Study on Molecular Information Intelligent Diagnosis and Treatment of Bladder Cancer on Pathological Tissue Image - Bai, Yanfeng; Wang, Huogen; Wu, Xuesong; Weng, Menghan; Han, Qingmei; Xu, Liming; Zhang, Han; Chang, Chengdong; Jin, Chaohui; Chen, Ming; Luo, Kunfeng; Teng, Xiaodong (Frontiers in Medicine, 2022)
Background: Molecular information of bladder cancer is significant for treatment and prognosis. Immunohistochemistry (IHC) method is widely used to analyze the specific biomarkers to determine molecular subtype. However, procedures in IHC and plenty of reagent are time & labor-consuming and expensive. This study established a computer-aid diagnose system for predicting molecular subtypes, p53 status, and PD-L1 status of bladder cancer with pathological images. Methods: We collected 119 muscle-invasive bladder cancer patients who underwent radical cystectomy between January 2016 to September 2018. All the pathological sections are scanned into digital WSIs and the IHC results of adjacent sections were recorded as the label of the corresponding slide. The tumor areas are first segmented, then molecular subtypes, p53 and PD-L1 status of those tumor-positive areas would be identified by three independent Convolutional Neural Networks. We measured the performance of this system for predicting molecular subtypes, p53 status, and PD-L1 status of bladder cancer with accuracy, sensitivity, and specificity. Results: For molecular subtypes recognition, the accuracy is 0.94, the sensitivity is 1.00, the specificity is 0.909. For PD-L1 status recognition, the accuracy is 0.897, the sensitivity is 0.875, the specificity is 0.913. For p53 status recognition, the accuracy is 0.846, the sensitivity is 0.857, the specificity is 0.750. Conclusions: Our computer-aided diagnosis system can provide a novel and simple assistant tool to obtain the molecular subtype, PD-L1 status, and p53 status. It can reduce the workload of pathologists and the medical cost.