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

ABSTRACT

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 < 0.001 for all models). The AUC values of LIPI, mLIPI, and EPSILON scores predicting objective response rate (ORR) were 0.606 (95% CI 0.546-0.665), 0.683 (95% CI 0.637-0.727), and 0.666 (95% CI 0.620-0.711), respectively (p < 0.001 for all models). The C-indexes of LIPI, mLIPI, and EPSILoN scores for PFS were 0.627 (95% CI 0.611-6.643), 0.677 (95% CI 0.652-0.682), and 0.631 (95% CI 0.617-0.645), respectively. CONCLUSIONS: As mLIPI scores had the highest accuracy when used to predict the outcomes in Chinese aNSCLC patients, this tool could be used to guide clinical immunotherapy decision-making.