Uterine Sacculation With Entrapped Placenta After Delivery.

BACKGROUND: Uterine sacculation refers to a temporary pouch or sac within the uterus that may contain the placenta or fetal parts and that may be diagnosed antepartum or after delivery. There is very limited published information about this rare condition and its management. CASES: We report two cases of uterine sacculation with entrapped placenta diagnosed immediately postpartum, managed with two different approaches. In one case, the patient underwent immediate laparotomy and placental extraction. In the second case, the patient was managed conservatively but ultimately developed signs of infection and underwent laparotomy. CONCLUSION: Uterine sacculation with entrapped placenta is a rare condition that is a potential etiology of retained placenta. Obstetric clinicians should be aware of this diagnosis and the management strategies available.

Opportunistic detection of type 2 diabetes using deep learning from frontal chest radiographs.

Deep learning (DL) models can harness electronic health records (EHRs) to predict diseases and extract radiologic findings for diagnosis. With ambulatory chest radiographs (CXRs) frequently ordered, we investigated detecting type 2 diabetes (T2D) by combining radiographic and EHR data using a DL model. Our model, developed from 271,065 CXRs and 160,244 patients, was tested on a prospective dataset of 9,943 CXRs. Here we show the model effectively detected T2D with a ROC AUC of 0.84 and a 16% prevalence. The algorithm flagged 1,381 cases (14%) as suspicious for T2D. External validation at a distinct institution yielded a ROC AUC of 0.77, with 5% of patients subsequently diagnosed with T2D. Explainable AI techniques revealed correlations between specific adiposity measures and high predictivity, suggesting CXRs' potential for enhanced T2D screening.

Gynecologic oncology tumor board: the central role of the radiologist.

This manuscript is a collaborative, multi-institutional effort by members of the Society of Abdominal Radiology Uterine and Ovarian Cancer Disease Focus Panel and the European Society of Urogenital Radiology Women Pelvic Imaging working group. The manuscript reviews the key role radiologists play at tumor board and highlights key imaging findings that guide management decisions in patients with the most common gynecologic malignancies including ovarian cancer, cervical cancer, and endometrial cancer.

Liver Fibrosis, Fat, and Iron Evaluation with MRI and Fibrosis and Fat Evaluation with US: A Practical Guide for Radiologists.

Quantitative imaging biomarkers of liver disease measured by using MRI and US are emerging as important clinical tools in the management of patients with chronic liver disease (CLD). Because of their high accuracy and noninvasive nature, in many cases, these techniques have replaced liver biopsy for the diagnosis, quantitative staging, and treatment monitoring of patients with CLD. The most commonly evaluated imaging biomarkers are surrogates for liver fibrosis, fat, and iron. MR elastography is now routinely performed to evaluate for liver fibrosis and typically combined with MRI-based liver fat and iron quantification to exclude or grade hepatic steatosis and iron overload, respectively. US elastography is also widely performed to evaluate for liver fibrosis and has the advantage of lower equipment cost and greater availability compared with those of MRI. Emerging US fat quantification methods can be performed along with US elastography. The author group, consisting of members of the Society of Abdominal Radiology (SAR) Liver Fibrosis Disease-Focused Panel (DFP), the SAR Hepatic Iron Overload DFP, and the European Society of Radiology, review the basics of liver fibrosis, fat, and iron quantification with MRI and liver fibrosis and fat quantification with US. The authors cover technical requirements, typical case display, quality control and proper measurement technique and case interpretation guidelines, pitfalls, and confounding factors. The authors aim to provide a practical guide for radiologists interpreting these examinations. © RSNA, 2023 See the invited commentary by Ronot in this issue. Quiz questions for this article are available in the supplemental material.

Practical Tips and a Template for Developing Your Curriculum Vitae.

The Alliance of Leaders in Academic Affairs in Radiology (ALAAR) advocates for a Universal Curriculum Vitae for all medical institutions and to that end, we have developed a template that can be downloaded on the AUR website (ALAAR CV template) that includes all of the elements required by many academic institutions. Members of ALAAR represent multiple academic institutions and have spent many hours reviewing and providing input on radiologists' curricula vitae. The purpose of this review is to help academic radiologists accurately maintain and optimize their CVs with minimal effort and to clarify common questions that arise at many different institutions in the process of constructing a CV.

