TFE 3‐ D ual N et: An Interpretable Foundation Model‐Based Deep Learning Ensemble for Diagnosing TFE3‐Rearranged Renal Cell Carcinoma From Whole‐Slide Images in a Two‐Center Cohort

作者：Yu-Hang Chen, Quanhui Xu, Haohua Yao, Ke-Zhi Liu, Cheng-Peng Gui, Liangmin Fu, Ying‐Han Wang, Jiangquan Zhu, Jun‐Cai Li, Min‐Yu Chen, Kangbo Huang, Han-Sen Lin, Bing Liao, Yun Cao, Jinhuan Wei, Peng-Ju Li, Junhang Luo, Jia‐Zheng Cao · 发表于：Cancer Medicine · 年份：2026 · DOI：10.1002/cam4.72161 · 研究领域：Renal cell carcinoma treatment、AI in cancer detection、Ferroptosis and cancer prognosis

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-DualN...