”

      
Canadian Association of Radiologists White Paper 
on Artificial Intelligence in Radiology
An Tang et al. 
Canadian Association of Radiologists Journal 69 (2018) 120e135 

    • “For many applications, expert annotations (ie, measurements, contouring, and descriptions) for training AI algorithms are very expensive to obtain. A direction of research should be to make the annotation process more efficient, which can also be done with AI. The Medical Image Computing and Computer Assisted Intervention Workshop on Large-scale Annotation of Biomedical data and Expert Label Synthesis is an example of an academic venue with this research focus.” 


      Canadian Association of Radiologists White Paper 
on Artificial Intelligence in Radiology
An Tang et al. 
Canadian Association of Radiologists Journal 69 (2018) 120e135 

    • “Development and validation of AI applications for radiology will require new thinking and approaches as it relates to collaborations and intellectual property between academic research laboratories and industrial partners. Several questions may arise in the process: 1) Who owns the data and intellectual property on the models developed jointly? 2) Should data sharing agreements be signed with individual sites or with the research consortium in the case of multicentre studies.” 


      Canadian Association of Radiologists White Paper 
on Artificial Intelligence in Radiology
An Tang et al. 
Canadian Association of Radiologists Journal 69 (2018) 120e135
    • ”Artificial intelligence (AI) is rapidly moving from an experimental phase to an implementation phase in many fields, including medicine. The combination of improved availability of large datasets, increasing computing power, and advances in learning algorithms has created major performance breakthroughs in the development of AI applications. In the last 5 years, AI techniques known as deep learning have delivered rapidly improving performance in image recognition, caption generation, and speech recognition. Radiology, in particular, is a prime candidate for early adoption of these techniques. It is anticipated that the implementation of AI in radiology over the next decade will significantly improve the quality, value, and depth of radiology’s contribution to patient care and population health, and will revolutionize radiologists’ workflows.”

      
Canadian Association of Radiologists White Paper 
on Artificial Intelligence in Radiology
An Tang et al. 
Canadian Association of Radiologists Journal 69 (2018) 120e135
Liver

    • “Hepatic artery aneurysms account for 20% of all visceral aneurysms, with 20% being intrahepatic. These can develop secondary to atherosclerotic disease. Calcified atherosclerotic disease is seen 30% of the time. At unenhanced CT, these calcifications are curvilinear and typically seen in the vessel wall . After IV contrast agent administration, the hepatic artery lumen and outpouching arising from the hepatic artery forming the aneurysm will opacify except for areas of thrombosis. An important complication of these aneurysms is rupture, with a mortality rate of 21%. Therefore, it is important to avoid potential misinterpretation of these lesions for a calcified liver lesion.”

      
Liver Calcifications and Calcified Liver Masses: Pattern Recognition Approach on CT
Madhavi Patnana et al
AJR 2018; 211:76–86
    • “Thorium dioxide was a contrast agent previously used for medical imaging between 1928 and 1955, but was discontinued in the 1960s because of its carcinogenic effects resulting in various cancers, including liver-related malignancy (e.g., cholangiocarcinoma, angiosarcoma, and HCC), around 20 years after injection. The exact cause is not known but is thought to be secondary to long-term low-dose α-irradiation leading to gene mutations . At CT, high-density deposits are typically seen in the liver, lymph nodes, and spleen and can feature a reticular pattern.”


      Liver Calcifications and Calcified Liver Masses: Pattern Recognition Approach on CT
Madhavi Patnana et al
AJR 2018; 211:76–86
    • “Cholangiocarcinoma is the second most common primary malignancy of the liver and the most common biliary tract tumor, with the highest frequency in Southeast Asia. Cholangiocarcinoma constitutes 10–15% of primary hepatic cancers and can be categorized as extrahepatic, peripheral intrahepatic, and hilar intrahepatic. Intrahepatic cholangiocarcinomas stem from the biliary system peripheral to the secondary bifurcation of the left or right hepatic ducts and are divided into three categories on the basis of their growth pattern.”


      Liver Calcifications and Calcified Liver Masses: Pattern Recognition Approach on CT
Madhavi Patnana et al
AJR 2018; 211:76–86
    • “Hepatocellular carcinoma (HCC) constitutes over 90% of all primary hepatic malignancies, with approximately 80% a complication of hepatitis B and C infection. Additional risk factors include alcoholic and nonalcoholic fatty liver disease, smoking, α-1 antitrypsin deficiency, hemochromatosis, and environmental and dietary factors.”


      Liver Calcifications and Calcified Liver Masses: Pattern Recognition Approach on CT
Madhavi Patnana et al
AJR 2018; 211:76–86
    • “The frequency of HCC varies geographically. High-frequency areas in the world are sub-Saharan Africa, China, Hong Kong, and Taiwan. Low-frequency areas include North and South America, Australia, parts of the Middle East, and most of Europe. In the United States, the frequency has been increasing over the past 20 years and is postulated to be associated with chronic hepatitis C infection.”


