利用泛免疫炎症值结合 PILE 评分构建用于晚期 NSCLC 免疫疗法预测预后的提名图
IF 4.6
Q2 MATERIALS SCIENCE, BIOMATERIALS
引用次数: 0
摘要
目的：本研究旨在探讨泛免疫炎症值（PIV）结合PILE评分对晚期非小细胞肺癌（NSCLC）患者免疫治疗的预测价值，并构建提名图预测模型，为临床工作提供参考：选取2019年1月至2021年12月在青岛市立医院接受ICIs治疗的晚期NSCLC患者作为研究对象。采用卡普兰-梅耶生存分析、Cox比例风险回归分析进行预后评估。结果通过提名图直观显示，并通过受试者工作特征曲线下面积（AUC）和C指数等指标判断模型的性能。根据 PILE 评分将患者分为高危和低危两组，并对不同风险组患者的预后进行评估：多变量Cox回归分析显示，免疫相关不良事件（irAEs）是总生存期（OS）改善的预后因素，ECOG PS评分≥2分、治疗前骨转移和PIV高表达是OS的独立危险因素。提名图模型预测 OS 的 C 指数为 0.750（95% CI：0.677- 0.823），校准曲线和 ROC 曲线显示该模型具有良好的预测性能。与低风险组相比，PILE 高风险组患者的炎症程度更高，身体状况更差，往往预后更差：PIV可作为接受ICIs治疗的晚期NSCLC患者的预后指标，并可构建提名图预测模型来评估患者的生存预测，从而有助于更好地进行临床决策和预后评估。 关键词：非小细胞肺癌；免疫检查点抑制剂；泛免疫炎症值；预后；提名图本文章由计算机程序翻译，如有差异，请以英文原文为准。
The Construction of a Nomogram Using the Pan-Immune-Inflammation Value Combined with a PILE Score for Immunotherapy Prediction Prognosis in Advanced NSCLC
Purpose: The purpose of this study was to investigate the predictive value of Pan-Immune-Inflammation Value (PIV) combined with the PILE score for immunotherapy in patients with advanced non-small cell lung cancer (NSCLC) and to construct a nomogram prediction model to provide reference for clinical work.
Patients and Methods: Patients with advanced NSCLC who received ICIs treatment in Qingdao Municipal Hospital from January 2019 to December 2021 were selected as the study subjects. The chi-square test, Kaplan-Meier survival analysis, and Cox proportional risk regression analysis were used to evaluate the prognosis. The results were visualized by a nomogram, and the performance of the model was judged by indicators such as the area under the subject operating characteristic curve (AUC) and C-index. The patients were divided into high- and low-risk groups by PILE score, and the prognosis of patients in different risk groups was evaluated.
Results: Multivariate Cox regression analysis showed that immune-related adverse events (irAEs) were prognostic factors for overall survival (OS) improvement, and ECOG PS score ≥ 2, bone metastases before treatment, and high PIV expression were independent risk factors for OS. The C index of OS predicted by the nomogram model is 0.750 (95% CI: 0.677– 0.823), and the Calibration and ROC curves show that the model has good prediction performance. Compared with the low-risk group, patients in the high-risk group of PILE were associated with a higher inflammatory state and poorer physical condition, which often resulted in a poorer prognosis.
Conclusion: PIV can be used as a prognostic indicator for patients with advanced NSCLC treated with ICIs, and a nomogram prediction model can be constructed to evaluate the survival prediction of patients, thus contributing to better clinical decision-making and prognosis assessment.
Keywords: non-small cell lung cancer, immune checkpoint inhibitors, pan-immune-inflammation value, prognosis, nomogram
Patients and Methods: Patients with advanced NSCLC who received ICIs treatment in Qingdao Municipal Hospital from January 2019 to December 2021 were selected as the study subjects. The chi-square test, Kaplan-Meier survival analysis, and Cox proportional risk regression analysis were used to evaluate the prognosis. The results were visualized by a nomogram, and the performance of the model was judged by indicators such as the area under the subject operating characteristic curve (AUC) and C-index. The patients were divided into high- and low-risk groups by PILE score, and the prognosis of patients in different risk groups was evaluated.
Results: Multivariate Cox regression analysis showed that immune-related adverse events (irAEs) were prognostic factors for overall survival (OS) improvement, and ECOG PS score ≥ 2, bone metastases before treatment, and high PIV expression were independent risk factors for OS. The C index of OS predicted by the nomogram model is 0.750 (95% CI: 0.677– 0.823), and the Calibration and ROC curves show that the model has good prediction performance. Compared with the low-risk group, patients in the high-risk group of PILE were associated with a higher inflammatory state and poorer physical condition, which often resulted in a poorer prognosis.
Conclusion: PIV can be used as a prognostic indicator for patients with advanced NSCLC treated with ICIs, and a nomogram prediction model can be constructed to evaluate the survival prediction of patients, thus contributing to better clinical decision-making and prognosis assessment.
Keywords: non-small cell lung cancer, immune checkpoint inhibitors, pan-immune-inflammation value, prognosis, nomogram
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