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"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence . This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To evaluate the performance of the winning machine learning (ML) models from the 2023 RSNA Abdominal Trauma Detection Artificial Intelligence Challenge. Materials and Methods The competition was hosted on Kaggle and took place between July 26, 2023, to October 15, 2023. The multicenter competition dataset consisted of 4,274 abdominal trauma CT scans in which solid organs (liver, spleen and kidneys) were annotated as healthy, low-grade or high-grade injury. Studies were labeled as positive or negative for the presence of bowel/mesenteric injury and active extravasation. In this study, performances of the 8 award-winning models were retrospectively assessed and compared using various metrics, including the area under the receiver operating characteristic curve (AUC), for each injury category. The reported mean values of these metrics were calculated by averaging the performance across all models for each specified injury type. Results The models exhibited strong performance in detecting solid organ injuries, particularly high-grade injuries. For binary detection of injuries, the models demonstrated mean AUC values of 0.92 (range:0.91-0.94) for liver, 0.91 (range:0.87-0.93) for splenic, and 0.94 (range:0.93-0.95) for kidney injuries. The models achieved mean AUC values of 0.98 (range:0.96-0.98) for high-grade liver, 0.98 (range:0.97-0.99) for high-grade splenic, and 0.98 (range:0.97-0.98) for high-grade kidney injuries. For the detection of bowel/mesenteric injuries and active extravasation, the models demonstrated mean AUC values of 0.85 (range:0.74-0.73) and 0.85 (range:0.79-0.89) respectively. Conclusion The award-winning models from the AI challenge demonstrated strong performance in the detection of traumatic abdominal injuries on CT scans, particularly high-grade injuries. These models may serve as a performance baseline for future investigations and algorithms. ©RSNA, 2024.

Supplemental material is available for this article.

This manuscript illuminates the need for childcare support for trainees and faculty in the field of radiology, highlighting the essential need for access, affordability, and high-quality childcare services. For over four decades, women radiologists have voiced the challenges of meeting both childcare and professional responsibilities. The COVID-19 pandemic highlighted systemic inadequacies in the childcare infrastructure, exacerbating the challenges of this long-standing balancing act. The 2022 National Plan for Health Workforce Well-Being and the American Medical Association's (AMA) recent resolutions underscore the necessity of affordable, high-quality childcare in recruiting and retaining a diverse healthcare workforce. Despite the recent federal threshold categorizing childcare costs as affordable when they comprise 7% of household income, many families allocate >30% of household income to childcare. Disparities in childcare disproportionately impact women, leading to increased burnout and attrition rates in healthcare. This review explores exemplary childcare initiatives across various economic sectors that demonstrate positive returns on investment and employee retention. The manuscript provides actionable recommendations for radiology departments that can enhance workforce well-being. By addressing childcare needs, the field of radiology can improve the lives of parenting professionals and the patients they serve., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Inc.)

Non-invasive prostate cancer classification from MRI has the potential to revolutionize patient care by providing early detection of clinically significant disease, but has thus far shown limited positive predictive value. To address this, we present a image-based deep learning method to predict clinically significant prostate cancer from screening MRI in patients that subsequently underwent biopsy with results ranging from benign pathology to the highest grade tumors. Specifically, we demonstrate that mixed supervision via diverse histopathological ground truth improves classification performance despite the cost of reduced concordance with image-based segmentation. Where prior approaches have utilized pathology results as ground truth derived from targeted biopsies and whole-mount prostatectomy to strongly supervise the localization of clinically significant cancer, our approach also utilizes weak supervision signals extracted from nontargeted systematic biopsies with regional localization to improve overall performance. Our key innovation is performing regression by distribution rather than simply by value, enabling use of additional pathology findings traditionally ignored by deep learning strategies. We evaluated our model on a dataset of 973 (testing n=198 ) multi-parametric prostate MRI exams collected at UCSF from 2016-2019 followed by MRI/ultrasound fusion (targeted) biopsy and systematic (nontargeted) biopsy of the prostate gland, demonstrating that deep networks trained with mixed supervision of histopathology can feasibly exceed the performance of the Prostate Imaging-Reporting and Data System (PI-RADS) clinical standard for prostate MRI interpretation (71.6% vs 66.7% balanced accuracy and 0.724 vs 0.716 AUC).

