Deep learning pathological microscopic features in endemic nasopharyngeal cancer: Prognostic value and protentional role for individual induction chemotherapy

Abstract

Abstract Background To explore the prognostic value and the role for treatment decision of pathological microscopic features in patients with nasopharyngeal carcinoma (NPC) using the method of deep learning. Methods The pathological microscopic features were extracted using the software QuPath (version 0.1.3. Queen's University) in the training cohort (Guangzhou training cohort, n = 843). We used the neural network DeepSurv to analyze the pathological microscopic features (DSPMF) and then classified patients into high‐risk and low‐risk groups through the time‐dependent receiver operating characteristic (ROC). The prognosis accuracy of the pathological feature was validated in a validation cohort (n = 212). The primary endpoint was progression‐free survival (PFS). Results We found 429 pathological microscopic features in the H&E image. Patients with high‐risk scores in the training cohort had shorter 5‐year PFS (HR 10.03, 6.06‐16.61; P < .0001). The DSPMF (C‐index: 0.723) had the higher C‐index than the EBV DNA (C‐index: 0.612) copies and the N stage (C‐index: 0.593). Furthermore, induction chemotherapy (ICT) plus concomitant chemoradiotherapy (CCRT) had better 5‐year PFS to those received CCRT (P < .0001) in the high‐risk group. Conclusion The DSPMF is a reliable prognostic tool for survival risk in patients with NPC and might be able to guide the treatment decision.

Keywords

• DeepSurv
• induction chemotherapy
• nasopharyngeal carcinoma
• pathological microfeatures