      Liver Calcifications and Calcified Liver Masses: Pattern Recognition Approach on CT
Madhavi Patnana et al
AJR 2018; 211:76–86
    • “Evaluation for HCC is optimally detected with a dynamic multiphase contrast-enhanced imaging study (CT or MRI) to assess the pattern of vascularity. Most HCCs develop in the presence of cirrhosis but can also occur without cirrhosis. HCC has various presentations, including a single mass (most common), multifocal masses, or a diffusely infiltrative tumor. The classic imaging pattern of solitary or multifocal HCC on CT or MRI (with extracellular contrast agents) is late arterial hypervascularity with portal venous phase or late washout and an enhancing peripheral capsule.”


      Liver Calcifications and Calcified Liver Masses: Pattern Recognition Approach on CT
Madhavi Patnana et al
AJR 2018; 211:76–86
    • “Vascular invasion can be seen, particularly involving the portal vein more frequently than the hepatic veins, and appears as an intraluminal tumor growth . These tumors also have hemorrhagic areas and necrosis and can develop dystrophic calcification, although it is rare. Calcification is more common after liver-directed therapy.”


      Liver Calcifications and Calcified Liver Masses: Pattern Recognition Approach on CT
Madhavi Patnana et al
AJR 2018; 211:76–86
    • “The fibrolamellar variant of HCC is much less common and has different imaging characteristics compared with HCC. This tumor tends to occur in a younger population (5–35 years old) and affects both male and female patients equally. There is no connection with chronic viral hepatitis or cirrhosis. It has a better prognosis compared with HCC.”


      Liver Calcifications and Calcified Liver Masses: Pattern Recognition Approach on CT
Madhavi Patnana et al
AJR 2018; 211:76–86
    • “Nodal metastatic disease is common (50–65%) and most frequently occurs in the hepatoduodenal ligament, retroperitoneum, pelvis, and mediastinum. Distant metastatic disease occurs in approximately 20–30% of cases, with the most common sites being in the lung, peritoneum, and adrenal glands.”

      
Liver Calcifications and Calcified Liver Masses: Pattern Recognition Approach on CT
Madhavi Patnana et al
AJR 2018; 211:76–86
    • “Biliary cystadenomas are uncommon multilocular potentially premalignant cystic tumors. They are nearly exclusively seen in women of middle age and are typically intrahepatic, more commonly found in the right hepatic lobe (55%). They can vary in size, ranging up to 35 cm . Patients can present with pain or biliary obstruction. At pathologic analysis, the cyst wall is similar to ovarian stroma (single layer of mucin-secreting cells) . The internal fluid is usually simple but can be proteinaceous, mucinous, or hemorrhagic. At CT, these lesions appear as a well-circumscribed cystic mass with internal crisscrossing septa and a thick fibrous capsule, which rarely calcifies (mural calcification).”

      
Liver Calcifications and Calcified Liver Masses: Pattern Recognition Approach on CT
Madhavi Patnana et al
AJR 2018; 211:76–86
    • “Hepatic epithelioid hemangioendothelioma is an uncommon low-to-intermediate grade malignant vascular endothelial tumor with a frequency of 1:100,000 in middle-aged patients, with a higher frequency among women. The 5-year survival rate is 43–55. In a study by Gan et al., in which 14 cases of hepatic epithelioid hemangioendothelioma were analyzed, 50% of patients had no symptoms, 21% (n = 3) had right upper quadrant pain, and 29% (n = 4) presented with weight loss.”


      Liver Calcifications and Calcified Liver Masses: Pattern Recognition Approach on CT
Madhavi Patnana et al
AJR 2018; 211:76–86
    • “At CT, these tumors may be seen as a single nodule or multiple masses. At late stages, they have a propensity to grow and coalesce (striplike sign) in a subcapsular or peripheral location. At unenhanced CT, these lesions are hypodense. Capsular retraction, central hypodensity, and dystrophic calcification can also be observed with these lesions at CT. Makhlouf et al detected dystrophic calcifications in approximately 20% of lesions.”

      
Liver Calcifications and Calcified Liver Masses: Pattern Recognition Approach on CT
Madhavi Patnana et al
AJR 2018; 211:76–86
    • “Hepatocellular adenomas are uncommon benign epithelial liver neoplasms most commonly seen in young women using oral contraceptives or in men using anabolic steroids. The association between adenomas and contraceptive use was rst described by Baum et al. in the early 1970s. Additional risk factors include glycogen storage disease, obesity, and familial polyposis.”

      
Liver Calcifications and Calcified Liver Masses: Pattern Recognition Approach on CT
Madhavi Patnana et al
AJR 2018; 211:76–86
    • “They are often in the right hepatic lobe and typically present as one lesion (70–80% of cases) ranging in size from 1 to 30 cm. Three types of hepatic adenomas have been classi- fied (according to genotype and phenotype), including adenomas with in ammatory features, mutations in the HNF-1α gene, or mutations in β-catenin.”

      
Liver Calcifications and Calcified Liver Masses: Pattern Recognition Approach on CT
Madhavi Patnana et al
AJR 2018; 211:76–86
    • “The inflammatory subtype is the most common (40–50% of cases), mainly seen in young female patients using oral contracep- tives; it is also associated with obesity. The risk of bleeding is the highest in this subtype (approximately 30%), with a low malignancy risk.”

