Digital Volumetric Biopsy Cores Improve Gleason Grading of Prostate Cancer Using Deep Learning
Journal:
arXiv
Published Date:
Sep 12, 2024
Abstract
Prostate cancer (PCa) was the most frequently diagnosed cancer among American
men in 2023. 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. This
process impedes the analysis of complex tissue structures such as glands, which
can vary depending on the tissue slice examined. We propose a novel digital
pathology data source called a "volumetric core," obtained via the extraction
and co-alignment of serially sectioned tissue sections using a novel
morphology-preserving alignment framework. We trained an attention-based
multiple-instance learning (ABMIL) framework on deep features extracted from
volumetric patches to automatically classify the Gleason Grade Group (GGG). To
handle volumetric patches, we used a modified video transformer with a deep
feature extractor pretrained using self-supervised learning. We ran our
morphology-preserving alignment framework to construct 10,210 volumetric cores,
leaving out 30% for pretraining. The rest of the dataset was used to train
ABMIL, which resulted in a 0.958 macro-average AUC, 0.671 F1 score, 0.661
precision, and 0.695 recall averaged across all five GGG significantly
outperforming the 2D baselines.
