Clinical use of a machine learning histopathological image signature in diagnosis and survival prediction of clear cell renal cell carcinoma
| Autor: | Ning Zhang, Jialiang Shao, Hong Yu, Junhua Zheng, Tao Wang, Encheng Zhang, Feng Gao, Siteng Chen, Wang Xiang, Liren Jiang |
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| Rok vydání: | 2020 |
| Předmět: |
China
Cancer Research Machine learning computer.software_genre Disease-Free Survival Machine Learning 03 medical and health sciences 0302 clinical medicine Image Interpretation Computer-Assisted medicine Humans Carcinoma Renal Cell Neoplasm Staging Framingham Risk Score business.industry Hazard ratio Area under the curve Cancer Digital pathology Nomogram Prognosis medicine.disease Kidney Neoplasms Nomograms Clear cell renal cell carcinoma Oncology 030220 oncology & carcinogenesis Cohort Artificial intelligence business computer |
| Zdroj: | International Journal of Cancer. 148:780-790 |
| ISSN: | 1097-0215 0020-7136 |
| DOI: | 10.1002/ijc.33288 |
| Popis: | 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 values of 97.0% and 81.4% in the Cancer Genome Atlas (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 < .0001) and validation cohort (hazard ratio = 9.50, P = .0091). The integrated 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/grade systems. These results indicate the potential clinical use of our machine learning histopathological image signature in diagnosis and survival prediction of ccRCC. |
| Databáze: | OpenAIRE |
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