Zheng Yuting, Luo Qinyue, He Yimeng, Li Hanting, Huang Mengting, Ding Chengyu, Han Xiaoyu, Shi Heshui
Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Hubei Provincial Clinical Research Center for Precision Radiology & Interventional Medicine, Wuhan, China.
Transl Lung Cancer Res. 2025 Aug 31;14(8):3009-3023. doi: 10.21037/tlcr-2025-327. Epub 2025 Aug 26.
The predictive value of body composition and inflammatory parameters in patients with resectable non-small cell lung cancer (NSCLC) undergoing neoadjuvant chemoimmunotherapy remains poorly defined. The study sought to evaluate the association between computed tomography (CT)-based body composition, inflammatory markers, and survival outcomes in NSCLC patients following neoadjuvant chemoimmunotherapy.
This retrospective study included resectable NSCLC patients undergoing neoadjuvant chemoimmunotherapy from June 2019 to March 2023. CT images were collected at three levels (T4, T10, and L1) for quantifying skeletal muscle and adipose tissue. Blood routine results were collected to calculate inflammatory parameters. All measurements were obtained at baseline and preoperatively. Kaplan-Meier survival curves were plotted and compared using the log-rank tests. Cox regression analysis was performed to investigate the predictive value of clinical, inflammatory, and body composition parameters for disease-free survival (DFS).
A total of 154 patients were included, with 21 (13.6%) deaths and 27 (17.5%) experienced recurrence or metastasis. Major pathological response (MPR) was observed in 71 (46.1%) patients. Multivariate analysis identified MPR and treatment time as independent clinical predictors of DFS. In body composition analysis, baseline subcutaneous adipose tissue area at L1, subcutaneous adipose tissue density at T10, pectoral muscle density (PMD) at T4, and delta-PMD at T4 demonstrated predictive value for DFS. Baseline inflammatory markers, including neutrophil-to-lymphocyte ratio and systemic immune inflammation index, were also associated with DFS. A comprehensive model integrating clinical, body composition, and inflammatory parameters demonstrated superior prognostic performance with the receiver operating characteristic areas under the curve for DFS at 1-, 2-, and 3-year of 0.832, 0.806 and 0.797, respectively.
Baseline body composition and inflammation parameters were valuable in predicting DFS, while preoperative parameters had limited prognostic value. A combined model integrating clinical, body composition, and inflammatory parameters demonstrated enhanced predictive performance for DFS, and may serve as a valuable tool for assessing prognosis in resectable NSCLC patients undergoing neoadjuvant chemoimmunotherapy.
对于接受新辅助化疗免疫治疗的可切除非小细胞肺癌（NSCLC）患者，身体成分和炎症参数的预测价值仍未明确界定。本研究旨在评估基于计算机断层扫描（CT）的身体成分、炎症标志物与新辅助化疗免疫治疗后NSCLC患者生存结局之间的关联。
这项回顾性研究纳入了2019年6月至2023年3月期间接受新辅助化疗免疫治疗的可切除NSCLC患者。在三个层面（T4、T10和L1）收集CT图像以量化骨骼肌和脂肪组织。收集血常规结果以计算炎症参数。所有测量均在基线和术前进行。绘制Kaplan-Meier生存曲线并使用对数秩检验进行比较。进行Cox回归分析以研究临床、炎症和身体成分参数对无病生存期（DFS）的预测价值。
共纳入154例患者，其中21例（13.6%）死亡，27例（17.5%）出现复发或转移。71例（46.1%）患者观察到主要病理缓解（MPR）。多因素分析确定MPR和治疗时间是DFS的独立临床预测因素。在身体成分分析中，L1水平的基线皮下脂肪组织面积、T10水平的皮下脂肪组织密度、T4水平的胸肌密度（PMD）以及T4水平的Δ-PMD对DFS具有预测价值。包括中性粒细胞与淋巴细胞比值和全身免疫炎症指数在内的基线炎症标志物也与DFS相关。一个整合临床、身体成分和炎症参数的综合模型显示出卓越的预后性能，其1年、2年和3年DFS的曲线下面积分别为0.832、0.806和0.797。
基线身体成分和炎症参数在预测DFS方面具有价值，而术前参数的预后价值有限。一个整合临床、身体成分和炎症参数的联合模型显示出对DFS增强的预测性能，并且可能作为评估接受新辅助化疗免疫治疗的可切除NSCLC患者预后的有价值工具。
