Deep Learning–Guided Quantitative Analysis Establishes Optimized BRAF V600E Immunohistochemical Criteria for Colorectal Cancer: A Multiplatform Validation Study

Abstract

Accurate detection of BRAF V600E mutation is critical for guiding therapeutic strategies. Unlike other solid tumors, colorectal cancer (CRC) lacks reliable immunohistochemical (IHC) interpretation criteria. This study aimed to establish CRC-specific IHC criteria through quantitative analysis. A cohort of 250 CRC cases with paired IHC and genetic testing (qPCR and next-generation sequencing) results was analyzed. Cross-platform generalization capability of 3 BRAF V600E antibodies was validated. Previously reported IHC criteria were applied and discordant cases were analyzed. A deep learning–based digital pathology platform quantified IHC parameters (H-score, staining intensity, and percentage). Receiver-operating characteristic analysis identified optimal thresholds, which were translated into practical criteria. External validation was performed to confirm generalizability. Cross-platform validation revealed consistent antibody performance across platforms, with absorbance optical density (2.0-2.3) and H-scores (145-160) showing no significant intergroup differences (P > .05). Initial comparison of existing criteria demonstrated 80.4% to 84.8% concordance with molecular testing. Discordant cases exhibited 5 distinct abnormal staining patterns. Artificial intelligence–driven quantification identified H-score 52.675 as the optimal upper cutoff (area under the curve [AUC], 0.938), translated into a positive criterion of >25% 2+ or >15% 3+ stained cells. A negative criterion of <20% 1+ cells was established. Cases with atypical staining patterns required molecular confirmation. The optimized criteria achieved superior concordance in internal (AUC, 0.932) and external validation (AUC, 0.977). This study established refined BRAF V600E IHC criteria for colorectal cancer using precision quantitative analysis. The optimized protocol significantly improves accuracy and standardization in complex real-world scenarios, demonstrating strong potential for broad clinical adoption.