Abstract 4967: Predicting progression-free survival (PFS) in first-line (1L) immune checkpoint inhibitor (ICI)-treated patients (pts) with advanced non-small cell lung cancer (aNSCLC): Machine learning (ML) application in real-world data

Abstract Introduction: Anti-programmed cell death (ligand) -1 ICIs are the standard of care 1L treatment for pts with aNSCLC without actionable oncogenic driver mutations. Heterogeneity in ICI utilization and outcomes is observed in real-world pts. By leveraging ML in electronic health records (EHRs), we identified predictors influencing key clinical outcomes, and developed a nomogram to predict likelihood of median PFS in 1L ICI-treated aNSCLC pts for potential risk stratification for interventions. Methods: 1L ICI-treated aNSCLC pts without ALK/ROS1/KRAS/BRAF/EGFR alterations were identified in a US oncology EHR database (ConcertAI; Jan 2015-Feb 2023). Survival ML models were trained on 112 clinical and demographic features with 5-fold nested cross-validation. The top predictors, determined by the best-performing ML model through SHapley Additive exPlanations and clinical judgement, were used to create a Cox proportional hazard (CPH) nomogram of median PFS. All models were evaluated using the concordance index (c-index). Patients were categorized as having high and low risk of progression/death at median PFS according to median risk predicted by the nomogram. Results: The study cohort had 4668 pts (median PFS: 6. 1 months; 3811 events). The CPH nomogram predicting 6-month PFS had a c-index of 0. 60 with the top 10 predictors identified using the XGBoost model (c-index: 0. 62). Nomogram predictors included increased number of metastases, ECOG PS, cough suppressant use, WBC counts, and NLR (Table). Median probability of 6-month PFS for low- and high-risk groups was 35. 9% and 19. 4%, respectively. Conclusions: Future research should validate these findings and evaluate the opportunity to guide clinical practice to optimize outcomes in aNSCLC pts treated with 1L ICIs. Summary of top 10 clinical predictors at baseline Mean HR (95% CI) Max nomogram points Total metastases, n 1. 15 (1. 11-1. 19) 100 ECOG PS, ordinal 1. 14 (1. 10-1. 19) 55 Basophils, % 0. 81 (0. 73-0. 90) 52 Albumin, g/dL 0. 81 (0. 75-0. 87) 46 Chloride, mmol/L 0. 98 (0. 97-0. 99) 46 Cough suppressants/expectorants, n 1. 08 (1. 03-1. 14) 42 NLR 1. 03 (1. 02-1. 05) 33 WBC counts, 103 cells/mL 1. 02 (1. 01-1. 03) 29 PD-L1 expression, % 1. 00 (1. 00-1. 00) 28 Presence of liver metastases 1. 26 (1. 15-1. 39) 24 ECOG PS, Eastern Cooperative Oncology Group performance status; NLR, neutrophils-to-lymphocytes ratio; PD-L1, programmed cell death-ligand 1; WBC, white blood cell. Citation Format: Deep K. Hathi, James Harnett, Ning Wu, Zhenxing Xu, Weishen Pan, T G. Hager, Ruben G. Quek, Shivani Aggarwal, Young Kim, Petra Rietschel, Frank Seebach, Fei Wang, Ying Li. Predicting progression-free survival (PFS) in first-line (1L) immune checkpoint inhibitor (ICI) -treated patients (pts) with advanced non-small cell lung cancer (aNSCLC): Machine learning (ML) application in real-world data abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts) ; 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84 (6Suppl): Abstract nr 4967.