Background: Molecular information about bladder cancer is significant for treatment and prognosis. The immunohistochemistry (IHC) method is widely used to analyze the specific biomarkers to determine molecular subtypes. However, procedures in IHC and plenty of reagents are time and labor-consuming and expensive. This study established a computer-aid diagnosis system for predicting molecular subtypes, p53 status, and programmed death-ligand 1 (PD-L1) status of bladder cancer with pathological images.
Materials and methods: We collected 119 muscle-invasive bladder cancer (MIBC) patients who underwent radical cystectomy from January 2016 to September 2018. All the pathological sections are scanned into digital whole slide images (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 status, and PD-L1 status of those tumor-positive areas would be identified by three independent convolutional neural networks (CNNs). 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 the recognition of molecular subtypes, the accuracy is 0.94, the sensitivity is 1.00, and the specificity is 0.909. For PD-L1 status recognition, the accuracy is 0.897, the sensitivity is 0.875, and the specificity is 0.913. For p53 status recognition, the accuracy is 0.846, the sensitivity is 0.857, and the specificity is 0.750.
Conclusion: 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.
Keywords: PD-L1; bladder cancer; deep learning; molecular information; molecular subtypes; p53; pathology.
Copyright © 2022 Bai, Wang, Wu, Weng, Han, Xu, Zhang, Chang, Jin, Chen, Luo and Teng.