Faculty Collaboration Database

Comparison of a machine and deep learning model for automated tumor annotation on digitized whole slide prostate cancer histology. PLoS One 2023;18(3):e0278084

Date

03/18/2023

Pubmed ID

36928230

Pubmed Central ID

PMC10019669

DOI

10.1371/journal.pone.0278084

Scopus ID

2-s2.0-85150250210 (requires institutional sign-in at Scopus site)   2 Citations

Abstract

One in eight men will be affected by prostate cancer (PCa) in their lives. While the current clinical standard prognostic marker for PCa is the Gleason score, it is subject to inter-reviewer variability. This study compares two machine learning methods for discriminating between cancerous regions on digitized histology from 47 PCa patients. Whole-slide images were annotated by a GU fellowship-trained pathologist for each Gleason pattern. High-resolution tiles were extracted from annotated and unlabeled tissue. Patients were separated into a training set of 31 patients (Cohort A, n = 9345 tiles) and a testing cohort of 16 patients (Cohort B, n = 4375 tiles). Tiles from Cohort A were used to train a ResNet model, and glands from these tiles were segmented to calculate pathomic features to train a bagged ensemble model to discriminate tumors as (1) cancer and noncancer, (2) high- and low-grade cancer from noncancer, and (3) all Gleason patterns. The outputs of these models were compared to ground-truth pathologist annotations. The ensemble and ResNet models had overall accuracies of 89% and 88%, respectively, at predicting cancer from noncancer. The ResNet model was additionally able to differentiate Gleason patterns on data from Cohort B while the ensemble model was not. Our results suggest that quantitative pathomic features calculated from PCa histology can distinguish regions of cancer; however, texture features captured by deep learning frameworks better differentiate unique Gleason patterns.

Author List

Duenweg SR, Brehler M, Bobholz SA, Lowman AK, Winiarz A, Kyereme F, Nencka A, Iczkowski KA, LaViolette PS

Authors

Peter LaViolette PhD Professor in the Radiology department at Medical College of Wisconsin
Andrew S. Nencka PhD Director, Associate Professor in the Radiology department at Medical College of Wisconsin

MESH terms used to index this publication - Major topics in bold

Humans
Machine Learning
Male
Neoplasm Grading
Prognosis
Prostate
Prostatic Neoplasms