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

Federated Learning with Research Prototypes: Application to Multi-Center MRI-based Detection of Prostate Cancer with Diverse Histopathology.

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

US lesion visibility predicts clinically significant upgrade of prostate cancer by systematic biopsy.

To identify predictors of when systematic biopsy leads to a higher overall prostate cancer grade compared to targeted biopsy. 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. 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). 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

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