Imaging and AI in tertiary prevention of lung cancer: Narrative review and clinical perspectives.

objectiveTo provide a structured narrative review of current evidence and future directions for artificial intelligence (AI) applications in the tertiary prevention of lung cancer, with a focus on radiology-driven recurrence surveillance, treatment response assessment, prognostic stratification, and real-world clinical implementation.

methodsA narrative review was conducted using PubMed, Embase, Web of Science, and IEEE Xplore databases, covering studies published between January 2019 and March 2024. Search terms included lung cancer, tertiary prevention, radiomics, deep learning, recurrence, treatment response, and survival prediction. Studies were selected based on relevance to post-diagnosis clinical management, availability of quantitative performance metrics, and clarity of validation strategy. AI methodologies evaluated included radiomics, convolutional neural networks (CNNs), transformer-based temporal models, ensemble learning, and multimodal data integration. Evidence was critically appraised with respect to study design, cohort size, validation approach, and clinical applicability.

resultsAcross tertiary-prevention tasks-particularly recurrence detection, treatment response monitoring, and survival prediction-AI-assisted models demonstrate performance improvements over conventional radiological assessment in selected, well-defined scenarios, especially when multimodal imaging and external validation are employed. Reported AUC values for recurrence and response prediction generally range from 0.75 to 0.92, though results remain heterogeneous. Radiomics-deep learning integration and ensemble models show the most consistent gains, while evidence for immunotherapy response prediction and long-term survival modeling remains limited by small cohorts and lack of prospective validation. Key barriers include data heterogeneity, limited interpretability, regulatory constraints, and integration into clinical workflows.

conclusionsAI has the potential to meaningfully support tertiary prevention of lung cancer by enhancing surveillance accuracy, treatment monitoring, and prognostic assessment. However, clinical adoption requires rigorous external validation, standardized imaging and data pipelines, transparent model behavior, and alignment with evolving regulatory frameworks. With continued multidisciplinary collaboration and quality assurance, AI is likely to become an integral adjunct to radiological practice in personalized lung cancer management.