Gamut of Extratesticular Scrotal Masses: Anatomic Approach to Sonographic Differential Diagnosis.

The commonly taught tenet that intratesticular lesions are always malignant and extratesticular scrotal lesions are always benign is a simplistic statement that erroneously minimizes the significance of extratesticular scrotal masses and their diagnosis. Yet, disease in the extratesticular space is commonly encountered by clinicians and radiologists and is often a source of uncertainty in diagnosis and management. Given the embryologically rooted complex anatomy of this region, a wide range of pathologic conditions is possible. Radiologists may not be familiar with some of these conditions; further, many of these lesions can have a specific sonographic appearance, allowing accurate diagnosis that can minimize surgical intervention. Lastly, malignancies can occur in the extratesticular space-although this is less common than in the testicles-and proper recognition of findings that warrant additional imaging or surgery is critical in optimizing outcomes. The authors present a compartmental anatomic framework for differential diagnosis of extratesticular scrotal masses and provide a comprehensive illustrative display of many of the pathologic conditions that can be encountered to familiarize radiologists with the sonographic appearances of these lesions. They also review management of these lesions and scenarios where US may not be definitive in diagnosis and selective use of scrotal MRI can be helpful. © RSNA, 2023 Quiz questions for this article are available in the supplemental material.

Gastrointestinal malignancies in pregnancy.

Gastrointestinal malignancies, though uncommon in pregnancy, present several unique challenges with regards to diagnosis, staging, and treatment. Imaging the pregnant patient with a suspected or confirmed GI malignancy requires modifications to the radiologic modality of choice and protocol in order to minimize harm to the fetus, ensure accuracy in diagnosis and staging and guide treatment decisions. In this review article, we discuss the imaging approach to the pregnant patient with GI cancer, including safe radiologic modalities and modifications to imaging protocols. We also review the most common GI cancers encountered in pregnancy, including colorectal, pancreatic, gastric, and small bowel tumors, with emphasis to imaging findings, staging, and treatment considerations.

Gender Disparities in Academic Radiology Authorship: A 13-Year Review.

RATIONALE AND OBJECTIVES: Underrepresentation of women in many scientific fields has been linked to biases against female-authored publications in peer-reviewed journals, thereby reducing their opportunities for career development and promotions. The objective of this study is to determine the representation of female academic radiologists in four flagship general radiology journals to elucidate gender-specific trends and disparities in academic radiology. MATERIALS AND METHODS: We analyzed 23,741 peer-reviewed articles published in Radiology, American Journal of Roentgenology, Journal of the American College of Radiology and Academic Radiology from 2007 to 2020. Data abstraction and statistical analysis were performed for author gender, first and last authorship, research funding, and number of citations and grants. Baseline demographics data of academic radiologists was obtained from the Association of American Medical Colleges (AAMC). RESULTS: A total of 72.4% of authors were male with 54% of articles were written by a male first author and male last author. When compared with assumed random pairing, there were significantly fewer Male/Female author combinations and more Female/Female author combinations than expected (p<0.01). Over the 13-year time period, the rate of increase in the number of female authors exceeded the rate of increase in women in academic radiology as reported by the AAMC. Female last authors received on average 3.2 less citations than their male counterparts (p=.03). Of manuscripts with last female authors, 31.7% of female last authors were funded compared to 25.9% of last male authors. CONCLUSION: This study showed the increasing numbers and higher productivity of female authors in academic radiology, suggesting progress is being made in overcoming gender disparities.

MR Imaging of Mimics of Adnexal Pathology.

Mimics of adnexal masses can include uterine leiomyomas, intraperitoneal cystic and solid masses of mesenteric or gastrointestinal origin, and extraperitoneal cystic and solid masses. When a pelvic mass is discovered on imaging, a radiologist should recognize these mimics to avoid mischaracterization of a mass as ovarian for optimal patient management. Knowledge of pelvic anatomy, determining whether a mass is intraperitoneal or extraperitoneal, and troubleshooting with MR imaging can help determine the etiology and origin of a pelvic mass. Imaging characteristics and keys to diagnosis of these adnexal mass mimics are reviewed in this article.

