Abstract
Gleason grading, a risk stratification method for prostate cancer, is subjective and dependent on experience and expertise of the reporting pathologist. Deep Learning (DL) systems have shown promise in enhancing the objectivity and efficiency of Gleason grading. However, DL networks exhibit domain shift and reduced performance on Whole Slide Images (WSI) from a source other than training data. We propose a DL approach for segmenting and grading epithelial tissue using a novel training methodology that learns domain agnostic features. In this retrospective study, we analyzed WSI from three cohorts of prostate cancer patients. 3741 core needle biopsies (CNBs) received from two centers were used for training. The κquad (quadratic-weighted kappa) and AUC were measured for grade group comparison and core-level detection accuracy, respectively. Accuracy of 89.4% and κquad of 0.92 on the internal test set of 425 CNB WSI and accuracy of 85.3% and κquad of 0.96 on an external set of 1201 images, was observed. The system showed an accuracy of 83.1% and κquad of 0.93 on 1303 WSI from the third institution (blind evaluation). Our DL system, used as an assistive tool for CNB review, can potentially improve the consistency and accuracy of grading, resulting in better patient outcomes.
Highlights
Gleason grading, a risk stratification method for prostate cancer, is subjective and dependent on experience and expertise of the reporting pathologist
Our work presents a Deep Learning system (Fig. 1) to detect cancer regions and predict Gleason grades in Whole Slide Images (WSI) of prostate core needle biopsies (CNBs)
A panel of pathologists evaluated the internal set of 425 biopsies to determine the Gleason grade
Summary
A risk stratification method for prostate cancer, is subjective and dependent on experience and expertise of the reporting pathologist. We propose a DL approach for segmenting and grading epithelial tissue using a novel training methodology that learns domain agnostic features. In this retrospective study, we analyzed WSI from three cohorts of prostate cancer patients. Several studies have shown that deep learning-based algorithms can handle Gleason scoring with performance comparable to expert-provided d iagnosis[8–16]. All of these techniques describe accuracy and agreement solely in terms of core-level grading, ignoring gland-level segmentation and the overlap with pathologists’ pixel-level annotations. Our work presents a Deep Learning system (Fig. 1) to detect cancer regions and predict Gleason grades in WSI of prostate CNB. The system was tested for its capacity to distinguish between benign and malignant biopsies, biopsies with low- and high-grade tumors, and group 2 vs. group 3 biopsies
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