Clinical use of machine learning‐based pathomics signature for diagnosis and survival prediction of bladder cancer

• Published in: Cancer science Vol. 112; no. 7; pp. 2905 - 2914
• Main Authors: Chen, Siteng, Jiang, Liren, Zheng, Xinyi, Shao, Jialiang, Wang, Tao, Zhang, Encheng, Gao, Feng, Wang, Xiang, Zheng, Junhua
• Format: Journal Article
• Language: English
• Published: England John Wiley & Sons, Inc 01.07.2021; John Wiley and Sons Inc
• Subjects: Bladder cancer; Cancer; Cystitis; Diagnosis; Genomes; Histopathology; Learning algorithms; Machine learning; Medical prognosis; Original; Pathology; pathomics; Patients; Predictions; prognosis; Survival; Tumors; diagnosis; pathomics; machine learning; prognosis; bladder cancer
• ISSN: 1347-9032; 1349-7006; 1349-7006
• DOI: 10.1111/cas.14927

Traditional histopathology performed by pathologists by the naked eye is insufficient for accurate and efficient diagnosis of bladder cancer (BCa). We collected 643 H&E‐stained BCa images from Shanghai General Hospital and The Cancer Genome Atlas (TCGA). We constructed and cross‐verified automatic diagnosis and prognosis models by performing a machine learning algorithm based on pathomics data. Our study indicated that high diagnostic efficiency of the machine learning‐based diagnosis model was observed in patients with BCa, with area under the curve (AUC) values of 96.3%, 89.2%, and 94.1% in the training cohort, test cohort, and external validation cohort, respectively. Our diagnosis model also performed well in distinguishing patients with BCa from patients with glandular cystitis, with an AUC value of 93.4% in the General cohort. Significant differences were found in overall survival in TCGA cohort (hazard ratio (HR) = 2.09, 95% confidence interval (CI): 1.56‐2.81, P < .0001) and the General cohort (HR = 5.32, 95% CI: 2.95‐9.59, P < .0001) comparing patients with BCa of high risk vs low risk stratified by risk score, which was proved to be an independent prognostic factor for BCa. The integration nomogram based on our risk score and clinicopathologic characters displayed higher prediction accuracy than current tumor stage/grade systems, with AUC values of 77.7%, 83.8%, and 81.3% for 1‐, 3‐, and 5‐y overall survival prediction of patients with BCa. However, prospective studies are still needed for further verifications. We extracted quantitative features from H&E‐stained images and used the features to construct bladder cancer diagnostic and prognostic models based on computational recognition of digital pathology. A machine learning histopathological image signature derived from digital pathology demonstrated high accuracy in bladder cancer diagnosis and survival prediction. The findings highlighted the potential clinical utility of machine learning for histopathologic image analysis in bladder cancer.