Utilizing CT to identify clinically significant biliary dilatation in symptomatic post-cholecystectomy patients: when should we be worried?

PURPOSE: To determine a reliable threshold common duct diameter on CT, in combination with other ancillary CT and clinical parameters, at which the likelihood of pathology requiring further imaging or intervention is increased in post-cholecystectomy patients. METHODS: In this IRB approved retrospective study, two attending radiologists independently reviewed CT imaging for 118 post-cholecystectomy patients, who were subsequently evaluated with MRCP, ERCP, or EUS, prompted by findings on the CT and clinical status. Measurements of the common duct (CD) were obtained at the porta hepatis, distal duct, and point of maximal dilation on axial and coronal CT scans. Patients were grouped by whether they required intervention after follow-up imaging. Pertinent baseline lab values and patient demographics were reviewed. RESULTS: Of the 118 post-cholecystectomy patients, 38 patients (31%) required intervention, and 80 patients (69%) did not require intervention after follow-up imaging. For both readers, axial and coronal CD diameters were significantly higher in the 'intervention required' vs 'no intervention' groups at all locations (p value < 0.05). There was good to excellent inter-reader agreement at all locations (ICC 0.68-0.92). Pertinent baseline lab values including AST (p = 0.043), ALT (p = 0.001), alkaline phosphatase (p = 0.0001), direct bilirubin (p = 0.011), total bilirubin (p = 0.028), and WBC (p = 0.043) were significantly higher in the 'intervention required' group. CD thresholds of 8 mm yielded the highest sensitivities (76-95%), and CD thresholds of 12 mm yielded the highest specificities (65-78%). CD combined with bilirubin levels increased sensitivity and specificity, compared to using either feature alone. CONCLUSION: Dilated CD on CT combined with bilirubin levels increases the sensitivity and specificity for identifying patients needing intervention. We recommend that a post-cholecystectomy patient who presents with a CD diameter > 10 mm on CT and elevated bilirubin levels should undergo further clinical and imaging follow-up.

Assessing the accuracy of multiparametric MRI to predict clinically significant prostate cancer in biopsy naïve men across racial/ethnic groups.

INTRODUCTION: The Prostate Imaging Reporting and Data System (PIRADS) has shown promise in improving the detection of Gleason grade group (GG) 2-5 prostate cancer (PCa) and reducing the detection of indolent GG1 PCa. However, data on the performance of PIRADS in Black and Hispanic men is sparse. We evaluated the accuracy of PIRADS scores in detecting GG2-5 PCa in White, Black, and Hispanic men. METHODS: We performed a multicenter retrospective review of biopsy-naïve Black (n = 108), White (n = 108), and Hispanic (n = 64) men who underwent prostate biopsy (PB) following multiparametric MRI. Sensitivity and specificity of PIRADS for GG2-5 PCa were calculated. Race-stratified binary logistic regression models for GG2-5 PCa using standard clinical variables and PIRADS were used to calculate area under the receiver operating characteristics curves (AUC). RESULTS: Rates of GG2-5 PCa were statistically similar between Blacks, Whites, and Hispanics (52.8% vs 42.6% vs 37.5% respectively, p = 0.12). Sensitivity was lower in Hispanic men compared to White men (87.5% vs 97.8% respectively, p = 0.01). Specificity was similar in Black versus White men (21.6% vs 27.4%, p = 0.32) and White versus Hispanic men (27.4% vs 17.5%, p = 0.14). The AUCs of the PIRADS added to standard clinical data (age, PSA and suspicious prostate exam) were similar when comparing Black versus White men (0.75 vs 0.73, p = 0.79) and White versus Hispanic men (0.73 vs 0.59, p = 0.11). The AUCs for the Base model and PIRADS model alone were statistically similar when comparing Black versus White men and White versus Hispanic men. CONCLUSIONS: The accuracy of the PIRADS and clinical data for detecting GG2-5 PCa seems statistically similar across race. However, there is concern that PIRADS 2.0 has lower sensitivity in Hispanic men compared to White men. Prospective validation studies are needed.

Association of CT findings in patients with hemoperitoneum due to ruptured ovarian cysts with subsequent intervention.