Competing Interests: Declaration of competing interest None.

Paid family and medical leave (FML) has significant benefits to organizations, including improvements in employee recruitment and retention, workplace culture, and employee morale and productivity, and is supported by evidence for overall cost savings. Furthermore, paid FML related to childbirth has significant benefits to individuals and families, including but not limited to improved maternal and infant health outcomes and improved breastfeeding initiation and duration. In the case of nonchildbearing parental leave, paid FML is associated with more equitable long-term division of household labor and childcare. Paid FML is increasingly being recognized as an important issue in medicine, as evidenced by the recent passage of policies by national societies and governing bodies, including the American Board of Medical Specialties, American Board of Radiology, Accreditation Council for Graduate Medical Education (ACGME), American College of Radiology, and American Medical Association. Implementation of paid FML requires adherence to federal, state, and local laws as well as institutional requirements. Specific requirements pertain to trainees from national governing bodies, such as the ACGME and medical specialty boards. Flexibility, work coverage, culture, and finances are additional considerations for ensuring an optimal paid FML policy that accounts for concerns of all impacted individuals.

Rationale and Objectives: Early prostate cancer detection and staging from MRI is extremely challenging for both radiologists and deep learning algorithms, but the potential to learn from large and diverse datasets remains a promising avenue to increase their performance within and across institutions. To enable this for prototype-stage algorithms, where the majority of existing research remains, we introduce a flexible federated learning framework for cross-site training, validation, and evaluation of custom deep learning prostate cancer detection algorithms., Materials and Methods: We introduce an abstraction of prostate cancer groundtruth that represents diverse annotation and histopathology data. We maximize use of this groundtruth if and when they are available using UCNet, a custom 3D UNet that enables simultaneous supervision of pixel-wise, region-wise, and gland-wise classification. We leverage these modules to perform cross-site federated training using 1400+ heterogeneous multi-parameteric prostate MRI exams from two University hospitals., Results: We observe a positive result, with significant improvements in cross-site generalization performance with negligible intra-site performance degradation for both lesion segmentation and per-lesion binary classification of clinically-significant prostate cancer. Cross-site lesion segmentation performance intersection-over-union (IoU) improved by 100%, while cross-site lesion classification performance overall accuracy improved by 9.5-14.8%, depending on the optimal checkpoint selected by each site., Conclusion: Federated learning can improve the generalization performance of prostate cancer detection models across institutions while protecting patient health information and institution-specific code and data. However, even more data and participating institutions are likely required to improve the absolute performance of prostate cancer classification models. To enable adoption of federated learning with limited re-engineering of federated components, we open-source our FLtools system at https://federated.ucsf.edu, including examples that can be easily adapted to other medical imaging deep learning projects., Competing Interests: Declaration of Competing Interest Authors have no competing interests to declare., (Copyright © 2023. Published by Elsevier Inc.)

The American College of Radiology (ACR) passed a historic paid family/medical leave (PFML) resolution at its April 2022 meeting, resolving that "diagnostic radiology, interventional radiology, radiation oncology, medical physics, and nuclear medicine practices, departments and training programs strive to provide 12 weeks of paid family/medical leave in a 12-month period for its attending physicians, medical physicists, and members in training as needed." The purpose of this article is to share this policy beyond radiology so that it may serve as a call to action for other medical specialties. Such a PFML policy (1) supports physician well-being, which in turn supports patient care; (2) is widely needed across American medical specialties; and (3) should not take nearly a decade to achieve, as it did in radiology, especially given increasing physician burnout and the ongoing COVID-19 pandemic. Supported by information on the step-by-step approach used to achieve radiology-specific leave policies and considering current and normative policies at the national level, this article concludes by reviewing specific strategies that could be applied toward achieving a 12-week PFML policy for all medical specialties.

