A deep learning system for prostate cancer diagnosis and grading in whole slide images of core needle biopsies

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.

The New Landscape of Diagnostic Imaging with the Incorporation of Computer Vision

“…Jiang et al categorize the implementation of computational pathology in oncology into five purposes, which are tumor diagnosis, subtyping, grading, staging, and prognosis [30]. Thus, we can find applications of these five purposes for breast cancer [30,[105][106][107][108], lung cancer [30,[109][110][111], colorectal cancer [30,[112][113][114][115], gastric cancer [30,116,117], prostate cancer [30,118,119], and thyroid cancer [30,120,121]. Another set of applications of computational pathology lies in the automatic analysis for the identification of rejection in organ transplantation.…”

Segmentation-Based Fractal Texture Analysis and Deep Convolution Networks for Multigrain Scoring of Prostate Cancer

“…Both CNNs attained similar results with mean and standard deviation accuracy of 61.17±7for AlexNet and for GoogleNet results of 60.9±7.4. Other studies adopted more sophisticated approaches as in Singhal et al study (Singhal et al 2022). A multi step iterative system is developed where pathologists first annotate the images, then active learning-based data labeling is performed on datasets.…”

Inference of core needle biopsy whole slide images requiring definitive therapy for prostate cancer

“…As for the global AI competition, the Prostate cANcer graDe Assessment (PANDA) challenge, a group of AI Gleason grading algorithms developed during a global competition generalized well to intercontinental and multinational cohorts with pathologist-level performance [10]. Other works [23,[28][29][30][31][32][33][34] have also looked into developing deep learning algorithms to classify prostate cancer Gleason scores based on histopathological images.…”