PURPOSE: Hemoperitoneum caused by ovarian cyst rupture may necessitate intervention. The literature is lacking in descriptions of CT findings that help guide patient management. The purpose of this study is to consider CT findings associated with a need for intervention (surgical or interventional radiology management) versus conservative treatment (observation, pain management, and blood transfusions). METHODS: Two radiologists retrospectively and independently reviewed 103 CTs of pre-menopausal women who presented with acute hemoperitoneum related to ruptured ovarian cysts between January 2010 and January 2019. The following imaging features were assessed: ovarian cyst characteristics, sentinel clot, contrast extravasation, and hemoperitoneum size. Findings were correlated with patient demographics, clinical parameters, and management with surgery/interventional radiology procedure (intervention group) versus conservative management (conservative group). RESULTS: Of the 103 patients with hemoperitoneum from cyst rupture, 16% (n = 16) required intervention, and 84% underwent conservative treatment (n = 87). Length of stay (p = .008) was higher in the intervention group. Statistically significant CT findings in the intervention versus conservative group included (p-value reader 1/p-value reader 2) greatest AP dimension of hemoperitoneum (p = .001/p = 0.02), posterior cul-de-sac AP dimension (p = 0.03/p = .006), total cul-de-sac AP dimension (p = .002/p = .007), and number of spaces with hemoperitoneum (p = .01/p = .02). There was good to excellent inter-reader agreement for these findings (ICC 0.68-0.91). Active contrast extravasation was significant for one reader (p = .02) with poor inter-reader agreement (ICC 0.36). In utilizing ROC curves, thresholds of 107 mm (greatest axial AP dimension of hemoperitoneum) yielded a sensitivity and specificity of 0.81 and 0.62 for reader 1 and 0.69 and 0.55 for reader 2; 45 mm (posterior cul-de-sac AP dimension) yielded a sensitivity and specificity of 0.75 and 0.63 for reader 1 and 0.94 and 0.49 for reader 2; 70 mm (total cul-de-sac AP dimension) yielded a sensitivity and specificity of 0.75 and 0.64 for reader 1 and 0.75 and 0.50 for reader 2; and greater than 5 spaces yielded a sensitivity and specificity of 0.75 and 0.58 for reader 1 and 0.69 and 0.70 for reader 2. CONCLUSION: CT findings associated with intervention in hemoperitoneum due to ovarian cyst rupture include size of hemoperitoneum, number of abdominopelvic spaces with hemoperitoneum, and contrast extravasation.

Review of uterine fibroids: imaging of typical and atypical features, variants, and mimics with emphasis on workup and FIGO classification.

Uterine fibroids are the most common gynecologic neoplasm. Although non-degenerated fibroids are easily identifiable on imaging, degenerated fibroids, fibroid variants, and fibroids with unusual growth patterns can constitute a diagnostic dilemma. Identification of these abnormal morphologic features can alter the diagnosis of presumed uterine fibroids and hence change management plans. This article reviews the typical and atypical radiologic imaging features of uterine fibroids, with an emphasis on the pitfalls, mimics, and radiologically identifiable features that can alter clinical management plans.

Team Approach to Improving Radiologist Wellness: A Case-Based Methodology.

Radiologist wellness is important on an individual and group/institutional level and helps to promote a strong and healthy working environment, which can improve radiologist retention and engagement. This paper will discuss case examples of radiologist wellness improvements in a single academic institution over a 3-year period using the DMAIC (Define, Measure, Analyze, Improve, and Control) model. Leveraging this framework led to the implementation of reading room assistants, reduction in work-related injuries by improvements in ergonomics, and the formation of a faculty mentorship program.

Detecting Racial/Ethnic Health Disparities Using Deep Learning From Frontal Chest Radiography.

