Computational analysis of pathological images enables a better diagnosis of TFE3 Xp11.2 translocation renal cell carcinoma
| العنوان: | Computational analysis of pathological images enables a better diagnosis of TFE3 Xp11.2 translocation renal cell carcinoma |
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| المؤلفون: | Jun Cheng, Jie Zhang, Dong Ni, Liang Cheng, Kun Huang, Wei Shao, Zhi Han, Qianjin Feng, Michael Cheng, Rohit Mehra |
| المصدر: | Nature Communications, Vol 11, Iss 1, Pp 1-9 (2020) Nature Communications |
| بيانات النشر: | Nature Publishing Group, 2020. |
| سنة النشر: | 2020 |
| مصطلحات موضوعية: | 0301 basic medicine, Male, Pathology, medicine.medical_specialty, Science, H&E stain, General Physics and Astronomy, TFE3, urologic and male genital diseases, General Biochemistry, Genetics and Molecular Biology, Article, Translocation, Genetic, Xp11 2 translocation, Machine Learning, 03 medical and health sciences, 0302 clinical medicine, Image processing, Renal cell carcinoma, medicine, Image Processing, Computer-Assisted, Humans, lcsh:Science, Pathological, neoplasms, Carcinoma, Renal Cell, In Situ Hybridization, Fluorescence, Multidisciplinary, business.industry, Basic Helix-Loop-Helix Leucine Zipper Transcription Factors, Cancer, Computational Biology, Diagnostic markers, General Chemistry, medicine.disease, female genital diseases and pregnancy complications, Kidney Neoplasms, 030104 developmental biology, 030220 oncology & carcinogenesis, Histopathology, Female, lcsh:Q, business, Clear cell |
| الوصف: | TFE3 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. Translocation renal cell carcinoma is an aggressive form of renal cancer that is often misdiagnosed to other subtypes. Here the authors demonstrated that by using machine learning and H&E stained whole-slide images, an accurate diagnose of this particular type of renal cancer can be achieved. |
| اللغة: | English |
| تدمد: | 2041-1723 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::51b0869c9dbbd7593e0b57948c73b48eTest http://link.springer.com/article/10.1038/s41467-020-15671-5Test |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....51b0869c9dbbd7593e0b57948c73b48e |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 20411723 |
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