BACKGROUND. CT-based body composition (BC) measurements have historically been too resource intensive to analyze for widespread use and have lacked robust comparison with traditional weight metrics for predicting cardiovascular risk. OBJECTIVE. The aim of this study was to determine whether BC measurements obtained from routine CT scans by use of a fully automated deep learning algorithm could predict subsequent cardiovascular events independently from weight, BMI, and additional cardiovascular risk factors. METHODS. This retrospective study included 9752 outpatients (5519 women and 4233 men; mean age, 53.2 years; 890 patients self-reported their race as Black and 8862 self-reported their race as White) who underwent routine abdominal CT at a single health system from January 2012 through December 2012 and who were given no major cardiovascular or oncologic diagnosis within 3 months of undergoing CT. Using publicly available code, fully automated deep learning BC analysis was performed at the L3 vertebral body level to determine three BC areas (skeletal muscle area [SMA], visceral fat area [VFA], and subcutaneous fat area [SFA]). Age-, sex-, and race-normalized reference curves were used to generate z scores for the three BC areas. Subsequent myocardial infarction (MI) or stroke was determined from the electronic medical record. Multivariable-adjusted Cox proportional hazards models were used to determine hazard ratios (HRs) for MI or stroke within 5 years after CT for the three BC area z scores, with adjustment for normalized weight, normalized BMI, and additional cardiovascular risk factors (smoking status, diabetes diagnosis, and systolic blood pressure). RESULTS. In multivariable models, age-, race-, and sex-normalized VFA was associated with subsequent MI risk (HR of highest quartile compared with lowest quartile, 1.31 [95% CI, 1.03-1.67], p = .04 for overall effect) and stroke risk (HR of highest compared with lowest quartile, 1.46 [95% CI, 1.07-2.00], p = .04 for overall effect). In multivariable models, normalized SMA, SFA, weight, and BMI were not associated with subsequent MI or stroke risk. CONCLUSION. VFA derived from fully automated and normalized analysis of abdominal CT examinations predicts subsequent MI or stroke in Black and White patients, independent of traditional weight metrics, and should be considered an adjunct to BMI in risk models. CLINICAL IMPACT. Fully automated and normalized BC analysis of abdominal CT has promise to augment traditional cardiovascular risk prediction models.

Paid family and medical leave policies are increasingly popular in today's competitive labor market and provide well-documented advantages to all stakeholders. Implementing paid leave for radiologists can seem daunting due to overlapping legal and institutional policies, logistical challenges and call coverage, as well as industry-specific special considerations such as resident education and historical workplace attitudes. This toolkit can empower radiology leaders to implement written paid leave policies in their home institutions and demonstrate that equitable, compassionate institutional policies for paid leave are financially favorable, widely desirable, and increasingly achievable with the right tools in hand., Competing Interests: Declaration of competing interest The authors did not receive support from any organization from the submitted work. The authors have no relevant financial or non-financial interests to disclose., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Purpose: To identify predictors of when systematic biopsy leads to a higher overall prostate cancer grade compared to targeted biopsy., Methods and Materials: 918 consecutive patients who underwent prostate MRI followed by MRI/US fusion biopsy and systematic biopsies from January 2015 to November 2019 at a single academic medical center were retrospectively identified. The outcome was upgrade of PCa by systematic biopsy, defined as cases when systematic biopsy led to a Gleason Grade (GG) ≥ 2 and greater than the maximum GG detected by targeted biopsy. Generalized linear regression and conditional logistic regression were used to analyze predictors of upgrade., Results: At the gland level, the presence of an US-visible lesion was associated with decreased upgrade (OR 0.64, 95% CI 0.44-0.93, p = 0.02). At the sextant level, upgrade was more likely to occur through the biopsy of sextants with MRI-visible lesions (OR 2.58, 95% CI 1.87-3.63, p < 0.001), US-visible lesions (OR 1.83, 95% CI 1.14-2.93, p = 0.01), and ipsilateral lesions (OR 3.89, 95% CI 2.36-6.42, p < 0.001)., Conclusion: Systematic biopsy is less valuable in patients with an US-visible lesion, and more likely to detect upgrades in sextants with imaging abnormalities. An approach that takes additional samples from regions with imaging abnormalities may provide analogous information to systematic biopsy., (© 2022. The Author(s).)