      
Liver Calcifications and Calcified Liver Masses: Pattern Recognition Approach on CT
Madhavi Patnana et al
AJR 2018; 211:76–86
    • “The second most common subtype is the HNF-1α-mutated hepatocellular adenoma (30–35% of cases). This subtype has the lowest complication rate, with essentially no risk for malignancy and minimal risk of bleeding. The third subtype is the β-catenin-mutated hepatocellular adenoma (10–15% of cases) and is more common in men, with an increased association with anabolic steroids, glycogen storage disease, and familial adenomatosis polyposis.” 


      Liver Calcifications and Calcified Liver Masses: Pattern Recognition Approach on CT
Madhavi Patnana et al
AJR 2018; 211:76–86
    • “The malignancy risk to hepatocellular carcinoma (HCC) is 5–10%; this subtype is considered to have the highest association of malignancy, because it is considered a borderline lesion between adenoma and carcinoma. Bleeding can occur, but the frequency is unknown.”

      
Liver Calcifications and Calcified Liver Masses: Pattern Recognition Approach on CT
Madhavi Patnana et al
AJR 2018; 211:76–86
    • “Hepatic adenomatosis occurs when there are 10 or more adenomas along with mutations of the HNF-1α gene. This can be seen in both sexes with or without oral contraceptive usage.”

      
Liver Calcifications and Calcified Liver Masses: Pattern Recognition Approach on CT
Madhavi Patnana et al
AJR 2018; 211:76–86
    • “In general, adenomas are hypo- to isodense at unenhanced CT, with 80% of lesions showing hyperenhancement on arterial contrast- enhanced images. Eighty-five percent of lesions are well-demarcated, and 25% have an enhancing peripheral capsule. Some lesions have a heterogeneous appearance from acute or chronic hemorrhage. Calci cations are dystrophic and typically are eccentric in location found in areas of old hemorrhage or necrosis in 10% of patients. Single or multiple calci cations can be seen.” 


      Liver Calcifications and Calcified Liver Masses: Pattern Recognition Approach on CT
Madhavi Patnana et al
AJR 2018; 211:76–86
    • “The most common hepatic tumor is the hepatic hemangioma, which has a frequency of 0.4–20%. These benign neoplasms can be diagnosed at any age, but are more common among female patients than male patients (ratio, 3:1) [28–33]. Hem- angiomas can vary in size; those larger than 5 cm are categorized as giant hemangiomas The cause is poorly understood, but they are considered vascular malformations. Their size can be affected by hormones (estrogen and progesterone) .”

      
Liver Calcifications and Calcified Liver Masses: Pattern Recognition Approach on CT
Madhavi Patnana et al
AJR 2018; 211:76–86
    • “Larger hemangiomas can have complications resulting in abdominal pain, fullness, thrombosis, and brosis or have focal stromal calcifications, which are frequently coarse and large. Calci cations are vi- sualized in 10–20% of hemangiomas and typically located in areas of central fibrosis. A much less common appearance includes the presence of phleboliths .”

      
Liver Calcifications and Calcified Liver Masses: Pattern Recognition Approach on CT
Madhavi Patnana et al
AJR 2018; 211:76–86
    • “Simple hepatic cysts are common nonmalignant lesions that have no biliary system communication, with an estimated 2.5% frequency in the general population [26]. They are typically asymptomatic, are more common among women, and can be solitary or multiple. They have smooth thin walls with absent internal nodularity or septation. At histologic analysis, they consist of serous uid lined by a cuboidal epithelium and a thin rim of fibrous stroma. At CT, simple cysts are typically well delineated and homogeneous, with no internal or peripheral enhancement. Cyst wall calcification is uncommon.”

      
Liver Calcifications and Calcified Liver Masses: Pattern Recognition Approach on CT
Madhavi Patnana et al
AJR 2018; 211:76–86
    • “The most frequent cause of focal calcified liver lesions is inflammation,with granulomatous disease being the most common cause. Most occurrences of granulomatous disease in the United States are attributed to histoplasmosis, sarcoidosis, and tuberculosis.”

      
Liver Calcifications and Calcified Liver Masses: Pattern Recognition Approach on CT
Madhavi Patnana et al
AJR 2018; 211:76–86
    • “TB is one of the most prevalent causes of morbidity and death worldwide, partic- ularly in low- or middle-income countries [5]. Hepatic TB typically occurs from 11 to 50 years of age, with a peak occurrence in the second decade and a male-to-female ratio of 2:1 [6]. The best imaging examination for active hepatic TB diagnosis is contrast- enhanced CT [7]. There are ve patterns of hepatic TB as classi ed by Levine: military TB, concomitant lung and liver disease, primary (isolated) TB, tubercular hepatic abscess, and cholangitis.” 


      Liver Calcifications and Calcified Liver Masses: Pattern Recognition Approach on CT
Madhavi Patnana et al
AJR 2018; 211:76–86
    • “ Some parasitic infections that can cause liver lesions with associated hepatic calcifications include hydatid disease, schistosomiasis, and fascioliasis.” 


