Computational analysis of pathological images enables a better diagnosis of TFE3 Xp11.2 translocation renal cell carcinoma

dc.contributor.authorCheng, Jun
dc.contributor.authorHan, Zhi
dc.contributor.authorMehra, Rohit
dc.contributor.authorShao, Wei
dc.contributor.authorCheng, Michael
dc.contributor.authorFeng, Qianjin
dc.contributor.authorNi, Dong
dc.contributor.authorHuang, Kun
dc.contributor.authorCheng, Liang
dc.contributor.authorZhang, Jie
dc.contributor.departmentMedicine, School of Medicineen_US
dc.date.accessioned2020-06-12T20:12:19Z
dc.date.available2020-06-12T20:12:19Z
dc.date.issued2020
dc.description.abstractTFE3 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_US
dc.eprint.versionFinal published versionen_US
dc.identifier.citationCheng, 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/s41467-020-15671-5en_US
dc.identifier.urihttps://hdl.handle.net/1805/22961
dc.language.isoen_USen_US
dc.publisherNature Researchen_US
dc.relation.isversionof10.1038/s41467-020-15671-5en_US
dc.relation.journalNature Communicationsen_US
dc.rightsAttribution 4.0 International*
dc.rights.urihttps://creativecommons.org/licenses/by/4.0*
dc.sourcePMCen_US
dc.subjectImage processingen_US
dc.subjectMachine learningen_US
dc.subjectDiagnostic markersen_US
dc.subjectRenal cell carcinomaen_US
dc.titleComputational analysis of pathological images enables a better diagnosis of TFE3 Xp11.2 translocation renal cell carcinomaen_US
dc.typeArticleen_US
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