Rationale and Objectives: To assess resident and fellowship program director (PD) perceptions of the abdominal radiology fellowship application process following the first cycle in which an embargo on interviews until December 1, 2019 was set according to the Society of Chairs of Academic Radiology Departments (SCARD) timeline for the 2021-2022 abdominal imaging fellowship year., Materials and Methods: Eligible study participants included fellowship PDs of all abdominal imaging programs in the United States and residents that attended the Society of Abdominal Radiology (SAR) 2020 Annual Meeting. A questionnaire was developed by content and survey experts, pilot tested, and administered from May to June 2020., Results: A total of 39% (36/92) of all PDs and 30% (46/152) of all individuals identified as residents with valid email addresses that attended the SAR 2020 Annual Meeting responded to the survey with an overall response rate of 34%. Only 42% of PDs and 33% of residents supported moving to a match, while 62% of PDs and 70% of residents thought that a match would limit the autonomy of applicants. While most PDs and residents also agreed that the first iteration of the SCARD timeline allowed residents to make a more informed choice, the majority of PDs were dissatisfied with their experience. Most PDs and residents additionally want applications to be accepted no earlier than July and/or August of the R3 year (initial SCARD guidelines did not restrict timing), interviews to begin on November 1st or earlier of the R3 year (compared to December 1st set in the first iteration of the guidelines), and a gap of 2-4 weeks between the date of first interviews and notification of first offers (initial SCARD guidelines did not restrict timing). Lastly, an overwhelming majority of PDs and residents agreed that SAR should enforce the abdominal imaging fellowship application process., Conclusion: Following the first cycle of abdominal imaging fellowship applications conducted according to the SCARD guidelines, a majority of trainees and PDs felt the changes were favorable and were opposed to a formal match. Specific suggestions for improvement were elicited from stakeholders and will be incorporated for the next cycle., (Copyright © 2020 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Body composition on chest CT scans encompasses a set of important imaging biomarkers. This study developed and validated a fully automated analysis pipeline for multi-vertebral level assessment of muscle and adipose tissue on routine chest CT scans. This study retrospectively trained two convolutional neural networks on 629 chest CT scans from 629 patients (55% women; mean age, 67 years ± 10 [standard deviation]) obtained between 2014 and 2017 prior to lobectomy for primary lung cancer at three institutions. A slice-selection network was developed to identify an axial image at the level of the fifth, eighth, and 10th thoracic vertebral bodies. A segmentation network (U-Net) was trained to segment muscle and adipose tissue on an axial image. Radiologist-guided manual-level selection and segmentation generated ground truth. The authors then assessed the predictive performance of their approach for cross-sectional area (CSA) (in centimeters squared) and attenuation (in Hounsfield units) on an independent test set. For the pipeline, median absolute error and intraclass correlation coefficients for both tissues were 3.6% (interquartile range, 1.3%-7.0%) and 0.959-0.998 for the CSA and 1.0 HU (interquartile range, 0.0-2.0 HU) and 0.95-0.99 for median attenuation. This study demonstrates accurate and reliable fully automated multi-vertebral level quantification and characterization of muscle and adipose tissue on routine chest CT scans. Keywords : Skeletal Muscle, Adipose Tissue, CT, Chest, Body Composition Analysis, Convolutional Neural Network (CNN), Supervised Learning Supplemental material is available for this article. © RSNA, 2022., Competing Interests: Disclosures of conflicts of interest: C.P.B. Support from the MGH and BWH Center for Clinical Data Science (CCDS) for travel to conferences. The CCDS in turn receives support from GE Healthcare, Nuance Communications, Nvidia, Diagnóstico da América S.A., and Fujifilm Sonosite; US Patent applications pending for: Computed Tomography Medical Imaging Intracranial Hemorrhage Model (US Patent Application 16/587,828). In collaboration with GE Healthcare and Medical Imaging Stroke Model (US Patent Application 16/588,080). In collaboration with GE Healthcare. T.D.B. No relevant relationships. M.M.W. No relevant relationships. J.P.M. No relevant relationships. K.M. Former trainee editorial board member for Radiology: Artificial Intelligence. C.J. No relevant relationships. J.H.C. Editorial board member of Radiology: Cardiothoracic Imaging. J.K.C. Deputy editor of Radiology: Artificial Intelligence. K.P.A. A Mobile Health Diagnostic Device for HIV Self-Testing NIH 1R61 AI140489-01A1 PI: Shafiee, Andriole, Co-Investigator, Study goals are to develop a hand-held device for HIV self-testing using artificial intelligence algorithms for data analysis. 8/2019-7/2022; associate editor of Radiology: Artificial Intelligence. F.J.F. American Roentgen Ray Society scholarship grant (related to this work); grant from William M. Wood Foundation (not related to this work); grant form Society of Interventional Oncology (unrelated to this work); research support from Boston Scientific (unrelated to this work); patents related to body composition analysis., (2022 by the Radiological Society of North America, Inc.)

