AI-Driven Computational Models for Lung Cancer Diagnosis: A Systematic Review and Meta-Analysis

Lung cancer remains the leading cause of cancer-related mortality worldwide, with early detection being critical for improving survival rates. While artificial intelligence (AI) has shown promise in enhancing lung cancer diagnosis and prognosis, challenges such as model generalizability, dataset diversity, and clinical validation hinder its widespread adoption. In recent years, hybrid approaches integrating deep learning with radiomics have gained attention for their potential to improve accuracy, interpretability, and robustness in lung cancer prediction. This systematic review and meta-analysis examine studies published between 2015 and 2023, focusing on hybrid AI-radiomics models that combine handcrafted radiomic features with deep learning architectures such as CNN, U-Net, and VGG-16. Additionally, machine learning classifiers like XGBoost, Random Forest, and SVM are explored in the context of radiomic feature analysis. Beyond performance evaluation, this study investigates dataset diversity, clinical validation challenges, and regulatory concerns affecting the translation of hybrid models into clinical practice. To provide a quantitative synthesis of current evidence, we conduct a meta-analysis of existing studies, assessing the effectiveness and reliability of hybrid AI-radiomics approaches compared to standalone AI models. Furthermore, an independent benchmarking experiment on the LIDC-IDRI dataset is performed to empirically validate the findings, demonstrating the superior performance of hybrid models in lung cancer diagnosis. Unlike previous reviews, this study fills a critical gap by combining both a systematic review and meta-analysis, offering a comprehensive evaluation of hybrid AI-radiomics models. The meta-analysis provides quantitative validation of these models' effectiveness, ensuring a more rigorous assessment of their real-world applicability. By identifying key limitations and opportunities, this review aims to bridge the gap between research and clinical implementation, offering insights for the development of more explainable, generalizable, and ethically responsible AI-driven solutions for lung cancer diagnosis.