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ICIs一线治疗晚期NSCLC的预后预测模型构建与验证
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| 篇名： | ICIs一线治疗晚期NSCLC的预后预测模型构建与验证 |
| TITLE： | Construction and validation of a prognostic prediction model for advanced non-small cell lung cancer with first-line immune checkpoint inhibitors |
| 摘要： | 目的 构建免疫检查点抑制剂（ICIs）一线治疗晚期非小细胞肺癌（NSCLC）的预后预测模型并验证。方法回顾性收集我院2020年1月至2023年12月ICIs一线治疗的211例晚期NSCLC患者的临床病理及外周血参数资料。将患者按7∶3随机划分为训练集（151例）和验证集（60例）。以无进展生存期（PFS）为结局指标，在训练集中，采用LASSO回归初步筛选预测变量，通过将初筛预测变量纳入多因素Cox回归分析以构建模型1；同时，对LASSO回归初筛的连续变量进行限制性立方样条（RCS）检验，将存在非线性关系（P＜0.1）的变量以RCS形式进行多因素Cox回归分析以构建模型2。采用一致性指数、受试者工作特征曲线、校准曲线和决策曲线分析比较两个模型的预测性能，选择最优模型绘制列线图；采用剔除程序性死亡配体1（PD-L1）未知数据后重新构建多因素Cox回归模型进行敏感性分析；采用Kaplan-Meier法绘制生存曲线，分析不同预后分层患者的生存差异。结果用于构建模型1的变量为性别、吸烟史、PD-L1表达情况、骨转移及癌胚抗原（CEA）；用于构建模型2的变量为性别、吸烟史、PD-L1表达情况、骨转移、CEA、中性粒细胞（Neu）及白蛋白（ALB）。在训练集和验证集中，模型2的一致性指数分别为0.710、0.672，均优于模型1（0.649、0.634）；模型2在训练集中12、18、24个月PFS的曲线下面积（AUC）分别为0.737、0.782、0.788，在验证集中分别为0.709、0.630、0.663，均优于模型1（训练集AUC为0.653、0.704、0.697；验证集AUC为0.688、0.605、0.651）。两个模型校准曲线显示，预测概率与实际概率均具有良好的一致性，模型2的临床净收益率高于模型1。敏感性分析提示预测模型具有稳健性。低风险组患者的中位PFS显著长于高风险组（P＜0.05）。结论患者性别、吸烟史、PD-L1表达情况、骨转移、Neu、ALB和CEA是影响ICIs一线治疗晚期NSCLC患者PFS的独立因素；基于以上因素构建的预测模型具有较好的预测效能，可用于预测晚期NSCLC患者的预后。 |
| ABSTRACT： | OBJECTIVE To construct and validate a prognostic prediction model for first-line immune checkpoint inhibitors （ICIs） therapy for advanced non-small cell lung cancer （NSCLC）.METHODS The clinicopathological data and peripheral blood parameters from 211 patients with advanced NSCLC who received first-line ICIs therapy at our hospital from January 2020 to December 2023 were retrospective collected. The patients were randomly divided into a training set （151 cases） and a validation set（60 cases） at a ratio of 7∶3. Using progression-free survival （PFS） as the outcome measure， LASSO regression was used to preliminarily screen predictive variables in the training set，and the preliminarily screened predictive variables were entered into a multivariable Cox regression model to construct Model 1. Meanwhile，restricted cubic splinec analysis was performed on the continuous variables preliminarily screened by LASSO regression. The variables with nonlinear relationships （ P ＜0.1） were analyzed by multivariable Cox regression in RCS form to construct Model 2. The concordance index，receiver operating characteristic curves，calibration curves，and decision curve analysis were used to compare the predictive performance of the two models； the optimal model was selected to construct a nomogram.Sensitivity analysis was performed by reconstructing the multivariable Cox regression model after excluding cases with unknown programmed death-ligand 1（PD-L1）status. Kaplan-Meier method was used to plot survival curves to analyze the survival differences among patients with different risk strata.RESULTS The variables used to construct Model 1 were sex， smoking history， PD-L1 expression status， bone metastasis， and carcinoembryonic antigen （CEA）；the variables used to construct Model 2 were sex， smoking history， PD-L1 expression status， bone metastasis， CEA， neutrophil（Neu）， and albumin（ALB）. The concordance indices of Model 2 were 0.710 in the training set and 0.672 in the validation set，both superior to those of Model 1 （0.649，0.634）. The areas under the curve （AUC） of Model 2 for 12， 18， and 24 months PFS were 0.737，0.782 and 0.788 in the training set，and 0.709，0.630 and 0.663 in the validation set，respectively，all outperforming Model 1 （0.653， 0.704， 0.697 in the training set；0.688， 0.605， 0.651 in the validation set）.The calibration curves of both models showed good consistency between the predicted probability and actual probability，and the clinical net benefit of Model 2 was higher than that of Model 1. Sensitivity analysis indicated that the prediction model was robust. The median PFS of patients in the low-risk group was significantly longer than that in the high-risk group（ P ＜0.05）.CONCLUSIONS Sex， smoking history， PD-L1 expression status， bone metastasis， Neu， ALB， and CEA were independent influencing factors for PFS in advanced NSCLC patients receiving first-line ICIs. The prediction model constructed based on the above factors has good predictive performance and can be used to predict the prognosis of advanced NSCLC patients. |
| 期刊： | 2026年第37卷第18期 |
| 作者： | 崔红霞；黎苏；梁宇；刘信 |
| AUTHORS： | CUI Hongxia，LI Su，LIANG Yu，LIU Xin |
| 关键字： | 非小细胞肺癌;免疫检查点抑制剂;预后;预测模型;LASSO回归分析 |
| KEYWORDS： | non-small cell lung cancer;immune checkpoint inhibitors;prognosis;Prediction model;LASSO regression |
| 阅读数： | 1 次 |
| 本月下载数： | 0 次 |
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