      Liver Calcifications and Calcified Liver Masses: Pattern Recognition Approach on CT
Madhavi Patnana et al
AJR 2018; 211:76–86
    • “Cystic echinococcosis can present as simple cysts or solid-appearing lesions [15]. Calcifi cations of the cyst can appear curvilinear, ringlike, or densely calcified [14, 19]. Four ra- diologic patterns have been described. Type I is a well-defined low-density simple-ap- pearing cyst lacking internal contents. Type II has three subtypes .”

      
Liver Calcifications and Calcified Liver Masses: Pattern Recognition Approach on CT
Madhavi Patnana et al
AJR 2018; 211:76–86
Musculoskeletal

    • Chordomas are most common in individuals aged 40–70 years, and occur twice as often in males as in females [1]. They are the most common primary tumour of the sacrum, arising from notochordal rests. 50–60% of chordomas occur in the sacrococcygeal region [1]. Chordoma is a low-grade tumour but can cause significant morbidity and mortality with local recurrence. Metastatic disease is uncommon but is seen more frequently in sacral chordomas than in skull base chordomas.
 Imaging features of primary and secondary malignant tumours of the sacrum


      E Thornton et al.The British Journal of Radiology 2012 85:1011, 279-284 
    • “Sacral chordoma usually arises from the third, fourth or fifth sacral vertebra in the midline or paramedian location, and is often seen as a large destructive osteolytic lesion with extraosseous extension When there is extraosseous extension, the tumour can be seen to extend exophytically into the pre-sacral region or sacral canal, resulting in a “mushroom” or “dumbbell” shape Internal calcifications are frequently seen on plain radiographs and CT .”


      Imaging features of primary and secondary malignant tumours of the sacrum
E Thornton et al.The British Journal of Radiology 2012 85:1011, 279-284 
    • “The tumour can spread over several segments, with or without involvement of the intervertebral discs. In >50% of cases, there are areas of decreased attenuation within the tumour on CT, which reflect myxoid-type tissue presented pathologically. A fibrous pseudocapsule is common, which is of increased attenuation relative to the rest of the tumour .”


      Imaging features of primary and secondary malignant tumours of the sacrum
E Thornton et al.The British Journal of Radiology 2012 85:1011, 279-284 
    • “Chordomas lie predominantly in the midline, unlike chondrosarcomas which lie eccentrically. Chordomas tend to arise from the lower sacral segments or sacrococcygeal region; by contrast, chondrosarcomas generally arise from the mid to upper sacrum. When calcification is present in chordomas, it is amorphous and predominates in the periphery of the lesion, as opposed to the ring-and-arc calcification seen in chondrosarcomas from the chondroid matrix. When present, dense amorphous osteoid matrix is a characteristic finding in osteosarcomas .”


      Imaging features of primary and secondary malignant tumours of the sacrum
E Thornton et al.The British Journal of Radiology 2012 85:1011, 279-284 
    • Ewing sarcoma (ES) is a rare, highly malignant tumor that is most common in childhood and has an incidence of 0.5 to 2 cases/million. Since its first description by James Ewing in 1921, it was suggested that Ewing sarcoma shared characteristics with primitive neuroectodermal tumors and Askin tumors . Nevertheless, the etiologies of these tumors were not regarded to be similar until the 1990 discovery of a common genetic translocation in all these tumors led to their classification in the same family of ESs. Arising from soft or hard tissues, primitive neuroectodermal tumors and ESs share t(11;22) translocation but differ by their degree of differentiation.
 Ewing Sarcoma of the Chest Wall: Prognostic Factors of Multimodal

      Therapy Including En Bloc Resection
Provost B et
 The Annals of Thoracic Surgery
Volume 106, Issue 1, July 2018, Pages 207-213

    • Thoracic localization of these sarcomas most often involves the ribs, and it is characterized by indolent progression. As a consequence, these tumors are often diagnosed at a locally advanced stage, with massive pleural cavity involvement (ie, consistent with the description by Askin and colleagues, or even at a metastatic stage (in 25% of cases).


      Ewing Sarcoma of the Chest Wall: Prognostic Factors of Multimodal Therapy Including En Bloc Resection
Provost B et
 The Annals of Thoracic Surgery
Volume 106, Issue 1, July 2018, Pages 207-213

    • The Ewing’s sarcoma family of tumours (ESFT) is an aggressive form of childhood cancer, which include classic Ewing’s sarcoma, Askin tumour, and peripheral primitive neuroectodermal tumour. While significant progress has been made in the diagnosis and treatment of localised disease over the past 30 years, there is much room for improvement.Before chemotherapy was introduced,about 10% of patients with Ewing’s sarcoma survived.Progress since then has been dramatic, with 75% of patients with localised tumours now surviving. 


