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应用泛免疫炎症值和PILE评分构建诺模图预测晚期NSCLC的免疫治疗预后
Received 30 January 2024
Accepted for publication 18 June 2024
Published 2 July 2024 Volume 2024:16 Pages 741—751
DOI https://doi.org/10.2147/CMAR.S461964
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 3
Editor who approved publication: Professor Yong Teng
Shixin Ma,1,2 Fei Li,2 Lunqing Wang2
1Graduate School, Dalian Medical University, Dalian, Liaoning, 116000, People’s Republic of China; 2Department of Thoracic Surgery, Qingdao Municipal Hospital, Qingdao, Shandong, 266071, People’s Republic of China
Correspondence: Lunqing Wang, Department of Thoracic Surgery, Qingdao Municipal Hospital, No. 5 Donghai Middle Road, Qingdao, People’s Republic of China, Tel +86 532 87077952, Email wanglunqing1973@163.com
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