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Preoperative Physiological Reserve and Pathological Response as Independent Predictors of Survival in Esophageal
Ruichen Cui1, Jiahan Cheng1, Xiaohu Hao1
1Institute of Thoracic Oncology and Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, People's Republic of China.
Background:
Despite standardized multimodal treatment, long-term outcomes in patients with locally advanced esophageal squamous cell carcinoma (ESCC) remain variable. We aimed to identify preoperative and pathological factors associated with survival following neoadjuvant therapy and esophagectomy.
Patients And Methods:
A retrospective cohort of 1017 patients who received surgery following neoadjuvant treatment at West China Hospital, Sichuan University, between December 2008 and January 2022 was analyzed. Clinical, pathological, and laboratory data were collected. Overall survival (OS) and disease-free survival (DFS) were evaluated using Cox proportional hazards models. Restricted cubic spline (RCS) analyses were employed to examine nonlinear associations of continuous predictors.
Results:
Independent predictors of worse OS included male sex (hazard ratio [HR] = 1.43, 95% confidence interval [CI]: 1.02-2.01; P < 0.05) and nodal stage N1 (HR = 2.38), N2 (HR = 3.21), and N3 (HR = 5.32; all P < 0.01). Pathological complete response (pCR) was protective (HR = 0.56, 95% CI: 0.41-0.76; P < 0.01). Higher body mass index (BMI) was associated with improved OS (HR = 0.92, 95% CI: 0.89-0.96; P < 0.01), while higher albumin-bilirubin (ALBI) score predicted worse survival (HR = 1.56, 95% CI: 1.20-2.02; P < 0.01). Similar results were observed for DFS. RCS analysis revealed that body mass index (BMI) below 22 kg/m2 and ALBI score above -2.0 were associated with significantly worse outcomes.
Conclusions:
Sex, nodal stage, pCR, BMI, and ALBI were independently associated with long-term survival after neoadjuvant therapy and surgery for ESCC. Integrating pathological and physiological factors may improve preoperative risk assessment and support individualized optimization strategies.