Many national radiology societies are recognizing the need for early career and trainee engagement as crucial to keeping their societies relevant, active, and invigorated with new ideas. In this descriptive paper, we review the benefits of establishing the Society of Abdominal Radiology's Resident and Fellow Section and Early Career Committee-including our activities and experience, advice for committee structure, and opportunities for growth., (© 2021. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

With vast interest in machine learning applications, more investigators are proposing to assemble large datasets for machine learning applications. We aim to delineate multiple possible roadblocks to exam retrieval that may present themselves and lead to significant time delays. This HIPAA-compliant, institutional review board-approved, retrospective clinical study required identification and retrieval of all outpatient and emergency patients undergoing abdominal and pelvic computed tomography (CT) at three affiliated hospitals in the year 2012. If a patient had multiple abdominal CT exams, the first exam was selected for retrieval (n=23,186). Our experience in attempting to retrieve 23,186 abdominal CT exams yielded 22,852 valid CT abdomen/pelvis exams and identified four major categories of challenges when retrieving large datasets: cohort selection and processing, retrieving DICOM exam files from PACS, data storage, and non-recoverable failures. The retrieval took 3 months of project time and at minimum 300 person-hours of time between the primary investigator (a radiologist), a data scientist, and a software engineer. Exam selection and retrieval may take significantly longer than planned. We share our experience so that other investigators can anticipate and plan for these challenges. We also hope to help institutions better understand the demands that may be placed on their infrastructure by large-scale medical imaging machine learning projects., (© 2021. The Author(s).)

The authors provide a commentary on the current status of the Abdominal Radiology Fellowship recruitment process, which is not presently governed by a formal Match. Abdominal Radiology is the largest radiology subspecialty fellowship that remains outside of the Match. The Society of Abdominal Radiology convened a task force in 2019 to assess stakeholder viewpoints on a Match and found that the community was divided. Radiology departments and Abdominal Radiology fellowship program directors have voluntarily complied with a series of guidelines laid out by the Society of Chairs in Academic Radiology Departments during the two most recent recruiting cycles, but challenges in the process persist. Stakeholders report improved organization and fairness as a result of these procedural changes, and the authors suggest that Abdominal Radiology may continue to consider a formal fellowship Match in coming years., (© 2021. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Artificial intelligence (AI) tools are rapidly being developed for radiology and other clinical areas. These tools have the potential to dramatically change clinical practice; however, for these tools to be usable and function as intended, they must be integrated into existing radiology systems. In a collaborative effort between the Radiological Society of North America, radiologists, and imaging-focused vendors, the Imaging AI in Practice (IAIP) demonstrations were developed to show how AI tools can generate, consume, and present results throughout the radiology workflow in a simulated clinical environment. The IAIP demonstrations highlight the critical importance of semantic and interoperability standards, as well as orchestration profiles for successful clinical integration of radiology AI tools. Keywords: Computer Applications-General (Informatics), Technology Assessment © RSNA, 2021., Competing Interests: Disclosures of conflicts of interest: W.F.W. University of Wisconsin GE CT Protocol Project Medical Advisory Board participation. K.M. Clinical Champion, Imaging AI in Practice (IAIP): 2020; Co-chair, IAIP Steering Committee, 2021 (unpaid volunteer, Radiological Society of North America [RSNA] provided funds for demonstration [administrative assistance, arrangement for Zoom calls] but no funds for the paper itself); former trainee editorial board member of Radiology: Artificial Intelligence. T.M.S.S. Johns Hopkins School of Medicine, adjunct professor; Marquette University College of Engineering, adjunct professor; RSNA, IAIP technical project manager (consulting fees); RSNA Structured Reporting Committee, Society for Imaging Informatics in Medicine (SIIM) Clinical Data Informaticist Task Force co-chair. S.D.O. No relevant relationships. C.D.C. No relevant relationships. M.D.K. Consulting fees from Alara Imaging paid to author as a shareholder; honoraria for speaking engagements with various topics, including AI, from Honor Health SCGR-Wires (author retained complete editorial control for all of these engagements); travel support from Korean Congress of Radiology, RSNA, SIIM, Digital Pathology Association; periodic uncompensated consulting for Nuance; leadership roles as chair of board of directors for SIIM, co-chair of Common Data Elements committee, RSNA Radiology Informatics Committee (RIC) member, IAIP Task Force member for RSNA, and Informatics Commission member for American College of Radiology; stock/stock holder in Alara Imaging. K.P.A. Unpaid volunteer member of the RSNA RIC, RSNA provided funds for demonstration (administrative assistance, arrangement for Zoom calls) but no funds for the paper itself; co-chair of IAIP Demonstration; support for travel from RSNA for RIC committee meetings; unpaid appointed member of Massachusetts General Brigham Data Science Oversight Committee, which occasionally hears concerns and gives institutional guidance on data and tissue-sharing activities; associate editor of Radiology: Artificial Intelligence., (2021 by the Radiological Society of North America, Inc.)

