The fine-tuned model predicted seven outputs using whole slide images (WSIs) (IMAGE)
Caption
Input patches were extracted at 6× downsampling, corresponding to an approximate magnification of 6.7× relative to the original WSI resolution (approximately 40×). Slide-level attention across four WSIs was randomly taken from the independent set. Each row corresponds to one slide, showing the thumbnail, overlay attention, and the highest- and lowest-attention patches at 40× magnification, with boxes covering a large area of the tissue (the highest- and lowest-attention patches are at the center of the boxes’ tissues) to highlight the selected area of the WSIs so that the boxes are visible. The model predicted cancer type, TP53 mutation status, TP53 RNA expression levels, and clinical outcomes for overall survival (OS) and progression-free interval (PFI) events, measured in months. A: Cancer: rectum adenocarcinoma | TP53: 0 [expression (expr) 10.07] | OS: 1 (65.5 months) | PFI: 1 (44.8 months). B: Cancer: head and neck squamous cell carcinoma | TP53: 0 (expr 10.86) | OS: 0 (49.2 months) | PFI: 0 (29.7 months). C: Cancer: brain lower-grade glioma | TP53: 1 (expr 10.31) | OS: 0 (38.2 months) | PFI: 0 (33.0 months). D: Cancer: prostate adenocarcinoma | TP53: 0 (expr 10.03) | OS: 1 (48.5 months) | PFI: 1 (41.1 months).
Credit
The American Journal of Pathology / Chaurasia et al.
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