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

Nitin Singhal; Shailesh Soni; Saikiran Bonthu; Nilanjan Chattopadhyay; Pranab Samanta; Uttara Joshi; Amit Jojera; Taher Chharchhodawala; Ankur Agarwal; Mahesh Desai; Arvind Ganpule

doi:10.1038/s41598-022-07217-0

journal article Open Access Mar 01, 2022

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

Nitin Singhal

Shailesh Soni Saikiran Bonthu Nilanjan Chattopadhyay Pranab Samanta

Uttara Joshi Amit Jojera Taher Chharchhodawala Ankur Agarwal

Mahesh Desai Arvind Ganpule

Scientific Reports Vol. 12 No. 1 · Springer Science and Business Media LLC

View at Publisher Save 10.1038/s41598-022-07217-0

Abstract

AbstractGleason 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.

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Details

Published: Mar 01, 2022
Vol/Issue: 12(1)
License: View

Authors

N

Nitin Singhal

AIRA Matrix, Maharashtra, Thane, India

Nilanjan Chattopadhyay

P

Pranab Samanta

Surface Engineering & Tribology Division, Council of Scientific and Industrial Research-Central Mechanical Engineering Research Institute, Durgapur 713209, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India

Taher Chharchhodawala

A

Ankur Agarwal

Career Post Graduate Institute of Dental Science, Lucknow, Uttar Pradesh, India

Cite This Article

Nitin Singhal, Shailesh Soni, Saikiran Bonthu, et al. (2022). A deep learning system for prostate cancer diagnosis and grading in whole slide images of core needle biopsies. Scientific Reports, 12(1). https://doi.org/10.1038/s41598-022-07217-0

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

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