Artificial or augmented intelligence, machine learning, and deep learning will be an increasingly important part of clinical practice for the next generation of radiologists. It is therefore critical that radiology residents develop a practical understanding of deep learning in medical imaging. Certain aspects of deep learning are not intuitive and may be better understood through hands-on experience; however, the technical requirements for setting up a programming and computing environment for deep learning can pose a high barrier to entry for individuals with limited experience in computer programming and limited access to GPU-accelerated computing. To address these concerns, we implemented an introductory module for deep learning in medical imaging within a self-contained, web-hosted development environment. Our initial experience established the feasibility of guiding radiology trainees through the module within a 45-min period typical of educational conferences., (© 2021. Society for Imaging Informatics in Medicine.)

Background Although CT-based body composition (BC) metrics may inform disease risk and outcomes, obtaining these metrics has been too resource intensive for large-scale use. Thus, population-wide distributions of BC remain uncertain. Purpose To demonstrate the validity of fully automated, deep learning BC analysis from abdominal CT examinations, to define demographically adjusted BC reference curves, and to illustrate the advantage of use of these curves compared with standard methods, along with their biologic significance in predicting survival. Materials and Methods After external validation and equivalency testing with manual segmentation, a fully automated deep learning BC analysis pipeline was applied to a cross-sectional population cohort that included any outpatient without a cardiovascular disease or cancer who underwent abdominal CT examination at one of three hospitals in 2012. Demographically adjusted population reference curves were generated for each BC area. The z scores derived from these curves were compared with sex-specific thresholds for sarcopenia by using χ 2 tests and used to predict 2-year survival in multivariable Cox proportional hazards models that included weight and body mass index (BMI). Results External validation showed excellent correlation ( R = 0.99) and equivalency ( P < .001) of the fully automated deep learning BC analysis method with manual segmentation. With use of the fully automated BC data from 12 128 outpatients (mean age, 52 years; 6936 [57%] women), age-, race-, and sex-normalized BC reference curves were generated. All BC areas varied significantly with these variables ( P < .001 except for subcutaneous fat area vs age [ P = .003]). Sex-specific thresholds for sarcopenia demonstrated that age and race bias were not present if z scores derived from the reference curves were used ( P < .001). Skeletal muscle area z scores were significantly predictive of 2-year survival ( P = .04) in combined models that included BMI. Conclusion Fully automated body composition (BC) metrics vary significantly by age, race, and sex. The z scores derived from reference curves for BC parameters better capture the demographic distribution of BC compared with standard methods and can help predict survival. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Summers in this issue.

