Cheng, JunHan, ZhiMehra, RohitShao, WeiCheng, MichaelFeng, QianjinNi, DongHuang, KunCheng, LiangZhang, Jie2020-06-122020-06-122020Cheng, J., Han, Z., Mehra, R., Shao, W., Cheng, M., Feng, Q., Ni, D., Huang, K., Cheng, L., & Zhang, J. (2020). Computational analysis of pathological images enables a better diagnosis of TFE3 Xp11.2 translocation renal cell carcinoma. Nature communications, 11(1), 1778. https://doi.org/10.1038/s41467-020-15671-5https://hdl.handle.net/1805/22961TFE3 Xp11.2 translocation renal cell carcinoma (TFE3-RCC) generally progresses more aggressively compared with other RCC subtypes, but it is challenging to diagnose TFE3-RCC by traditional visual inspection of pathological images. In this study, we collect hematoxylin and eosin- stained histopathology whole-slide images of 74 TFE3-RCC cases (the largest cohort to date) and 74 clear cell RCC cases (ccRCC, the most common RCC subtype) with matched gender and tumor grade. An automatic computational pipeline is implemented to extract image features. Comparative study identifies 52 image features with significant differences between TFE3-RCC and ccRCC. Machine learning models are built to distinguish TFE3-RCC from ccRCC. Tests of the classification models on an external validation set reveal high accuracy with areas under ROC curve ranging from 0.842 to 0.894. Our results suggest that automatically derived image features can capture subtle morphological differences between TFE3-RCC and ccRCC and contribute to a potential guideline for TFE3-RCC diagnosis.en-USAttribution 4.0 InternationalImage processingMachine learningDiagnostic markersRenal cell carcinomaArticle