Ouyang Ting, Zhang Fei, Yang Yan, Luo Tianwei, Chen Ao, Han Ning, Qian Jun, Chu Xiaoyuan, Chen Chao, Yang Mi
Department of Oncology, Nanjing Drum Tower Hospital, Nanjing Drum Tower Hospital Clinical College, Nanjing University of Chinese Medicine, Nanjing, China.
Department of Clinical Laboratory, Jinling Hospital, Medical School of Nanjing University, Nanjing, China.
BMC Cancer. 2025 Aug 22;25(1):1353. doi: 10.1186/s12885-025-14559-1.
Lung cancer remains the leading cause of cancer-related mortality worldwide. While immune checkpoint inhibitors (ICIs) have improved survival outcomes for some patients, their efficacy and adverse effects vary significantly. Thus, developing accurate and practical prognostic tools is essential to optimize treatment decision-making.
This retrospective study analyzed 436 lung cancer patients treated with ICIs, who were randomly divided into training (70%) and validation (30%) cohorts. Independent prognostic factors for overall survival (OS) and progression-free survival (PFS) were identified using LASSO regression and multivariate Cox regression. Nomograms were constructed based on clinical and blood biomarkers. Model performance was assessed using the concordance index (C-index), ROC curve, calibration curve, and decision curve analysis (DCA). Kaplan-Meier analysis validated patient stratification.
The key independent predictive factors for OS and PFS included neutrophil-to-lymphocyte ratio (NLR), previous surgery, liver metastasis, clinical stage, treatment lines, and treatment response evaluation. The nomograms achieved C-index values of 0.709 (OS) and 0.730 (PFS) in the training cohort, with validation C-indexes of 0.655 (OS) and 0.694 (PFS). The ROC curves demonstrated good predictive accuracy for 12-, 24-, and 36-month outcomes. High-risk patients exhibited significantly shorter median OS and PFS (P < 0.001).
The nomograms developed in this study, integrating clinical and blood biomarkers, provide a cost-effective, simple, and accurate tool for predicting the prognosis of lung cancer patients receiving ICIs treatment, to facilitate personalized clinical decision-making.
肺癌仍然是全球癌症相关死亡的主要原因。虽然免疫检查点抑制剂（ICI）改善了部分患者的生存结局，但其疗效和不良反应差异很大。因此，开发准确实用的预后工具对于优化治疗决策至关重要。
这项回顾性研究分析了436例接受ICI治疗的肺癌患者，他们被随机分为训练组（70%）和验证组（30%）。使用LASSO回归和多变量Cox回归确定总生存期（OS）和无进展生存期（PFS）的独立预后因素。基于临床和血液生物标志物构建列线图。使用一致性指数（C指数）、ROC曲线、校准曲线和决策曲线分析（DCA）评估模型性能。Kaplan-Meier分析验证患者分层。
OS和PFS的关键独立预测因素包括中性粒细胞与淋巴细胞比值（NLR）、既往手术史、肝转移、临床分期、治疗线数和治疗反应评估。列线图在训练组中OS的C指数值为0.709，PFS的C指数值为0.730，验证组中OS的C指数为0.655，PFS的C指数为0.694。ROC曲线显示对12个月、24个月和36个月的结局具有良好的预测准确性。高危患者的中位OS和PFS显著缩短（P < 0.001）。
本研究开发的列线图整合了临床和血液生物标志物，为预测接受ICI治疗的肺癌患者的预后提供了一种经济有效、简单且准确的工具，以促进个性化临床决策。
