A Survival Prediction Nomogram for Esophageal Squamous Cell Carcinoma Treated with Neoadjuvant Chemoradiotherapy Followed by Surgery

Cancer Manag Res. 2021 Oct 9:13:7771-7782. doi: 10.2147/CMAR.S329687. eCollection 2021.

Abstract

Background: Neoadjuvant chemoradiotherapy (NCRT) followed by surgery is a component of the standard treatment for resectable locally advanced esophageal squamous cell carcinoma (ESCC), and the parameters for survival prediction are not clear yet. Our study aimed to construct a survival prediction nomogram for ESCC with NCRT followed by surgery.

Methods: We analyzed hematological parameters and related-derivative indexes from 122 ESCC patients treated with NCRT followed by surgery. Univariate and multivariate Cox survival analyses were performed to identify independent prognostic factors to establish a nomogram and predict overall survival (OS). The predictive value of the nomogram for OS was evaluated by the concordance index (C-index), decision curve analysis (DCA), the clinical impact curve (CIC), net reclassification improvement (NRI), and integrated discrimination improvement (IDI).

Results: The pretreatment nutritional candidate, prognostic nutrition index, inflammation-related absolute monocyte count and TNM staging were entered into the nomogram for ESCC with NCRT followed by surgery. The C-index of the nomogram for OS was 0.790 (95% CI = 0.688-0.893), which was higher than that of TNM staging (0.681; 95% CI = 0.565-0.798, P = 0.026). The DCA, CIC, NRI, and IDI of the nomogram showed moderate improvement in predicting survival. Based on the cut point calculated according to the constructed nomogram, the high-risk group had poorer OS than that of the low-risk group (P < 0.05).

Conclusion: A novel nomogram based on nutrition- and inflammation-related indicators might help predict the survival of ESCC treated with NCRT followed by surgery.

Keywords: esophageal squamous cell carcinoma; neoadjuvant chemoradiotherapy; nomogram; prognosis; surgery; survival.

Grants and funding

This work was funded by grants from Science and Technology Planning Project of Shantou City (No. 200605115266724) and 2020 Li Ka Shing FoundationCross-Disciplinary Research Grant (No. 2020LKSFG01B).