Peng Yizhou, Wo Yang, Liu Pengcheng, Yuan Chongze, Wu Zhigang, Shang Yan, Hong Hui, Sun Yihua
Department of Thoracic Surgery and State Key Laboratory of Genetic Engineering, Fudan University Shanghai Cancer Center, Shanghai, China.
Institute of Thoracic Oncology, Fudan University, Shanghai, China.
J Thorac Dis. 2023 Mar 31;15(3):1142-1154. doi: 10.21037/jtd-22-997. Epub 2023 Mar 6.
A survival benefit from pulmonary resection was observed in octogenarians with non-small cell lung cancer (NSCLC). Meanwhile, the identification of patients who can indeed benefit can be difficult. Therefore, we aimed to establish a web-based predictive model to identify optimal candidates for pulmonary resection.
Octogenarians with NSCLC in Surveillance, Epidemiology and End Results (SEER) database were enrolled and split into the surgery and non-surgery groups based on whether they received pulmonary resection. Propensity-score matching (PSM) was utilized to eliminate the imbalance. Independent prognostic factors were identified. Patients in the surgery group who lived longer than the median cancer-specific survival (CSS) time of the non-surgery group were assumed to benefit from the surgery. The surgery group was further divided into the beneficial group and the non-beneficial group based on the median CSS time of the non-surgery group. Among the surgery group, a nomogram was established through a logistic regression model.
A total of 14,264 eligible patients were extracted, with 4,475 (31.37%) patients receiving pulmonary resection. Surgery was an independent favorable factor of prognosis after PSM (median CSS time: 58 14 months, P<0.001). A total of 750 (70.4%) patients lived longer than 14 months (beneficial group) in the surgery group. Factors including age, gender, race, histologic type, differentiation grade, and tumor-node-metastasis (TNM) stage were used to formulate the web-based nomogram. The precise discrimination and predictive capability of the model were validated through receiver operating characteristic curves, calibration plots, and decision curve analyses.
A web-based predicted model was constructed to distinguish specific patients who can indeed benefit from pulmonary resection among octogenarians with NSCLC.
在患有非小细胞肺癌（NSCLC）的八旬老人中观察到肺切除有生存获益。同时，确定真正能获益的患者可能很困难。因此，我们旨在建立一个基于网络的预测模型，以识别肺切除的最佳候选者。
纳入监测、流行病学和最终结果（SEER）数据库中患有NSCLC的八旬老人，并根据他们是否接受肺切除分为手术组和非手术组。采用倾向得分匹配（PSM）来消除不平衡。确定独立的预后因素。手术组中生存期长于非手术组癌症特异性生存（CSS）时间中位数的患者被认为从手术中获益。根据非手术组的CSS时间中位数，手术组进一步分为获益组和非获益组。在手术组中，通过逻辑回归模型建立列线图。
共提取了14264例符合条件的患者，其中4475例（31.37%）接受了肺切除。PSM后手术是预后的独立有利因素（CSS时间中位数：58±14个月，P<0.001）。手术组中共有750例（70.4%）患者生存期超过14个月（获益组）。包括年龄、性别、种族、组织学类型、分化程度和肿瘤-淋巴结-转移（TNM）分期等因素被用于制定基于网络的列线图。通过受试者操作特征曲线、校准图和决策曲线分析验证了模型的精确区分和预测能力。
构建了一个基于网络的预测模型，以区分在患有NSCLC的八旬老人中真正能从肺切除中获益的特定患者。