PURPOSE: The aim of this study was to assess racial/ethnic and socioeconomic disparities in the difference between atherosclerotic vascular disease prevalence measured by a multitask convolutional neural network (CNN) deep learning model using frontal chest radiographs (CXRs) and the prevalence reflected by administrative hierarchical condition category codes in two cohorts of patients with coronavirus disease 2019 (COVID-19). METHODS: A CNN model, previously published, was trained to predict atherosclerotic disease from ambulatory frontal CXRs. The model was then validated on two cohorts of patients with COVID-19: 814 ambulatory patients from a suburban location (presenting from March 14, 2020, to October 24, 2020, the internal ambulatory cohort) and 485 hospitalized patients from an inner-city location (hospitalized from March 14, 2020, to August 12, 2020, the external hospitalized cohort). The CNN model predictions were validated against electronic health record administrative codes in both cohorts and assessed using the area under the receiver operating characteristic curve (AUC). The CXRs from the ambulatory cohort were also reviewed by two board-certified radiologists and compared with the CNN-predicted values for the same cohort to produce a receiver operating characteristic curve and the AUC. The atherosclerosis diagnosis discrepancy, Δvasc, referring to the difference between the predicted value and presence or absence of the vascular disease HCC categorical code, was calculated. Linear regression was performed to determine the association of Δvasc with the covariates of age, sex, race/ethnicity, language preference, and social deprivation index. Logistic regression was used to look for an association between the presence of any hierarchical condition category codes with Δvasc and other covariates. RESULTS: The CNN prediction for vascular disease from frontal CXRs in the ambulatory cohort had an AUC of 0.85 (95% confidence interval, 0.82-0.89) and in the hospitalized cohort had an AUC of 0.69 (95% confidence interval, 0.64-0.75) against the electronic health record data. In the ambulatory cohort, the consensus radiologists' reading had an AUC of 0.89 (95% confidence interval, 0.86-0.92) relative to the CNN. Multivariate linear regression of Δvasc in the ambulatory cohort demonstrated significant negative associations with non-English-language preference (ß = -0.083, P < .05) and Black or Hispanic race/ethnicity (ß = -0.048, P < .05) and positive associations with age (ß = 0.005, P < .001) and sex (ß = 0.044, P < .05). For the hospitalized cohort, age was also significant (ß = 0.003, P < .01), as was social deprivation index (ß = 0.002, P < .05). The Δvasc variable (odds ratio [OR], 0.34), Black or Hispanic race/ethnicity (OR, 1.58), non-English-language preference (OR, 1.74), and site (OR, 0.22) were independent predictors of having one or more hierarchical condition category codes (P < .01 for all) in the combined patient cohort. CONCLUSIONS: A CNN model was predictive of aortic atherosclerosis in two cohorts (one ambulatory and one hospitalized) with COVID-19. The discrepancy between the CNN model and the administrative code, Δvasc, was associated with language preference in the ambulatory cohort; in the hospitalized cohort, this discrepancy was associated with social deprivation index. The absence of administrative code(s) was associated with Δvasc in the combined cohorts, suggesting that Δvasc is an independent predictor of health disparities. This may suggest that biomarkers extracted from routine imaging studies and compared with electronic health record data could play a role in enhancing value-based health care for traditionally underserved or disadvantaged patients for whom barriers to care exist.

Ovarian cancer reporting lexicon for computed tomography (CT) and magnetic resonance (MR) imaging developed by the SAR Uterine and Ovarian Cancer Disease-Focused Panel and the ESUR Female Pelvic Imaging Working Group.

OBJECTIVES: Imaging evaluation is an essential part of treatment planning for patients with ovarian cancer. Variation in the terminology used for describing ovarian cancer on computed tomography (CT) and magnetic resonance (MR) imaging can lead to ambiguity and inconsistency in clinical radiology reports. The aim of this collaborative project between Society of Abdominal Radiology (SAR) Uterine and Ovarian Cancer (UOC) Disease-focused Panel (DFP) and the European Society of Uroradiology (ESUR) Female Pelvic Imaging (FPI) Working Group was to develop an ovarian cancer reporting lexicon for CT and MR imaging. METHODS: Twenty-one members of the SAR UOC DFP and ESUR FPI working group, one radiology clinical fellow, and two gynecologic oncology surgeons formed the Ovarian Cancer Reporting Lexicon Committee. Two attending radiologist members of the committee prepared a preliminary list of imaging terms that was sent as an online survey to 173 radiologists and gynecologic oncologic physicians, of whom 67 responded to the survey. The committee reviewed these responses to create a final consensus list of lexicon terms. RESULTS: An ovarian cancer reporting lexicon was created for CT and MR Imaging. This consensus-based lexicon has 6 major categories of terms: general, adnexal lesion-specific, peritoneal carcinomatosis-specific, lymph node-specific, metastatic disease -specific, and fluid-specific. CONCLUSIONS: This lexicon for CT and MR imaging evaluation of ovarian cancer patients has the capacity to improve the clarity and consistency of reporting disease sites seen on imaging. KEY POINTS: • This reporting lexicon for CT and MR imaging provides a list of consensus-based, standardized terms and definitions for reporting sites of ovarian cancer on imaging at initial diagnosis or follow-up. • Use of standardized terms and morphologic imaging descriptors can help improve interdisciplinary communication of disease extent and facilitate optimal patient management. • The radiologists should identify and communicate areas of disease, including difficult to resect or potentially unresectable disease that may limit the ability to achieve optimal resection.

