Change in Maximal Esophageal Wall Thickness Provides Prediction of Survival and Recurrence in Patients with Esophageal Squamous Cell Carcinoma After Neoadjuvant Chemoradiotherapy and Surgery

PURPOSE: This study aimed to evaluate the relationship of the percentage decrease of maximal esophageal wall thickness with pathological complete response (pCR) and recurrence in esophageal squamous cell carcinoma (ESCC). PATIENTS AND METHODS: A total of 146 ESCC patients treated with neoadjuvant chemoradiotherapy (NCRT) and surgery were included. The prognostic factors for overall survival (OS) and disease-free survival (DFS) were analyzed. The recurrence site, time, and frequency were included in the analysis. The percentage decrease of maximal esophageal wall thickness after NCRT was determined with the formula: [(pre-post)/pre] × 100. RESULTS: Overall, only 42 patients achieved pCR. Multivariable logistic analyses showed that the percentage decrease of maximal esophageal wall thickness (HR: 2.504; 95% CI: 1.112–5.638, P=0.027) was independently correlated with pCR. In multivariable Cox analyses, a ≤40% percentage decrease of maximal esophageal wall thickness was an independent adverse factor for both OS (HR: 1.907, 95% CI: 1.149–3.165; P=0.012) and DFS (HR: 2.054, 95% CI: 1.288–3.277; P=0.003). Compared with patients with a ≤40% percentage decrease, those with a >40% percentage decrease had better 5-year OS (29.0% vs 60.1%, P<0.05) and DFS (27.8% vs 54.4%, P<0.05). Perineural invasion (PNI) was also an unfavorable factor for OS (HR: 2.138, 95% CI: 0.094–4.178; P=0.026). Lymph vessel invasion (HR: 2.874, 95% CI: 1.574–5.248; P=0.001) and PNI (HR: 2.050; 95% CI: 1.044–4.023; P=0.037) were independent prognosticators for DFS. The rates of local and distant recurrence were also significantly difference between those with a percentage decrease of ≤40% and of >40% (P<0.05). CONCLUSION: The percentage decrease of maximal esophageal wall thickness is associated with pCR and recurrence in ESCC patients who undergo NCRT and surgery and can thus be used to independently predict prognosis.