      Ewing’s sarcoma 
Naomi J Balamuth, Richard B Womer 
 Lancet Oncol 2010; 11: 184–92
Practice Management

    • “For 2018, one can look at the top trends through the lens of a small generation of people with the potential to have a disproportionate impact on the world—Generation Z. Generation Z is composed of people who are currently 18 to 22 years old and in many ways they have more similar- ities with Generation X than with Millennials. Generation Z tends to be practical and driven, and this is reflected in the top 10 trends for 2018 that I will discuss.”


      Meet Generation Z: Top 10 Trends of 2018
Tina Wells, Elliot K. Fishman, Karen M. Horton, Steven P. Rowe
Publication stage: In Press Corrected Proof
Journal of the American College of Radiology
    • “Seeking Purpose, with Purpose. Companies appealing to con- sumer’s emotions and doing good is nothing new. However, it is now a virtual expectation that companies engage in purposes beyond purely making money. This phenomenon has put the responsibility on com- panies to adopt and promote purposes. Indeed, many com- panies now use marketing, particularly online and social media-based marketing, to define what they stand for instead of only focusing on their products. In addition to consumers choosing companies with purposes, many members of Generation Z who are entering the workforce are looking for companies that align with their values and passions. Beyond loving their jobs, Generation Z workers also want to love the companies they are working for. The health care industry should naturally be able to appeal to consumers and prospective employees on this basis given the centrality of patient well- being to the health care enterprise.”

      
Meet Generation Z: Top 10 Trends of 2018
Tina Wells, Elliot K. Fishman, Karen M. Horton, Steven P. Rowe
Publication stage: In Press Corrected Proof
Journal of the American College of Radiology
    • “Social Shopping. This is perhaps the biggest trend of 2018. Social media companies are seamlessly incorporating ads into their platforms allowing users to click and purchase items while browsing. These ads may have the appearance of other users’ posts. From the sign-up information and posting habits of users, companies can determine ages, interests, locations, de- mographics, and behaviors. Taken together, this informa- tion allows ads that are specifically tailored to individual users, providing a driving force for improved return on investment for those taking out the ads. This may represent an opportunity for radiology to reach out to the members of Generation Z and provide important information on subjects such as screening tests.”


      Meet Generation Z: Top 10 Trends of 2018
Tina Wells, Elliot K. Fishman, Karen M. Horton, Steven P. Rowe
Publication stage: In Press Corrected Proof
Journal of the American College of Radiology
    • “However, some women are concerned that they will not have access to mentoring by men and that men will not be willing to be alone with them. In health care, in which mentorship is profoundly important and the majority of many graduating medical school classes are women, we must ensure that women continue to have access to mentorship from men in the #MeToo era.”

      
Meet Generation Z: Top 10 Trends of 2018
Tina Wells, Elliot K. Fishman, Karen M. Horton, Steven P. Rowe
Publication stage: In Press Corrected Proof
Journal of the American College of Radiology
    • “Artificial Intelligence (AI) Adoption. Machine learning algorithms and computer programs are becoming progressively smarter, and we are increasingly relying on them to make our lives easier. Big companies are investing heavily in AI. Chatbots are playing an increasing role in customer service, and we are making many of our purchases through social media. Elon Musk has gone as far as to start a company focused on the creation of ultra-high bandwidth interfaces that can connect the human brain to computers. We seem to have reached the tipping point where consumers undestand AI and are increasingly accepting of the ways in which it might improve their lives. The AI revolution in health care is just beginning, but eventually every facet of the health care in- dustry will be impacted by machine learning algorithms that can make sense of immense data sets and generate predictive analytics based on that data. Radiology and pathology are likely to be the two specialties most impacted early in the AI transition.”


      Meet Generation Z: Top 10 Trends of 2018
Tina Wells, Elliot K. Fishman, Karen M. Horton, Steven P. Rowe
Publication stage: In Press Corrected Proof
Journal of the American College of Radiology
    • “Total Transparency. Consumers highly value transparency—and when companies try to hide something, it invariably comes to light anyway. Earning trust from customers is a way to sustain long-lasting relationships. Con- sumers want to know why things cost what they do and for com- panies to show them what efforts they are making to remove extraneous things from products to make them cheaper. Outside of a company’s customer base, shareholders and employees also value transparency (for example, companies can choose to publish the salaries of all of their employees and the formulae used to calculate those salaries). Of course, these same attitudes are relevant to the health care industry, in which many con- sumers want complete trans- parency from their health care providers so that they can participate as active stewards of their own health. In addition, consumers are interested in health care costs and will demand increasing transparency regarding how those costs are derived.”


      Meet Generation Z: Top 10 Trends of 2018
Tina Wells, Elliot K. Fishman, Karen M. Horton, Steven P. Rowe
Publication stage: In Press Corrected Proof
Journal of the American College of Radiology
    • “Broadly, Generation Z seems to be following in the footsteps of Gener- ation X in being practical and job-driven. In addition to those characteristics, Generation Z also tends to follow the trends of seeking purpose, early adoption of AI, and placing an emphasis on self-care. The health care industry should be interested in having Generation Z employees on board to help “steer the ship” in new directions. Large hospital systems often have huge amounts of important content, but knowing how to get that content to the people who need it is an evolving process. Nimbly responding to changing trends is difficult for any large entity such as a health care system, but strategically placing members of Generation Z in the right positions may be helpful.”