Artificial intelligence and machine learning (AI-ML) have taken center stage in medical imaging. To develop as leaders in AI-ML, radiology residents may seek a formative data science experience. The authors piloted an elective Data Science Pathway (DSP) for 4th-year residents at the authors' institution in collaboration with the MGH & BWH Center for Clinical Data Science (CCDS). The goal of the DSP was to provide an introduction to AI-ML through a flexible schedule of educational, experiential, and research activities. The study describes the initial experience with the DSP tailored to the AI-ML interests of three senior radiology residents. The authors also discuss logistics and curricular design with common core elements and shared mentorship. Residents were provided dedicated, full-time immersion into the CCDS work environment. In the initial DSP pilot, residents were successfully integrated into AI-ML projects at CCDS. Residents were exposed to all aspects of AI-ML application development, including data curation, model design, quality control, and clinical testing. Core concepts in AI-ML were taught through didactic sessions and daily collaboration with data scientists and other staff. Work during the pilot period led to 12 accepted abstracts for presentation at national meetings. The DSP is a feasible, well-rounded introductory experience in AI-ML for senior radiology residents. Residents contributed to model and tool development at multiple stages and were academically productive. Feedback from the pilot resulted in establishment of a formal AI-ML curriculum for future residents. The described logistical, planning, and curricular considerations provide a framework for DSP implementation at other institutions. Supplemental material is available for this article. © RSNA, 2020., Competing Interests: Disclosures of Conflicts of Interest: W.F.W. disclosed no relevant relationships. M.T.C. disclosed no relevant relationships. K.M. Activities related to the present article: affiliated with Brigham and Women’s Hospital. Activities not related to the present article: institution received grant from RSNA R&E Foundation and an SAR research grant. Other relationships: disclosed no relevant relationships. S.A.G. disclosed no relevant relationships. E.G. disclosed no relevant relationships. M.H.R. Activities related to the present article: disclosed no relevant relationships. Activities not related to the present article: institution received grant from Stand Up to Cancer Foundation, Lustgarten Foundation, and National Institutes of Health. Other relationships: disclosed no relevant relationships. G.C.G. disclosed no relevant relationships. K.P.A. Activities related to the present article: affiliated with Brigham and Women’s Hospital, Harvard Medical School, MGH & BWH Center for Clinical Data Science. Activities not related to the present article: board membership Academy for Radiology and Biomedical Imaging Research (board of directors 2012–2014, executive board 2019–present); travel accommodations for RSNA Radiology Informatics Committee; travel reimbursement for ASNR/RSNA CERT Meeting; American College of Radiology, Data Science Institute, senior scientist for education, no funds received. Other relationships: disclosed no relevant relationships., (2020 by the Radiological Society of North America, Inc.)

Purpose: After the Society of Chairs of Academic Radiology Departments timeline and guidelines were released for the 2021 through 2022 fellowship application cycle, the Society of Abdominal Radiology conducted a survey of residents, fellows, and abdominal imaging fellowship program directors (PDs) to assess stakeholders' perceptions of changes in the fellowship application process., Methods: Eligible study participants included fellowship PDs of all US abdominal imaging programs and Society of Abdominal Radiology members-in-training. A questionnaire was developed by content and survey experts, pilot-tested, and administered from August to October 2019., Results: Survey response rates were 51.4% among PDs (54 of 103) and 24.2% among trainees (67 of 279), with an overall response rate of 31.8%. Attitudes regarding the abdominal imaging fellowship application process were overall similar between PDs and trainees, including expressed support for a common application. Although trainees and PDs agreed that the Society of Chairs of Academic Radiology Departments 2021 through 2022 cycle timeline is preferable to the prior unstructured system, only 42.4% of PDs and 40.7% of trainees supported moving to a formal match, with a significant number of respondents undecided. Both PDs and trainees favored timing fellowship interviews during the fall of the third year of residency (R3 year), with a 1- to 2-month buffer between the start of interviews and offers., Conclusions: PDs and trainees demonstrate similar attitudes in support of the Society of Chairs of Academic Radiology Departments 2021 through 2022 cycle timeline and a common abdominal imaging fellowship application. Shifting the interview season from winter to fall of R3 year could be considered to meet the preferences of PDs and trainees alike. Moving to a formal match remains controversial., (Copyright © 2020 American College of Radiology. Published by Elsevier Inc. All rights reserved.)

