Establishment and validation of a prognostic risk classification for patients with stage T1-3N0M0 esophageal squamous cell carcinoma

Abstract

Background: No existing methods can accurately predict postoperative prognosis in patients with stage T1-3N0M0 esophageal squamous cell carcinoma (ESCC). We explored the postoperative prognosis of such patients using a new prognostic risk classification system (Esorisk). Methods: Patients with stage T1-3N0M0 ESCC from two cancer centers—Sun Yat-sen University Cancer Center (training cohort: N=819) and Henan Cancer Hospital (external validation cohort: N=177)—who underwent esophagectomy between 1995 and 2015 were included. We integrated significant risk factors for death events into multivariable logistic regression models and applied them to the training cohort to build Esorisk. The parsimonious aggregate Esorisk score was calculated for each patient; the training set was divided into three prognostic risk classes according to the 33rd and 66th Esorisk score percentiles. Esorisk’s association with cancer-specific survival (CSS) was assessed using Cox proportional hazard regression analyses. Results: The Esorisk model was: [10 + 0.023 × age + 0.517 × drinking history – 0.012 × hemoglobin – 0.042 × albumin – 0.032 × lymph nodes removed]. Patients were grouped into three classes—Class A (5.14-7.26, low risk), Class B (7.27-7.70, middle risk), and Class C (7.71-9.29, high risk). In the training group, the 5-year CSS decreased across categories (A: 63%, B: 52%, C: 30%; log-rank P<0.001). Similar findings were observed in the validation group (A: 81%, B: 69%, C: 48%; log-rank P<0.001). Esorisk stratified the CSS in patients from two cohorts with stage pT1 (P=0.0029), pT2 (P<0.001), or pT3 (P<0.001) disease. Cox proportional hazard regression analyses showed that the Esorisk aggregate score remained significantly associated with CSS in the training cohort (P<0.001) and validation cohort (P=0.001) after adjusting for pT stage and tumor differentiation. Conclusion: We established and validated a novel prognostic risk classification that can predict the CSS for patients with T1-3N0M0 ESCC. This may help clinicians identify patient subgroups with poor prognosis.