      Meet Generation Z: Top 10 Trends of 2018
Tina Wells, Elliot K. Fishman, Karen M. Horton, Steven P. Rowe
Publication stage: In Press Corrected Proof
Journal of the American College of Radiology
    • “Radiology must become more proactive in meeting the needs of Generation Z. Not only are the members of Generation Z going to be responsible for their own health care, but they will inevitably be involved in the health care of elderly relatives, even to the extent that they may become the focal point for ensuring proper health care for their families.”

      
Meet Generation Z: Top 10 Trends of 2018
Tina Wells, Elliot K. Fishman, Karen M. Horton, Steven P. Rowe
Publication stage: In Press Corrected Proof
Journal of the American College of Radiology
    • ”With the recent acceleration in technological change, it is imperative that companies be nimble and react quickly to embrace transformational changes. In the end, all of us want to serve our guests or our patients with the utmost care, and we need to anticipate their needs in order to accomplish that. With the rapidly changing landscape in digital technology, both the hospitality and the health care industries need to take bold steps to make technology an integral part of why our guests or patients choose us.”

      
The Incipient Digital Revolution in Hospitality and Health Care: Digital Is Hospitable
Brian King, Elliot K. Fishman, Karen M. Horton, Steven P. Rowe 
JACR (in press) 2018
    • “This pervasive connectivity also al- lows massive data collection, opening the possibility of applying artificial intelligence to predictive analytics to enable improved anticipation of customer needs and a 
better customer experience. These principles have important implications for any digital strategy, whether it be in hospitality or health care.”

      
The Incipient Digital Revolution in Hospitality and Health Care: Digital Is Hospitable
Brian King, Elliot K. Fishman, Karen M. Horton, Steven P. Rowe 
JACR (in press) 2018
    • “We refer to this idea as “omni-channel” functionality, and it is a core precept of our digital strategy. Maintaining 
omni-channel functionality across platforms will become an increasingly important consideration for the health care industry as more frontline tasks such as scheduling and dissemination of patient preparation instructions are handled through the Internet.” 


      The Incipient Digital Revolution in Hospitality and Health Care: Digital Is Hospitable
Brian King, Elliot K. Fishman, Karen M. Horton, Steven P. Rowe 
JACR (in press) 2018
    • “We want to make the agent of the future more predictive and to make the guest interaction as seamless as possible. We are working on a means to provide pertinent customer data, which we have permission from our customers to use, to the right agent at the right time, facilitating customer choices and potentially delighting customers with the kind of extraordinary service that creates long-term value. For example, customers can tell us their room-type choices in advance, such as a high floor or a king bed, and not have to repeat that information every time they interact across our channels. Furthermore, we are working to free up our customer service teams from tasks that can be automated so that they can focus on creating transformative and unique experiences for our guests.” 


      The Incipient Digital Revolution in Hospitality and Health Care: Digital Is Hospitable
Brian King, Elliot K. Fishman, Karen M. Horton, Steven P. Rowe 
JACR (in press) 2018
    • “Just as in the hospitality industry, patient expectations when interacting with radiology are likely to evolve to expect omni-channel functionality. Scheduling, rescheduling, asking questions, obtaining results, and other interactions should be made available across platforms, and the experience should be seamless regardless of how the functionality is accessed. To keep up with changing consumer expectations for other in- dustries, radiology will need to gradually abandon the model in which rooms of schedulers are waiting for phone calls from patients and will need to shift as many services as possible to online resources with this omni-channel functionality.” 


      The Incipient Digital Revolution in Hospitality and Health Care: Digital Is Hospitable
Brian King, Elliot K. Fishman, Karen M. Horton, Steven P. Rowe 
JACR (in press) 2018
    • “Ultimately, high tech can be high touch. Just as in the hospi- tality industry, the coming shift of radiology resources toward digital technology and the increasing role that Internet-based platforms will play in many aspects of patient interaction doesn’t mean that health care has to become impersonal or monolithic. Indeed, having technology handle tasks such as scheduling and having algorithms available to answer relatively simple frequently asked questions will free those who work in health care to provide higher value to patients. For example, instead of a radiologist answering a question about patient preparation for an upcoming imaging test, the radiologist may now have time to consult with the patient about the implications of the imaging test results once the examination is done.” 


      The Incipient Digital Revolution in Hospitality and Health Care: Digital Is Hospitable
Brian King, Elliot K. Fishman, Karen M. Horton, Steven P. Rowe 
JACR (in press) 2018
Syndromes in CT

    • " Because most of these features are directly assessed via preoperative abdominal imaging, in particular computed tomography (CT) scanning, a clear mutual understanding among surgeons and radiologists of those adrenal tumor features impacting operative approach selection is vital for planning adrenal surgery. In this context, we review the preoperative CT imaging features that specifically inform adrenalectomy approach selection, provide illustrative examples from our institution's imaging and surgical archives, and provide a stepwise guide to both the open and laparoscopic adrenalectomy approaches.”


