Prognostic Nomogram for Predicting Long-Term Overall Survival of Esophageal Cancer Patients Receiving Neoadjuvant Chemoradiotherapy Plus Surgery: A Population-Based Study - Chen, Mingduan; Hong, Zhinuan; Shen, Zhimin; Gao, Lei; Kang, Mingqiang (Frontiers in Surgery, 2022)
Objective: Neoadjuvant chemoradiotherapy (nCRT) plays an important role in patients with locally advanced esophageal cancer (EC). We aimed to determine the prognostic risk factors and establish a reliable nomogram to predict overall survival (OS) based on SEER population. Methods: Patients with esophageal cancer diagnosed in 04-15 in SEER database. The data were divided into the training and validation groups (7:3). Nomograms were constructed according to the Cox proportional-risk model evaluated by operating characteristic (ROC) curves and the area under the curve (AUC). The calibration curves were applied to measure the agreement between predicted and actual outcomes. Decision curve analysis (DCA) was used to evaluate the clinical application value. The optimal cutoff value for the scores from nomograms in terms of OS was determined by X-tile software, and patients were divided into three groups (low risk, mid risk, and high risk). Results: Total 2209 EC patients receiving nCRT were included for further analysis, with 1549 in training cohort and 660 in validation group. Based on COX analysis, sex, marital status, T stage, N stage, M stage, and pathological grade were selected as risk factors. A nomogram model was established to predict 36-, 60, and 84-months OS. The ROC curve and AUC indicated a good discriminative power of the model. The calibration curves showed good agreement between predicted and actual outcomes. Further, DCA demonstrated the effective clinical value of nomogram model. According to the results of X-tile analysis, patients classified as a low-risk subgroup had a better long-term prognosis(P<0.001) in both training cohort and validation cohort. Conclusion: This study established an easy-to-use nomogram model consisting of independent prognostic factors in EC patients receiving nCRT, which could contribute to risk stratification, help clinicians identify patients in high-risk, and provide personalized treatment options.