Yang Wenlei, Liu Fangfang, Xu Ruiping, Yang Wei, He Yu, Liu Zhen, Zhou Fuyou, Heng Fanxiu, Hou Bolin, Zhang Lixin, Chen Lei, Zhang Fan, Cai Fen, Xu Huawen, Lin Miaoping, Liu Mengfei, Pan Yaqi, Liu Ying, Hu Zhe, Chen Huanyu, He Zhonghu, Ke Yang
Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Laboratory of Genetics, Peking University Cancer Hospital & Institute, Beijing, People's Republic of China.
Anyang Cancer Hospital, Anyang, Henan Province, People's Republic of China.
Ann Surg. 2023 Jan 1;277(1):e61-e69. doi: 10.1097/SLA.0000000000004958. Epub 2021 Jun 2.
To construct a prediction model for more precise evaluation of prognosis which will allow personalized treatment recommendations for adjuvant therapy in patients following resection of ESCC.
Marked heterogeneity of patient prognosis and limited evidence regarding survival benefit of various adjuvant therapy regimens pose challenges in the clinical treatment of ESCC.
Based on comprehensive clinical data obtained from 4129 consecutive patients with resected ESCC in a high-risk region in China, we identified predictors for overall survival through a 2-phase selection based on Cox proportional hazard regression and minimization of Akaike information criterion. The model was internally validated using bootstrapping and externally validated in 1815 patients from a non-high-risk region in China.
The final model incorporates 9 variables: age, sex, primary site, T stage, N stage, number of lymph nodes harvested, tumor size, adjuvant treatment, and hemoglobin level. A significant interaction was also observed between N stage and adjuvant treatment. N1+ stage patients were likely to benefit from addition of adjuvant therapy as opposed to surgery alone, but adjuvant therapy did not improve overall survival for N0 stage patients. The C -index of the model was 0.729 in the training cohort, 0.723 after bootstrapping, and 0.695 in the external validation cohort. This model outperformed the seventh edition American Joint Committee on Cancer staging system in prognostic prediction and risk stratification.
The prediction model constructed in this study may facilitate precise prediction of survival and inform decision-making about adjuvant therapy according to N stage.
构建一个预测模型,以更精确地评估预后,从而为食管鳞状细胞癌(ESCC)切除术后患者的辅助治疗提供个性化的治疗建议。
患者预后存在显著异质性,且关于各种辅助治疗方案生存获益的证据有限,这给ESCC的临床治疗带来了挑战。
基于从中国一个高危地区连续4129例接受ESCC切除术的患者获得的综合临床数据,我们通过基于Cox比例风险回归和最小化赤池信息准则的两阶段选择,确定了总生存的预测因素。该模型通过自举法进行内部验证,并在中国一个非高危地区的1815例患者中进行外部验证。
最终模型纳入了9个变量:年龄、性别、原发部位、T分期、N分期、清扫淋巴结数量、肿瘤大小、辅助治疗和血红蛋白水平。还观察到N分期与辅助治疗之间存在显著交互作用。与单纯手术相比,N1+期患者可能从辅助治疗中获益,但辅助治疗并未改善N0期患者的总生存。该模型在训练队列中的C指数为0.729,自举后为0.723,在外部验证队列中为0.695。该模型在预后预测和风险分层方面优于美国癌症联合委员会第七版分期系统。
本研究构建的预测模型可能有助于精确预测生存,并根据N分期为辅助治疗决策提供参考。