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
03/18/2023Pubmed ID
36928230Pubmed Central ID
PMC10019669DOI
10.1371/journal.pone.0278084Scopus ID
2-s2.0-85150250210 (requires institutional sign-in at Scopus site) 5 CitationsAbstract
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 PSAuthors
Samuel Bobholz PhD Assistant Professor in the Radiology department at Medical College of WisconsinSavannah R. Duenweg Research Scientist I in the Radiology department at Medical College of Wisconsin
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
HumansMachine Learning
Male
Neoplasm Grading
Prognosis
Prostate
Prostatic Neoplasms