Clinical use of a machine learning histopathological image signature in diagnosis and survival prediction of clear cell renal cell carcinoma.

Due to the complicated histopathological characteristics of renal neoplasms, traditional distinguishing of clear-cell renal-cell carcinoma (ccRCC) by naked eyes of experienced pathologist remains labor-intensive and time-consuming. Here, we extracted quantitative features of hematoxylin-eosin stained images using CellProfiler and performed machine-learning method to develop and verify a novel computational recognition of digital pathology for diagnosis and prognosis of ccRCC patients in the training, test and external validation cohort. The diagnostic model based on digital pathology could accurately distinguish ccRCC from normal renal tissues, with area under the curve (AUC) of 96.0%, 94.5% and 87.6% in the training, test and external validation cohorts, respectively. It could also accurately distinguish ccRCC from other pathological types of renal cancer, with AUC of 97.0% and 81.4% in the TCGA cohort and General cohort. We next developed and verified a computational recognition prognosis model with risk score. There was a significant difference in disease-free survival comparing patients with high vs low risk score in training cohort (hazard ratio = 2.72, P < 0.0001) and validation cohort (hazard ratio = 9.50, P = 0.0091). The integration nomogram based on our computational recognition risk score and clinicopathologic factors demonstrated excellent survival prediction for ccRCC patients, with increased accuracy by 6.6% in patients from Shanghai General Hospital and by 2.5% in patients from TCGA cohort when compared to current tumor stages/grades systems. These results indicate the potential clinical use of our machine learning histopathological image signature in diagnosis and survival prediction of ccRCC.

International journal of cancer. 2020 Sep 07 [Epub ahead of print]

Siteng Chen, Ning Zhang, Liren Jiang, Feng Gao, Jialiang Shao, Tao Wang, Encheng Zhang, Hong Yu, Xiang Wang, Junhua Zheng

Department of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China., Department of Urology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China., Department of Pathology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.