A Clinical Scoring Model to Predict the Effect of Induction Chemotherapy With Definitive Concurrent Chemoradiotherapy on Esophageal Squamous Cell Carcinoma Prognosis - Li, Yang; Du, Qingwu; Wei, Xiaoying; Guo, Zhoubo; Lei, Tongda; Li, Yanqi; Han, Dong; Wu, Xiaoyue; Zhang, Kunning; Zhang, Tian; Chen, Xi; Dong, Jie; et al. (Frontiers in Oncology, 2021)
Abstract Purpose The aim of the study was to compare the clinical outcomes of induction chemotherapy (IC) followed by definitive concurrent chemoradiotherapy (dCCRT) versus chemoradiotherapy alone in patients with esophageal squamous cell carcinoma (ESCC) on the basis of a clinical scoring model. Methods A retrospective review of 599 patients with ESCC treated with dCCRT at our institution from 2010 to 2019 was conducted. The patients were divided into two groups based on whether they received IC. A clinical scoring model was performed using the significant variables obtained from the multivariate analysis. The PFS and OS rates were estimated using the Kaplan–Meier method. Results During the study period, 182 patients receiving IC followed by dCCRT and 417 dCCRT alone were identified. No significant differences in the PFS and OS rates were observed between the IC group (P=0.532) and the non-IC group (P=0.078). A clinical scoring model was constructed based on independent prognostic factors with scores ranging from 0 to 10.4. The patients were divided into high- and low-risk groups by using the median score as the cutoff value. The PFS rate of patients receiving IC was higher than that of patients treated without IC (P=0.034), while there was no improvement in the OS rate (P=0.794) in the high-risk group. No significant differences in the PFS (P=0.207) or OS (P=0.997) rate were found between the two treatment groups in the low-risk group. Conclusions The addition of IC followed by dCCRT for patients with ESCC might be associated with better PFS rates based on a clinical scoring model but has no impact on OS rates. Further prospective studies are warranted for the validation of this model.