Performance of Machine Learning Models Based on Medical Imaging in Predicting the expression of PD-L1 and CD8+TILs in Thoracic cancer: A Systematic Review and Meta-Analysis.
Authors
Affiliations (3)
Affiliations (3)
- XI 'AN Medical university, Xi'an, Shaanxi Province, China (M.L.); State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers and Department of Radiation Oncology, Xijing Hospital, The Fourth Military Medical University, Xi'an, China (M.L., J.G., Y.L., F.M., Z.S., Y.C., L.Z.).
- State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers and Department of Radiation Oncology, Xijing Hospital, The Fourth Military Medical University, Xi'an, China (M.L., J.G., Y.L., F.M., Z.S., Y.C., L.Z.).
- State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers and Department of Radiation Oncology, Xijing Hospital, The Fourth Military Medical University, Xi'an, China (M.L., J.G., Y.L., F.M., Z.S., Y.C., L.Z.). Electronic address: [email protected].
Abstract
Medical imaging integrated with artificial intelligence (AI) has demonstrated considerable potential in predicting the tumor immune microenvironment (TIME). However, the robustness of methodologies and predictive performance remains debated. This study systematically reviews and performs a meta-analysis to evaluate the advancements in AI-driven medical imaging for predicting the TIME in thoracic tumors, with a specific focus on Programmed Death-Ligand 1 (PD-L1) expression and CD8+ tumor-infiltrating lymphocytes (TILs). We hypothesize that medical imaging-based AI models can effectively predict key components of the tumor immune microenvironment in thoracic cancers, specifically PD-L1 expression and CD8+ TILs. Following PRISMA guidelines, we systematically searched PubMed, Cochrane, Embase, and Web of Science for studies published up to July 01, 2025. Studies evaluating AI-driven medical imaging for predicting thoracic tumor TIME were included. Diagnostic accuracy data were extracted, and a meta-analysis was conducted using a random-effects model to assess the predictive performance of AI models for PD-L1 expression and CD8+ TILs. Heterogeneity analysis and publication bias assessment were also performed. A total of 68 studies were included, of which 25 were eligible for meta-analysis. The pooled area under the curve (AUC) for AI-driven medical imaging prediction of PD-L1 expression was 0.81 (95% CI: 0.78-0.85), with a sensitivity of 0.77 (95% CI: 0.74-0.80) and a specificity of 0.73 (95% CI: 0.71-0.76). For CD8+ TIL prediction, the pooled AUC was 0.86 (95% CI: 0.82-0.89), with a sensitivity of 0.81 (95% CI: 0.72-0.87) and a specificity of 0.81 (95% CI: 0.76-0.84). Subgroup analysis indicated that integrating multimodal imaging and deep learning models improved predictive performance. Nevertheless, considerable heterogeneity was observed among studies (I² > 75%). AI-driven medical imaging exhibits strong predictive capability for thoracic tumor TIME, particularly in PD-L1 expression and CD8+ TIL prediction. However, significant interstudy heterogeneity limits the generalizability of current models. Future studies should prioritize multicenter, large-scale research, standardization of imaging feature extraction, and integration of biological mechanisms to improve the clinical applicability of AI models.