Digital Volumetric Biopsy Cores Improve Gleason Grading of Prostate Cancer Using Deep Learning.

Prostate cancer (PCa) was the most frequently diagnosed cancer among American men in 2023 [1]. The histological grading of biopsies is essential for diagnosis, and various deep learning-based solutions have been developed to assist with this task. Existing deep learning frameworks are typically applied to individual 2D cross-sections sliced from 3D biopsy tissue specimens.

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.