Mechanosensitive TRPV4 immunohistochemistry improves deep learning-based grading of ductal carcinoma in situ beyond H&E morphology

Ductal carcinoma in situ (DCIS) is a non-invasive breast cancer spanning a biologic continuum from atypical ductal hyperplasia (ADH) to high-grade lesions with variable risk of progression to invasive ductal carcinoma (IDC), yet diagnostic accuracy remains limited when based on morphologic assessment via hematoxylin and eosin (H&E) alone. TRPV4, a mechanosensitive ion channel we previously demonstrated to exhibit pathology-dependent spatial distribution patterns in DCIS, offers a biologically motivated immunohistochemical (IHC) marker that may refine classification beyond routine H&E assessment. We evaluated whether deep learning models trained on TRPV4 IHC outperform those trained on H&E for DCIS classification. We assembled a multi-institutional dataset of paired H&E and TRPV4 IHC whole-slide images from 108 patients (24,248 image tiles), with both stains available for most cases in an internal development cohort (n=69) and an external test cohort (n=39). Each cohort was digitized on different scanners to assess cross-platform robustness. Tiles from annotated regions were grouped into four ordered classes reflecting DCIS progression: normal/benign, ADH/low-grade DCIS, high-grade DCIS, and IDC. Xception and EfficientNet-B0 convolutional neural networks were trained with patient-level 3-fold cross-validation on the development cohort and evaluated as ensembles on the test cohort. On external testing at the patient level, H&E-based ensembles showed moderate performance (macro-F1=0.43-0.44, macro-AUC=0.73-0.80), whereas TRPV4 IHC-based models substantially improved classification (macro-F1=0.68-0.72, macro-AUC=0.91-0.92). Across tile-level predictions, 68-79% of errors were between adjacent grades, consistent with an ordinal DCIS spectrum. Per-class tile-level analyses on the external test cohort showed the greatest improvement with TRPV4 IHC over H&E for ADH/low-grade DCIS (AUC 0.83-0.84 vs 0.70-0.81) and IDC (AUC 0.74-0.79 vs 0.65-0.66), supporting classification across the DCIS progression spectrum. These findings support TRPV4 IHC as a mechanistically grounded complement to H&E, improving deep learning-based DCIS classification in a pilot multi-institutional setting.