@article{YANG2023, 
author = {Wenlei YANG and Fangfang LIU and Ruiping XU and Wei YANG and Yu HE and Zhen LIU and Fuyou ZHOU and Fanxiu HENG and Bolin HOU and Lixin ZHANG and Lei CHEN and Fan ZHANG and Fen CAI and Huawen XU and Miaoping LIN and Mengfei LIU and Yaqi PAN and Ying LIU and Zhe HU and Huanyu CHEN and Zhonghu HE and Yang KE},
title = {Development and Validation of A Prognosis Prediction Model for Esophageal Squamous Cell Carcinoma Patients Treated with Esophagectomy: A Multicenter Real-world Cohort Study},
year = {2023},
journal = {Medical Journal of Peking Union Medical College Hospital},
volume = {14},
number = {1},
pages = {101-113},
keywords = {esophageal squamous cell carcinoma, real-world study, prognosis prediction model, clinical treatment decision-making},
url = {https://www.sciopen.com/article/10.12290/xhyxzz.2022-0496},
doi = {10.12290/xhyxzz.2022-0496},
abstract = {ObjectiveTo construct and validate a prognosis prediction model and a risk stratification tool for more precise and individualized evaluation of prognosis for patients following resection of esophageal squamous cell carcinoma (ESCC), and provide real-world evidence for informing optimal decision-making about adjuvant therapy.MethodsThe comprehensive clinical data and follow-up data were collected from consecutive patients with ESCC in the Anyang Cancer Hospital (Anyang center) from May 31, 2011 to July 31, 2018, and in the Cancer Hospital of Shantou University Medical College (Shantou center) from August 1, 2009 to December 31, 2018. Patients from the Anyang center formed the training cohort, and a two-phase selection based on backward stepwise multivariable Cox proportional hazard regression and minimization of AIC was used to construct prediction model for overall survival (OS). Bootstrap with 1 000 resamples was used for internal validation, and cohort from the Shantou center was used for external validation. Furthermore, a risk stratification tool was constructed according to the tertiles of the total points derived from nomogram in the training cohort.ResultsA total of 4 171 eligible patients were included in the training cohort, and 1 895 patients were included in the validation cohort. The final model incorporated nine variables: age, sex, primary tumor location, T stage, N stage, number of lymph nodes harvested, tumor size, adjuvant treatment, and preoperative hemoglobin level. A significant interaction was observed between N stage and adjuvant treatment (P < 0.001), which means that N+ stage patients were likely to benefit from addition of adjuvant therapy as opposed to surgery alone, but adjuvant therapy did not improve OS for N0 stage patients. The C-index of the model was 0.728 (95% CI: 0.713-0.742) in the training cohort, 0.722 (95% CI: 0.711-0.739) after bootstrapping, and 0.679 (95% CI: 0.662-0.697) in the external validation cohort. Calibration plots demonstrated favorable agreement between model prediction and actual observation for 1-, 3- and 5-year OS. In both training and validation cohorts, this model outperformed the seventh edition of the AJCC TNM (tumor, lymph node, and metastasis) staging system in terms of the accuracy of prognostic prediction (P < 0.05). Moreover, within each TNM staging group, this model achieved ideal risk stratification.ConclusionsThe prediction model constructed in this study may provide individualized survival prediction for patients with resected ESCC in China. This study also demonstrated that the N stage may be a fundamental determinant in planning postoperative adjuvant therapy for ESCC patients.}
}