Comparison of a machine and deep learning model for automated tumor annotation on digitized whole slide prostate cancer histology

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.

Multimodal data integration for predicting progression risk in castration-resistant prostate cancer using deep learning: a multicenter retrospective study

“…In Lei et al.’s training study of MRI DL involving 396 patients with PCa, training a DL model for PCa classification using pairs of ResNet-50 anti-paradigms improved the generalization and classification abilities of the model ( 39 ). In another pathomics study, texture features captured using the ResNet DL framework were able to better distinguish unique Gleason patterns ( 40 ).…”

Whole slide imaging (WSI) scanner differences influence optical and computed properties of digitized prostate cancer histology

“…These models take images as input and learn textural features to use for quantification, such as automated Gleason pattern analysis in prostate WSI. 39 Low resolution and/or limited color differences on digitizes images may hinder model training as these textural features may not be easily captured.…”

Applications of artificial intelligence in urologic oncology

“…Each study’s inclusion and exclusion criteria were highly heterogeneous, but they all focused on urological cancer. Finally, 58 studies on urological cancers (prostate cancer: 21 [ 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 ], bladder cancer: 20 [ 5 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 ], kidney cancer: 17 [ 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 ]) were identified as shown in Fig. 1 .…”