Identification of recurrence risk factors and development of personalized follow-up strategies for esophageal squamous cell carcinoma patients after neoadjuvant therapy and surgery: a retrospective cohort study.

BACKGROUND

Recurrence after neoadjuvant therapy and R0 resection remains common in esophageal squamous cell carcinoma (ESCC), yet validated baseline models for predicting recurrence using routinely available clinical and inflammatory/nutritional markers are limited, and how recurrence risk evolves with additional recurrence-free survival time has rarely been described in this setting. This study aimed to identify high-risk factors for recurrence in ESCC and to construct a prognostic prediction model. We also aimed to evaluate the dynamic survival and recurrence risks of ESCC and, as a complementary descriptive analysis, to formulate a hypothesis-generating personalized follow-up framework using conditional survival (CS).

METHODS

We retrospectively analyzed 408 ESCC patients who underwent neoadjuvant therapy followed by R0 resection at Fujian Cancer Hospital. Candidate predictors were screened by univariate logistic regression (P<0.05) and entered into multivariate logistic regression to identify independent recurrence risk factors and construct a nomogram; performance was assessed within the derivation cohort using receiver operating characteristic (ROC) curve, calibration curve and decision-curve analysis. Kaplan-Meier analysis compared overall survival (OS), progression-free survival (PFS), local-regional recurrence-free survival (LRRFS) and distant metastasis-free survival (DMFS) across nomogram-based risk groups, and CS was used to describe how recurrence-free probabilities changed with additional survival time.

RESULTS

During a median follow-up of 28 months, recurrence occurred in 156 patients (38.2%). Multivariate Cox analysis identified age at diagnosis, tumor length, tumor thickness, N stage, and lymphocyte-to-monocyte ratio (LMR) as independent risk factors for recurrence. The time-dependent area under the curve (AUC) was 0.723. Calibration curves showed good agreement. Decision curve analysis (DCA) confirmed net clinical benefit. Patients were stratified into low-, moderate-, and high-risk groups with significant survival differences (all P<0.001). CS analysis revealed increasing recurrence-free probability over time, most pronounced in high-risk patients surviving beyond 2 years. We propose a risk-adapted follow-up framework as a hypothesis for future prospective testing.

CONCLUSIONS

This study established a clinically applicable risk stratification system for post-surgical ESCC patients, integrating baseline risk factors with dynamic survival analysis. The model has not been externally validated, and the proposed follow-up framework is hypothesis-generating and requires prospective validation before clinical use. This work provides a foundation for developing personalized surveillance protocols that could optimize resource utilization and potentially improve early recurrence detection.