Abstract 4170: Artificial intelligence (AI)-based multi-modal approach using H&E and CT image for predicting treatment response of immune checkpoint inhibitor (ICI) in non-small cell lung cancer (NSCLC)

Abstract Background: Previously, AI-powered spatial tumor-infiltrating lymphocyte analysis in H 0. 0001). It correlates with PD-L1 expression, as tumor proportion score (TPS) ≥ 50% subgroup had higher ensemble score (median 0. 312 vs 0. 257, P = 0. 0295). Interestingly, among TPS ≥ 50% subgroup, the high ensemble score shows significantly favorable PFS of ICI (median PFS 6. 7 months vs 2. 9 months, HR 0. 46, P = 0. 0002). In subgroup analysis according to tissue harvest site (primary tumor vs lymph node or distant metastasis), the AUROC values of AI-HE-TIL and AI-CT for the subgroup harvested from the primary tumor (N=111) were 0. 6568 and 0. 6658, respectively, and those for the subgroup harvested from the metastatic lesion (N=97) were 0. 6162 and 0. 6973, respectively, implying AI-CT is relatively not affected by tissue harvest site. Conclusion: AI-powered multi-modal approach of using H Part 1 (Regular Abstracts) ; 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84 (6Suppl): Abstract nr 4170.