Development and validation of a novel prognostic model for patients with surgically resected esophageal squamous cell carcinoma - Hu, Haiyang; Zhang, Jun; Yan, Hang; Qin, Chao; Guo, Haiyang; Liu, Tao; Tang, Shengjie; Zhou, Haining (Frontiers in Oncology, 2022)
The study aimed to identify risk factors and develop a predictive model for ESCC following surgical resection. 533 ESCC patients who underwent surgical resection from Suining Center Hospital were enrolled in the study. Cox proportional hazards regression and least absolute shrinkage and selection operator (LASSO) regression were performed to identify significant prognostic factors. A prognostic model was constructed and receiver operating characteristic (ROC) curve, concordance index (C-index), and decision cure analysis (DCA) were used to evaluate the discrimination and calibration of the prognostic model. Subsequently, we built a nomogram for overall survival (OS) incorporating the prognostic factors and calibration plot was employed to assess the consistency between the predicted survival and the observed survival. Based on the model risk score, we split the patients into two subgroups: low-risk and high-risk, and we analyzed the survival time of these two groups using Kaplan–Meier(K-M) survival plots.Five independent prognosis factors were identified as independent risk factors for OS in ESCC patients who underwent surgical resection. The C-index, ROC curve, and DCA showed that the prognostic model had good predictive accuracy and discriminatory power in the training cohort and validation cohort than other Clinical features. Nomogram consisting of prognosis factors showed some superior net benefit. K-M survival plots showed significant differences in OS between low-risk and high-risk groups. Similar results were observed in the subgroup analysis based on age, grade and stage. Univariate and multivariate Cox regression analysis revealed that both risk score and risk group are independent prognostic factors in patients cohort. This study put forward a novel prognostic model based on clinical features, biopsy data and blood biomarkers may represent a promising tool for estimating OS in ESCC patients.