A narrative review of the prediction of immunotherapy efficacy for treating NSCLC: An artificial intelligence perspective

作者：Shaowei Wu, Anzi Zhuang, Gengda Huang, Ziyi Zhao, Weijie Zhan, Lei Yu, C Li, Lintong Yao, Yubo Zhou, Yangzhong Guo, Haiyu Zhou · 发表于：Intelligent Oncology · 年份：2025 · DOI：10.1016/j.intonc.2025.05.001 · 被引用次数：9 · 研究领域：Cancer Immunotherapy and Biomarkers、Colorectal and Anal Carcinomas、Radiomics and Machine Learning in Medical Imaging

Immunotherapy efficacy in non-small-cell lung cancer (NSCLC) remains variable, with traditional biomarkers (PD-L1 and TMB) limited by heterogeneity and sensitivity/specificity constraints. This review evaluates the role of artificial intelligence (AI) in advancing predictive biomarkers through the integration of radiomics, pathomics, and multi-omics. We conducted a narrative literature review (PubMed/Web of Science, 2012–2025) using keywords spanning NSCLC, immunotherapy, and AI methodologies (radiomics, pathomics, and multi-omics). The inclusion criterion was studies that developed predictive models for immune checkpoint inhibitor (ICI) outcomes; the exclusion criteria were non-original research and preclinical studies. Emerging evidence highlights AI’s transformative role in the prediction of immunotherapy efficacy for treating NSCLC: radiomics quantifies tumor heterogeneity through CT-based PD-L1 and TMB ≥ 10 mut/Mb biomarkers, while pathomics refines PD-L1 scoring and immune microenvironment classification. Multi-omics integration synergizes imaging, histopathology, and liquid biopsy data with dynamic models by resolving spatial discordance and temporal clonal evolution. However, clinical adoption requires the standardization of peri-tumoral radiomics, validation of delta-feature protocols, and prospective trials to address overfitting risks in small cohorts. AI-driven multi-omics redefines immunotherapy prognostication, but faces interpretability and ethical challenges. ...