Combined laboratory and imaging indicators to construct risk models for predicting immunotherapy efficacy and prognosis in non-small cell lung cancer: An observational study (STROBE compliant)

作者：Xinyu Bai, Xin Wang, Hailan Xu, Yiying Bai, Qianhui Chen, Shengli Bi, Senyang Chen, Hongbin Yang, Xiaotong Zhang, Fan Li, Lei Liu, Li Zhang · 发表于：Medicine · 年份：2025 · DOI：10.1097/md.0000000000045224 · 被引用次数：4 · 研究领域：Cancer Immunotherapy and Biomarkers、Radiomics and Machine Learning in Medical Imaging、Lung Cancer Diagnosis and Treatment

This study aimed to investigate the correlations between short- and long-term efficacy of immune checkpoint inhibitors (ICIs) and pretreatment laboratory/imaging parameters in advanced non-small cell lung cancer (NSCLC), and to construct risk prediction models. We enrolled 137 NSCLC patients with stage IIIB-IV disease who completed 4 cycles of PD-1/PD-L1 inhibitor monotherapy or combination therapy. All participants underwent pretreatment laboratory assessments encompassing inflammatory markers, lymphocyte subsets, tumor biomarkers, coagulation profiles, and contrast-enhanced computed tomography (CE-CT) scans. The primary endpoints were objective response rate (ORR) and overall survival (OS), with progression-free survival (PFS) as the secondary endpoint. Univariate and multivariate logistic regression analyses were performed to identify significant predictors of short-term treatment response and develop an efficacy prediction model. For long-term outcomes, univariate and multivariate Cox proportional hazards regression analyses were conducted to establish a prognostic risk model. The final models were presented as nomograms and validated through receiver operating characteristic (ROC) curve analysis, calibration curves, and decision curve analysis (DCA). CD4+ T-cell count (P = .007), fibrinogen (FIB, P = .047), and mediastinal lymph node enlargement (P = .028) emerged as independent predictors of ORR. The prediction model demonstrated an area under the ROC curve (AUC) of 0.8...