Ultrasound Workflow in a Pandemic: Lessons Learned from COVID-19.

The 2019 novel coronavirus (COVID-19) pandemic has posed unique, sudden challenges to health care systems. This is true particularly in the context of ultrasound logistics given the risks of inherent prolonged close contact of patients with sonographers and equipment during sonographic image acquisition. We describe the adaptations and modifications in scheduling, workflow, and imaging protocols implemented in our radiology department ultrasound division (a large urban academic center). The hierarchy of controls to minimize exposures to occupational hazards to protect workers, outlined by The National Institute for Occupational Safety and Health (NIOSH) are listed from most effective to least effective: elimination, substitution, engineering controls, administrative controls, and PPE (personal protective equipment (PPE)). Most of the mitigation techniques used in the ultrasound department to reduce hazards to workers involved administrative controls and PPE. We reduced preventable risks by using sterile precautions, imaging triage, and strategically minimizing image acquisition times. These implementations provide a modifiable framework for rapid adaptation during the evolving COVID-19 pandemic, including resurgences of variant strains. This framework ensures a level of preparedness for possible future pandemics or other widespread emergencies.

Validation of a deep learning, value-based care model to predict mortality and comorbidities from chest radiographs in COVID-19.

We validate a deep learning model predicting comorbidities from frontal chest radiographs (CXRs) in patients with coronavirus disease 2019 (COVID-19) and compare the model's performance with hierarchical condition category (HCC) and mortality outcomes in COVID-19. The model was trained and tested on 14,121 ambulatory frontal CXRs from 2010 to 2019 at a single institution, modeling select comorbidities using the value-based Medicare Advantage HCC Risk Adjustment Model. Sex, age, HCC codes, and risk adjustment factor (RAF) score were used. The model was validated on frontal CXRs from 413 ambulatory patients with COVID-19 (internal cohort) and on initial frontal CXRs from 487 COVID-19 hospitalized patients (external cohort). The discriminatory ability of the model was assessed using receiver operating characteristic (ROC) curves compared to the HCC data from electronic health records, and predicted age and RAF score were compared using correlation coefficient and absolute mean error. The model predictions were used as covariables in logistic regression models to evaluate the prediction of mortality in the external cohort. Predicted comorbidities from frontal CXRs, including diabetes with chronic complications, obesity, congestive heart failure, arrhythmias, vascular disease, and chronic obstructive pulmonary disease, had a total area under ROC curve (AUC) of 0.85 (95% CI: 0.85-0.86). The ROC AUC of predicted mortality for the model was 0.84 (95% CI,0.79-0.88) for the combined cohorts. This model using only frontal CXRs predicted select comorbidities and RAF score in both internal ambulatory and external hospitalized COVID-19 cohorts and was discriminatory of mortality, supporting its potential use in clinical decision making.

Magnetic Resonance Imaging in the Evaluation of Female Infertility.

Pelvic MRI plays an important adjunctive role in the clinical workup of female infertility. Hysterosalpingography is the first line imaging modality in evaluation of female infertility, and hysterosalpingo-contrast sonography can also be used to evaluate both the uterine cavity and fallopian tubes. Pelvic MRI can be helpful in the workup of female infertility, particularly in cases of Mullerian duct anomalies, fibroids, adenomyosis, endometriosis, and tubal disease. These conditions and their appearance on imaging will be reviewed in this article.