Accuracy of Predicting Residual Disease and Disease Progression During Active Surveillance for Esophageal Cancer

Ann Surg Oncol. 2025 Oct 23. doi: 10.1245/s10434-025-18531-y. Online ahead of print.

Abstract

Background: To date, active surveillance has been non-inferior to standard surgery for patients with esophageal cancer, achieving a clinical complete response (CCR) after neoadjuvant chemoradiotherapy (nCRT). However, two thirds of patients have residual disease detected 12 weeks after nCRT and undergo surgery. At 12 weeks, nearly half of the patients with CCR will experience locoregional regrowth. This study aimed to identify routine predictive factors for achieving (sustained) CCR to improve patient selection for active surveillance.

Methods: Data from the SANO trial were analyzed, including data of patients who underwent nCRT for esophageal cancer. Logistic regression assessed predictors of CCR at 12 weeks, with potential factors including age, sex, WHO performance status, clinical T and N categories, histology, differentiation grade, tumor location, and tumor length. For patients with CCR in active surveillance, cause-specific proportional hazards regression identified predictors of sustained CCR (no locoregional regrowth, dissemination, or death) during a minimum 3-year follow-up period. Discrimination was quantified using the concordance statistic (c-statistic) with bootstrap validation.

Results: Of 750 patients, 274 (37 %) achieved CCR at 12 weeks. Higher cN category was associated with lower likelihood of CCR (cN2-3 vs cN0: odds ratio [OR], 0.57; 95 % confidence interval [CI], 0.37-0.88; P < 0.01; c-statistic, 0.56). Among 198 patients in active surveillance, 25 % had sustained CCR after a median follow-up period of 54 months (interquartile range [IQR],46-58 months). Higher cN category (cN2-3 vs cN0; HR, 2.08; 95 % CI, 1.25-3.48; P < 0.01) was associated with non-sustained CCR (c-statistic, 0.58).

Conclusion: Standard clinical parameters poorly predict clinical response after nCRT. Additional predictive parameters and better diagnostic tests are needed to improve patient selection for active surveillance.