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.
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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.