Background: Parenting issues can affect physicians' choice of specialty or subspecialty, as well as their selection of individual training programs, because of the distinctive challenges facing residents and fellows with children. Specific information about how residents perceive these challenges is limited., Objective: We sought to better understand the challenges associated with parenting during residency and fellowship training in order to inform policy and research., Methods: In 2017, a voluntary online questionnaire was distributed to all 2214 Partners HealthCare graduate medical education trainees across 285 training programs. The survey queried attitudes of and about trainees with children and assessed needs and experiences related to parental leave, lactation, and childcare. Responses were compared between subgroups, including gender, surgical versus nonsurgical specialty, parental status, and whether the respondent was planning to become a parent., Results: A total of 578 trainees (26%) responded to the questionnaire. Of these, 195 (34%) became parents during training. An additional 298 (52%) planned to become parents during training. Respondents overwhelmingly agreed that their institution should support trainees with children (95%) and that doing so is important for trainee wellness (98%). However, 25% felt that trainees with children burden trainees without children. Childcare access, affordability, and availability for sufficient hours were identified as key challenges, along with issues related to parental leave, lactation facilities, and effect on peers., Conclusions: This survey highlights trainees' perspectives about parenting during their clinical training, signaling parental leave, lactation facilities, and childcare access and affordability as particular challenges and potential targets for future interventions., Competing Interests: Conflict of interest: The authors declare they have no competing interests., (Accreditation Council for Graduate Medical Education 2020.)

Germ cell tumors, because they contain immature and mature elements, can differentiate into different tissue types. They can exhibit unusual imaging features or manifest in a syndromic fashion. The authors describe these features and assign them to one of the following categories: (a) unusual manifestations of metastatic disease (growing teratoma syndrome, choriocarcinoma syndrome, ossified metastases, and gliomatosis peritonei); (b) autoimmune manifestations (sarcoidlike reaction and paraneoplastic syndromes); (c) endocrine syndromes (sex hormone production, struma ovarii, and struma carcinoid); or (d) miscellaneous conditions (ruptured dermoid cyst, squamous cell carcinoma arising from a mature teratoma, Currarino triad, fetus in fetu, pseudo-Meigs syndrome, and pancreatitis). Rare conditions associated with germ cell tumors demonstrate characteristic imaging findings that can help lead to the appropriate diagnosis and management recommendations. When evaluating for potential metastatic disease, alternative benign diagnoses should be considered (eg, growing teratoma syndrome, ossified metastases, ruptured dermoid cyst, gliomatosis peritonei, and sarcoidlike reaction), which may impact management. Germ cell tumors may also lead to life-threatening complications such as extensive hemorrhage from choriocarcinoma metastases or the rupture of mature teratomas, cases in which timely diagnosis is crucial. Autoimmune and endocrine manifestations such as paraneoplastic encephalitis, autoimmune hemolytic anemia, and hyperthyroidism may occur owing to the presence of germ cell tumors and can create a diagnostic dilemma for clinicians. Knowledge of the syndromic and unusual imaging findings associated with germ cell tumors helps guide appropriate management. © RSNA, 2019.

KRAS, BRAF, and PI3KCA are the most frequently mutated oncogenes in human colon cancer. To explore their effects on morphogenesis, we used the colon cancer-derived cell line Caco-2. When seeded in extracellular matrix, individual cells proliferate and generate hollow, polarized cysts. The expression of oncogenic phosphatidylinositol 3-kinase (PI3KCA H1047R) in Caco-2 has no effect, but K-Ras V12 or B-Raf V600E disrupts polarity and tight junctions and promotes hyperproliferation, resulting in large, filled structures. Inhibition of mitogen-activated protein/extracellular signal-regulated kinase (ERK) kinase blocks the disruption of morphology, as well as the increased levels of c-myc protein induced by K-Ras V12 and B-Raf V600E. Apical polarity is already established after the first cell division (two-cell stage) in Caco-2 three-dimensional cultures. This is disrupted by expression of K-Ras V12 or B-Raf V600E but can be rescued by ribonucleic acid interference-mediated depletion of c-myc. We conclude that ERK-mediated up-regulation of c-myc by K-Ras or B-Raf oncogenes disrupts the establishment of apical/basolateral polarity in colon epithelial cells independently of its effect on proliferation.