医学
嫌色细胞
组织病理学
肾细胞癌
清除单元格
病理
肾透明细胞癌
金标准(测试)
H&E染色
活检
放射科
预测值
免疫组织化学
内科学
作者
Michael Fenstermaker,Scott A. Tomlins,Karandeep Singh,Jenna Wiens,Todd M. Morgan
出处
期刊:Urology
[Elsevier]
日期:2020-10-01
卷期号:144: 152-157
被引量:34
标识
DOI:10.1016/j.urology.2020.05.094
摘要
To develop and test the ability of a convolutional neural network (CNN) to accurately identify the presence of renal cell carcinoma (RCC) on histopathology specimens, as well as differentiate RCC histologic subtype and grade.Digital hematoxylin and eosin stained biopsy images were downloaded from The Cancer Genome Atlas. A CNN model was trained on 100 um2 samples of either normal (3000 samples) or RCC (12,168 samples) tissue samples from 42 patients. RCC specimens included clear cell, chromophobe, and papillary histiotypes, as well as tissue of Fuhrman grades 1 through 4. Model testing was performed on an additional held-out cohort of benign and RCC specimens. Model performance was assessed on the basis of diagnostic accuracy, sensitivity, specificity, positive predictive value, and negative predictive value.The CNN model achieved an overall accuracy of 99.1% in the testing cohort for distinguishing normal parenchyma from RCC (sensitivity 100%, specificity 97.1%). Accuracy for distinguishing between clear cell, papillary, and chromophobehistiotypes was 97.5%. Accuracy for predicting Fuhrman grade was 98.4%.CNNs are able to rapidly and accurately identify the presence of RCC, distinguish RCC histologic subtypes, and identify tumor grade by analyzing histopathology specimens.
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