Abstract Background: Deep learning models trained on hematoxylin-eosin (H its Macenko stain vectors and 99th-percentile concentration parameters were used for harmonization. All accepted WSIs were normalized to this reference. From normalized slides, 256×256 tiles (stride 256) with tissue fraction ≥70% and artifact fraction ≤2% were extracted to train a slide-level binary outcome model. Results: Of 143 WSIs, 140 (97.9%) passed QC; three were excluded. Retained slides had a median resolution of 33,792 × 46,080 pixels. From 128 QC-passing cases, we obtained 215,220 analysis-ready tiles (median 1,590 per slide). By comparison, a simpler Otsu-based pipeline produced 249,073 tiles; thus, QC-aware masking removed 13.6% of candidates—primarily low-tissue or artifact-laden regions—without diminishing tumor or stromal coverage. Stain-normalized previews demonstrated highly consistent H Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 81.
Rad et al. (2026) studied this question.