Three models that predict the efficacy of immunotherapy in Chinese patients with advanced non‐small cell lung cancer

• Published in: Cancer medicine (Malden, MA) MA), 2021-09, Vol.10 (18), p.6291-6303
• Main Authors: Zhao, Qian, Li, Butuo, Xu, Yiyue, Wang, Shijiang, Zou, Bing, Yu, Jinming, Wang, Linlin
• Format: Article
• Language: English
• Subjects: Aged; Biomarkers; Blood platelets; Cancer therapies; Carcinoma, Non-Small-Cell Lung - diagnosis; Carcinoma, Non-Small-Cell Lung - drug therapy; Carcinoma, Non-Small-Cell Lung - mortality; Chemotherapy; China - epidemiology; Clinical Cancer Research; Clinical medicine; Decision making; Dehydrogenases; FDA approval; Feasibility Studies; Female; Histology; Humans; Immune checkpoint inhibitors; Immune Checkpoint Inhibitors - therapeutic use; Immunotherapy; Kaplan-Meier Estimate; L-Lactate dehydrogenase; Lactic acid; Leukocytes (neutrophilic); Liver; Lung cancer; Lung Neoplasms - diagnosis; Lung Neoplasms - drug therapy; Lung Neoplasms - mortality; Lymphocytes; Male; Medical prognosis; Metastases; Metastasis; Middle Aged; Mutation; Neutrophils; Non-small cell lung carcinoma; non‐small cell lung cancer (NSCLC); Oncology; Patients; predictive; Prognosis; Progression-Free Survival; Radiation; Retrospective Studies; Risk Assessment - methods; ROC Curve; score; Software
• ISSN: 2045-7634; 2045-7634
• DOI: 10.1002/cam4.4171

Background Many tools have been developed to predict the efficacy of immunotherapy, such as lung immune prognostic index (LIPI), EPSILoN [Eastern Cooperative Oncology Group performance status (ECOG PS), smoking, liver metastases, lactate dehydrogenase (LDH), neutrophil‐to‐lymphocyte ratio (NLR)], and modified lung immune predictive index (mLIPI) scores. The aim of this study was to determine the ability of three predictive scores to predict the outcomes in Chinese advanced non‐small cell lung cancer (aNSCLC) patients treated with immune checkpoint inhibitors (ICIs). Methods We retrospectively analyzed 429 patients with aNSCLC treated with ICIs at our institution. The predictive ability of these models was evaluated using area under the curve (AUC) in receiver operating characteristic curve (ROC) analysis. Calibration was assessed using the Hosmer–Lemeshow test (H–L test) and Spearman's correlation coefficient. Progression‐free survival (PFS) and overall survival (OS) curves were generated using the Kaplan–Meier method. Results The AUC values of LIPI, mLIPI, and EPSILoN scores predicting PFS at 6 months were 0.642 [95% confidence interval (CI):0.590–0.694], 0.720 (95% CI: 0.675–0.762), and 0.633 (95% CI: 0.585–0.679), respectively (p