Mixed Supervision of Histopathology Improves Prostate Cancer Classification from MRI

Non-invasive prostate cancer detection from MRI has the potential to revolutionize patient care by providing early detection of clinically-significant disease (ISUP grade group >= 2), but has thus far shown limited positive predictive value. To address this, we present an MRI-based deep learning method for predicting clinically significant prostate cancer applicable to a patient population with subsequent ground truth biopsy results ranging from benign pathology to ISUP grade group~5. Specifically, we demonstrate that mixed supervision via diverse histopathological ground truth improves classification performance despite the cost of reduced concordance with image-based segmentation. That is, 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=160) multi-parametric prostate MRI exams collected at UCSF from 2015-2018 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 significantly exceed the performance of the Prostate Imaging-Reporting and Data System (PI-RADS) clinical standard for prostate MRI interpretation

Family and Medical Leave for Diagnostic Radiology, Interventional Radiology, and Radiation Oncology Residents in the United States: A Policy Opportunity

The American Board of Medical Specialties recently announced that effective July 1, 2021, member boards with training programs of 2 years or more must “establish requirements for candidates to become eligible for Initial Certification, including standards for training” and have “policies that accommodate reasonable leaves of absence from residency and fellowship training for personal or familial needs”. In preparation for this mandate, the American Board of Radiology (ABR) solicited comments from diverse stakeholders in March 2021—including the Association of Program Directors in Radiology, the Association of Program Directors in Interventional Radiology, and the ABR Initial Certification Advisory Committee for Radiation Oncology—with regards to Residency Service-Time Requirement, including considerations of family and medical leave. These communications included an initial proposed policy suggesting that “Programs may grant up to six weeks Parental, Caregiver and Medical Leave during the residency”. We appreciate the ABR\u27s efforts to seek feedback as it develops an updated policy. The purpose of this piece is to promote transparent discourse and to examine the nuanced issues pertaining to family and medical leave considerations within the broader context of Residency Service-Time Requirement policies for diagnostic radiology (DR), interventional radiology (IR), and radiation oncology (RO) residents, with the shared goal of optimizing both the training of competent clinicians worthy of public trust as well as professional well-being and diversity, equity, and inclusion. Given the rationale provided below, we recommend that the ABR leave policy allow a resident who is in good standing to take 12 weeks of family and medical leave during residency (in addition to 4 weeks of vacation per year), to sit for the Core/Qualifying Examinations on time, and to graduate without extension of training, with additional leave to be considered by the program director on a case-by-case basis

Childbearing and Family Leave Policies for Resident Physicians at Top Training Institutions.

This study describes childbearing and family leave at 15 graduate medical education (GME)–sponsoring institutions affiliated with 12 US medical schools on top 10 lists for funding or ranking

Childbearing and Family Leave Policies for Resident Physicians at Top Training Institutions.

This study describes childbearing and family leave at 15 graduate medical education (GME)–sponsoring institutions affiliated with 12 US medical schools on top 10 lists for funding or ranking

Imaging AI in Practice: A Demonstration of Future Workflow Using Integration Standards.

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