Table 1.
Patient demographics.
Fig 1.
Top: Annotation and tile extraction process. After manual annotation of digitized slides, 3000x3000 pixel tiles are extracted from unique annotated regions. Those tiles are then further divided into 1024x1024 pixel tiles and those that remain within a mask are saved (black tiles indicate unsaved tiles). Middle: Workflow for the ATARI classifier. Quantitative pathomic features calculated from the large tiles are used as input to a compact classification ensemble to predict cancer vs non-cancer in a whole-slide image. Bottom: Workflow for the ResNet101 classifier. 1024x1024 pixel annotated tiles are used as input into the ResNet model to predict non-cancer vs Gleason grade groups. Abbreviations: HGPIN = high-grade prostatic intraepithelial neoplasia; G3 = Gleason pattern 3; G4CG = Gleason pattern 4 cribriform; G4NC = Gleason pattern 4 non-cribriform; G5 = Gleason pattern 5.
Table 2.
Model input data.
Fig 2.
Confusion matrices for the three classification models for both the ResNet101 and ATARI. The ResNet101 was able to distinguish between unique Gleason patterns at higher accuracies that the corresponding ATARI models.
Table 3.
Model performance.
Fig 3.
Ground truth annotation maps compared to the ResNet101 model for all Gleason grades and the three tested ATARI models: all Gleason grades, high- vs low-grade cancer, and cancer vs non-cancer only. ResNet101 model for all Gleason grades and the three ATARI models: all Gleason grades, high- vs low-grade cancer, and cancer vs non-cancer only.