Ma Huiyun, Li Shuangjiang, Zhu Ying, Zhang Wenbiao, Luo Yingwei, Liu Baocong, Gou Wenjing, Xie Chuanmiao, Li Qiong
Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, Guangdong, China.
Department of Endoscopy, Sun Yat-sen University Cancer Center, Guangzhou, 510060, Guangdong, China.
Ann Surg Oncol. 2023 Jun;30(6):3769-3778. doi: 10.1245/s10434-023-13248-2. Epub 2023 Feb 23.
There is no simple and definitive way to predict the prognosis of synchronous multiple primary lung cancer (SMPLC). In this study, we developed a clinical prognostic score for predicting the survival of patients with SMPLC.
This study included 206 patients with SMPLC between 2011 and 2020 at three hospitals. Kaplan-Meier analysis was used to determine the optimal cutoff values for the quantitative chest computed tomography (CT) parameters. Multivariable Cox proportional hazards regression was carried out to identify independent prognostic factors for predicting overall survival (OS) and disease-free survival (DFS). The time-dependent receiver operating characteristic curve was analyzed to evaluate the prognostic performance.
A CT-based prognostic score (CTPS) comprising six chest CT parameters was developed. Compared with T stage, CTPS had a higher prediction accuracy for OS and DFS. All C-indices of the model reached a satisfactory level in both the development and validation cohorts. Significant differences in the OS and DFS curves were observed when the patients were stratified into different risk groups. The high-risk group (CTPS of 5-6) had poorer survival than the low-risk group (CTPS of 0-4).
The developed CTPS and the corresponding risk stratification system are valid for predicting the survival of patients with SMPLC.
目前尚无简单且确定的方法来预测同步性多原发性肺癌（SMPLC）的预后。在本研究中，我们开发了一种临床预后评分系统，用于预测SMPLC患者的生存情况。
本研究纳入了2011年至2020年间在三家医院就诊的206例SMPLC患者。采用Kaplan-Meier分析确定胸部计算机断层扫描（CT）定量参数的最佳临界值。进行多变量Cox比例风险回归分析，以确定预测总生存期（OS）和无病生存期（DFS）的独立预后因素。分析时间依赖性受试者工作特征曲线以评估预后性能。
开发了一种基于CT的预后评分（CTPS），该评分包含六个胸部CT参数。与T分期相比，CTPS对OS和DFS具有更高的预测准确性。该模型在开发队列和验证队列中的所有C指数均达到令人满意的水平。当将患者分层为不同风险组时，观察到OS和DFS曲线存在显著差异。高风险组（CTPS为5 - 6）的生存率低于低风险组（CTPS为0 - 4）。
所开发的CTPS及相应的风险分层系统对于预测SMPLC患者的生存情况是有效的。