      What the radiologist needs to know: the role of preoperative computed tomography in selection of operative approach for adrenalectomy and review of operative techniques.
Rowe SP1, Lugo-Fagundo C1, Ahn H1, Fishman EK1, Prescott JD2.
Abdom Radiol (NY). 2018 Jul 2. doi: 10.1007/s00261-018-1669-y. (in press)
    • “Sarcoidosis is a multisystem granulomatous disorder characterized by development of noncaseating granulomas in various organs. Although the etiology of this condition is unclear, environmental and genetic factors may be substantial in its pathogenesis. Clinical features are often nonspecific, and imaging is essential to diagnosis. Abnormalities may be seen on chest radiographs in more than 90% of patients with thoracic sarcoidosis. Symmetric hilar and mediastinal adenopathy and pulmonary micronodules in a perilymphatic distribution are characteristic features of sarcoidosis. Irreversible pulmonary fibrosis may be seen in 25% of patients with the disease. Although sarcoidosis commonly involves the lungs, it can affect virtually any organ in the body.” 


      Sarcoidosis from Head to Toe: What the Radiologist Needs to Know 
Ganeshan D et al. 
RadioGraphics 2018; 38:1180–1200
    • * Approximately 10%–30% of patients with sarcoidosis have ocular abnormalities, cutaneous lesions, or peripheral lymph- adenopathy. 
* Well-defined micronodules measuring 2–5 mm with a perilymphatic distribution along the bronchovascular bundles, interlobular septa, interlobar fissures, and subpleural regions are characteristic CT findings seen in pulmonary sarcoidosis. 



      Sarcoidosis from Head to Toe: What the Radiologist Needs to Know 
Ganeshan D et al. 
RadioGraphics 2018; 38:1180–1200
    • Although sarcoidosis typically manifests with symmetric hilar and mediastinal adenopathy, perilymphatic micronodules, and brotic changes, it can be associated with a wide range of atypical imaging features. Sarcoidosis may manifest as asymmetric or unilateral hilar or mediastinal adenopathy, which can mimic lymphoma, metastatic adenopathy, tuberculosis, or other granulomatous disorders. Similarly, a spectrum of atypical pulmonary parenchymal abnormalities have been described in sarcoidosis, including mass-like opacities, con uent alveolar opacities (alveolar sarcoid), bulky confluent pulmonary masses, ground-glass opacities, interlobular septal thickening, fibrocystic changes, and miliary opacities.

      
Sarcoidosis from Head to Toe: What the Radiologist Needs to Know 
Ganeshan D et al. 
RadioGraphics 2018; 38:1180–1200
    • “Familial clustering in sarcoidosis has been reported in 4%–17% of cases . Sverrild et al reported an 80-fold increased risk of devel- oping sarcoidosis in monozygotic twins, which further lends support to genetic susceptibility for sarcoidosis. Human leukocyte antigen type may also be important in sarcoidosis, with certain hu- man leukocyte antigen subtypes resulting in in- creased risk of its progressive form, while others may be associated with spontaneous resolution. The Sarcoidosis Genetic Analysis (SAGA) study reported that molecular cytogenetics may be important to disease susceptibility and clinical presentation in patients with sarcoidosis.”


      Sarcoidosis from Head to Toe: What the Radiologist Needs to Know 
Ganeshan D et al. 
RadioGraphics 2018; 38:1180–1200
    • “Sarcoidosis is an idiopathic systemic granulomatous disorder characterized by the development of noncaseating granulomas in various organs . It is a global disease with a worldwide incidence of 1–40 cases per 100 000 people per year and a prevalence of 0.2–64 cases per 100 000 people. Although sarcoidosis is diagnosed in people of all races, there are substantial differences in the incidence depending on race, ethnicity, age, sex, and geography. For example, the incidence is low in Japan compared with that in northern Euro- pean countries. In the United States, it is more common among African Americans (36 cases per 100 000 people) than in white peo- ple (11 per 100 000 people) (1); and it is slightly more common in women. Sarcoidosis predominantly develops in patients 25–45 years old, although children and elderly patients may be affected.” 


      Sarcoidosis from Head to Toe: What the Radiologist Needs to Know 
Ganeshan D et al. 
RadioGraphics 2018; 38:1180–1200
    • “Retroperitoneal fibrosis (RPF) is a condition characterized by the presence of inflammation and fibrosis in the retroperitoneal space. Unfortunately, no standard definition exists that clearly defines the criteria that must be present for the diagnosis of RPF. It is this ambiguity that has made formal investigation into this disease challenging and comparison of multiple different reports vulnerable to misinterpretation. As a starting point, most agree that a pathologic specimen obtained anywhere in the retroperitoneum indicating fibrosis is not sufficient for the diagnosis of RPF. Rather, the salient feature that must be present is the radiographic finding of periaortitis.”

