TFE3‐DualNet: An Interpretable Foundation Model‐Based Deep Learning Ensemble for Diagnosing TFE3‐Rearranged Renal Cell Carcinoma From Whole‐Slide Images in a Two‐Center Cohort
ABSTRACT Background TFE3‐rearranged renal cell carcinoma (TFE3‐rRCC) is a rare, aggressive subtype that predominantly affects adolescents and young adults. Its marked morphologic heterogeneity can delay recognition and downstream confirmatory testing. Methods We assembled a two‐center retrospective cohort of patients < 30 years with renal cell carcinoma (n = 228; 59 TFE3‐rRCC), using fluorescence in situ hybridization (FISH) as the reference standard. Model development was performed in a development cohort (n = 129), followed by independent external validation (n = 99). We developed TFE3‐DualNet, an ensemble of weakly supervised CLAM models trained on routine hematoxylin and eosin (H&E) whole‐slide images (WSIs) using patch embeddings extracted from two pathology foundation models (UNI and CHIEF). We compared performance with three immunohistochemistry (IHC) scoring methods and a feature‐fusion CLAM baseline using concatenated H&E‐derived UNI and CHIEF features, and assessed interpretability by attention mapping. Results In the external validation cohort, TFE3‐DualNet achieved an area under the receiver operating characteristic curve (AUROC) of 0.932, with accuracy 0.879, sensitivity 0.893, and specificity 0.873. The model outperformed IHC scoring methods (AUROC 0.793–0.819; all p < 0.05) and exceeded the feature‐fusion baseline (AUROC 0.906). Attention hotspots localized to diagnostically relevant tumor regions and showed concordance with TFE3 IHC patterns. Conclusions TFE3‐DualNet showed encouraging performance as an interpretable H&E WSI‐based screening model for TFE3‐rRCC in young patients, supporting its potential use to prioritize confirmatory testing and pathologist review in routine diagnostic workflows.