Xiao-Yan Zhang1, Wan-Pu Yan2, Yu Sun3, Xiao-Ting Li1, Ying Chen1, Meng-Ying Fan2, Ying Wu3, Zhen Liang2, Hong-Chao Xiong2, Zhi-Long Wang1, Ying-Shi Sun4, Ke-Neng Chen5. 1. Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Radiology, Peking University Cancer Hospital and Institute, Beijing, China. 2. Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), The First Department of Thoracic Surgery, Peking University Cancer Hospital and Institute, Beijing, China. 3. Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Pathology, Peking University Cancer Hospital and Institute, Beijing, China. 4. Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Radiology, Peking University Cancer Hospital and Institute, Beijing, China. sys27@163.com. 5. Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), The First Department of Thoracic Surgery, Peking University Cancer Hospital and Institute, Beijing, China. chenkeneng@bjmu.edu.cn.
Abstract
BACKGROUND: Accurate prediction of treatment response and prognosis before surgery allows prompt therapy adjustment. This study aimed to evaluate the efficacy of computed tomography (CT) signs in predicting treatment response and survival for advanced esophageal squamous cell carcinoma patients who received preoperative chemotherapy. METHODS: This study retrospectively enrolled 135 consecutive patients with preoperative chemotherapy from September 2005 to December 2011. A logistic regression model was used to evaluate the association between pathologic response and CT signs. Overall survival (OS) and disease-free survival (DFS) were estimated using the Kaplan-Meier method, and a Cox proportional hazards model was constructed to determine associations between CT signs after neoadjuvant chemotherapy and survival outcomes. RESULTS: Logistic regression showed that the significant predictors of a poor response were the total number of lymph nodes (LNs) (>6) at baseline [odds ratio (OR) 5.07; 95 % confidence interval (CI) 1.86-13.81; P = 0.002] and the CT value change rate (≤17 %) (OR 2.35; 95 % CI 1.05-5.23; P = 0.037). In the Cox analyses, the significant predictors of OS were preoperative tumor thickness (>10 mm) [hazard ratio (HR) 2.33; 95 % CI 1.36-4; P = 0.002), total number of LNs (>6) (HR 1.88; 95 % CI 1.12-3.17; P = 0.017), and short diameter of the largest LN (>10 mm) (HR 1.87; 95 % CI 1.07-3.28; P = 0.028), whereas only the short diameter of the largest LN was a significant predictor of DFS (HR 2.36; 95 % CI 1.23-4.54; P = 0.01). CONCLUSIONS: CT signs can predict therapeutic efficacy and survival outcomes and provide an opportunity to offer additional treatment options before surgery.
BACKGROUND: Accurate prediction of treatment response and prognosis before surgery allows prompt therapy adjustment. This study aimed to evaluate the efficacy of computed tomography (CT) signs in predicting treatment response and survival for advanced esophageal squamous cell carcinomapatients who received preoperative chemotherapy. METHODS: This study retrospectively enrolled 135 consecutive patients with preoperative chemotherapy from September 2005 to December 2011. A logistic regression model was used to evaluate the association between pathologic response and CT signs. Overall survival (OS) and disease-free survival (DFS) were estimated using the Kaplan-Meier method, and a Cox proportional hazards model was constructed to determine associations between CT signs after neoadjuvant chemotherapy and survival outcomes. RESULTS: Logistic regression showed that the significant predictors of a poor response were the total number of lymph nodes (LNs) (>6) at baseline [odds ratio (OR) 5.07; 95 % confidence interval (CI) 1.86-13.81; P = 0.002] and the CT value change rate (≤17 %) (OR 2.35; 95 % CI 1.05-5.23; P = 0.037). In the Cox analyses, the significant predictors of OS were preoperative tumor thickness (>10 mm) [hazard ratio (HR) 2.33; 95 % CI 1.36-4; P = 0.002), total number of LNs (>6) (HR 1.88; 95 % CI 1.12-3.17; P = 0.017), and short diameter of the largest LN (>10 mm) (HR 1.87; 95 % CI 1.07-3.28; P = 0.028), whereas only the short diameter of the largest LN was a significant predictor of DFS (HR 2.36; 95 % CI 1.23-4.54; P = 0.01). CONCLUSIONS: CT signs can predict therapeutic efficacy and survival outcomes and provide an opportunity to offer additional treatment options before surgery.