      
Retroperitoneal Fibrosis
Paul J.ScheelJr.MD, NancyFeeleyCRNP
Rheumatic Disease Clinics of North America
Volume 39, Issue 2, May 2013, Pages 365-381
    • “There are currently 5 different diseases that lead to infrarenal periaortitis: inflammatory abdominal aortic aneurysm (IAAA), perianeurysmal retroperitoneal fibrosis, RPF, Erdheim-Chester disease (ECD), and immunoglobulin G4 (IgG4)-related disease. In most reports IAAA, perianeurysmal retroperitoneal fibrosis, and RPF have been lumped together for the purposes of analysis. ECD, while sharing some similar radiographic features, has a distinct histologic and clinical presentation.”


      Retroperitoneal Fibrosis
Paul J.ScheelJr.MD, NancyFeeleyCRNP
Rheumatic Disease Clinics of North America
Volume 39, Issue 2, May 2013, Pages 365-381
    • “ Because no standard definition exists, it is important to establish the definition to be used when reviewing the literature on RPF. The following must be present:

      1. A soft-tissue density surrounding the infrarenal aorta or iliac vessels by contrast-enhanced computed tomography (CT) or magnetic resonance imaging (MRI).

      2. Absence of a biopsy in the retroperitoneum that is positive for malignancy.

      3. Absence of a systemic, multicentric, fibrosis processing such as IgG4-related disease.”


      Retroperitoneal Fibrosis
Paul J.ScheelJr.MD, NancyFeeleyCRNP
Rheumatic Disease Clinics of North America
Volume 39, Issue 2, May 2013, Pages 365-381
    • “ RPF begins with clinical symptoms of flank pain and unexplained weight loss. Radiographically, fibrosis starts to surround the infrarenal aorta, and progresses inferiorly toward the iliac bifurcation and laterally toward the renal hilum and surrounding structures, ultimately leading to ureteral obstruction and acute renal failure. Historically, treatment has focused on relieving the obstruction with percutaneous or cystoscopic assisted placement of ureteral stents followed by more definitive resolution of ureteric obstruction with open or laparoscopic ureterolysis, with or without omental wrapping.”

      
Retroperitoneal Fibrosis
Paul J.ScheelJr.MD, NancyFeeleyCRNP
Rheumatic Disease Clinics of North America
Volume 39, Issue 2, May 2013, Pages 365-381
    • “ Patients with RPF present to medical attention in the fifth, sixth, and seventh decades of life with a mean age of 54 years. There is a slight male to female predominance with ratios ranging from 1:1 to 3:1, depending on the report.  All races appear to be affected equally. Few epidemiologic studies exist that accurately characterize the incidence and prevalence of the disease. One report from the Netherlands suggests an incidence of 0.10 per 100,000 individuals.”


      Retroperitoneal Fibrosis
Paul J.ScheelJr.MD, NancyFeeleyCRNP
Rheumatic Disease Clinics of North America
Volume 39, Issue 2, May 2013, Pages 365-381
    • “ Contrast-enhanced CT is the preferred method of imaging in the authors’ center. CT images show a rim of soft-tissue density surrounding the aorta, possibly extending from the renal vessels to the proximal iliac arteries. Typically arterial-phase, venous-phase and delayed (urogram) images are obtained if renal function permits. Arterial-phase imaging allows for excellent definition of the aorta, abdominal branches, and iliac vessels. The luminal size of the aorta can be accurately measured, and the presence or absence of aneurismal dilation allows for rapid classification. Narrowing of the renal, iliac, or mesenteric vessels may provide the clinician with valuable information pertaining to clinical symptoms of worsening or new-onset hypertension, postprandial abdominal pain, or symptoms of claudication. Arterial-phase imaging also allows for definition of crisp margins between the aorta and the soft-tissue density surrounding the aorta, allowing for accurate measurements of the size of the soft-tissue mass..”


      Retroperitoneal Fibrosis
Paul J.ScheelJr.MD, NancyFeeleyCRNP
Rheumatic Disease Clinics of North America
Volume 39, Issue 2, May 2013, Pages 365-381
    • “ RPF is a fibroinflammatory disorder of unknown etiology that surrounds the infrarenal aorta and may progress to surrounding structures. Unfortunately, despite a recent surge in the number of publications on this topic, little progress has been made in our understanding of the classification, pathophysiology, and, most importantly, the most appropriate treatment for this disease. A lack of standardized definition of disease, small numbers of patients, and differing end points in research publications has limited our efficiency in understanding the optimum treatment. Future studies should strive to develop common definitions of this disease, as has been done for other rheumatic conditions, and an emphasis should be placed on multicenter, randomized trials in an attempt to define the most effective treatment.”


      Retroperitoneal Fibrosis
Paul J.ScheelJr.MD, NancyFeeleyCRNP
Rheumatic Disease Clinics of North America
Volume 39, Issue 2, May 2013, Pages 365-381
    • Reported risk factors for retroperitoneal fibrosis
      • Radiation
      • Exposure to asbestos
      • Malignancy
      • Tuberculosis
      • Medications:
         ○ Methysergide
         ○ Ergotamine
         ○ Hydralazine
         ○ Methyldopa
         ○ Phenacetin
         ○ β-Blockers
© 1999-2018 Elliot K. Fishman, MD, FACR. All rights reserved.