A novel model for predicting immunotherapy response and prognosis in NSCLC patients

Abstract

Abstract Background How to screen beneficiary populations has always been a clinical challenge in the treatment of non-small-cell lung cancer (NSCLC) with immune checkpoint inhibitors (ICIs). Routine blood tests, due to their advantages of being minimally invasive, convenient, and capable of reflecting tumor dynamic changes, have potential value in predicting the efficacy of ICIs treatment. However, there are few models based on routine blood tests to predict the efficacy and prognosis of immunotherapy. Methods Patients were randomly divided into training cohort and validation cohort at a ratio of 2:1. The random forest algorithm was applied to select important variables based on routine blood tests, and a random forest (RF) model was constructed to predict the efficacy and prognosis of ICIs treatment. For efficacy prediction, we assessed receiver operating characteristic (ROC) curves, decision curve analysis (DCA) curves, clinical impact curve (CIC), integrated discrimination improvement (IDI) and net reclassification improvement (NRI) compared with the Nomogram model. For prognostic evaluation, we utilized the C-index and time-dependent C-index compared with the Nomogram model, Lung Immune Prognostic Index (LIPI) and Systemic Inflammatory Score (SIS). Patients were classified into high-risk and low-risk groups based on RF model, then the Kaplan–Meier (K–M) curve was used to analyze the differences in progression-free survival (PFS) and overall survival (OS) of patients between the two groups. Results The RF model incorporated RDW-SD, MCV, PDW, CD3+CD8+, APTT, P-LCR, Ca, MPV, CD4+/CD8+ ratio, and AST. In the training and validation cohorts, the RF model exhibited an AUC of 1.000 and 0.864, and sensitivity/specificity of (100.0%, 100.0%) and (70.3%, 93.5%), respectively, which had superior performance compared to the Nomogram model (training cohort: AUC = 0.531, validation cohort: AUC = 0.552). The C-index of the RF model was 0.803 in the training cohort and 0.712 in the validation cohort, which was significantly higher than Nomogram model, LIPI and SIS. K-M survival curves revealed that patients in the high-risk group had significantly shorter PFS/OS than those in the low-risk group. Conclusions In this study, we developed a novel model (RF model) to predict the response to immunotherapy and prognosis in NSCLC patients. The RF model demonstrated better predictive performance for immunotherapy responses than the Nomogram model. Moreover, when predicting the prognosis of immunotherapy, it outperformed the Nomogram model, LIPI, and SIS.

Keywords

• Non-small cell lung cancer
• Predictive biomarkers
• Immune checkpoint inhibitors
• Machine learning