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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.)

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.)