Lung cancer (LC) remains a leading global cause of cancer mortality, with current diagnostic and prognostic methods lacking precision. This meta-analysis evaluated the role of artificial intelligence (AI) in LC imaging-based diagnosis and prognostic prediction. We systematically reviewed 315 studies from major databases up to January 7, 2025. Among them, 209 studies on LC diagnosis yielded a combined sensitivity of 0.86 (0.84–0.87), specificity of 0.86 (0.84–0.87), and AUC of 0.92 (0.90–0.94). For LC prognosis, 106 studies were analyzed: 58 with diagnostic data showed a pooled sensitivity of 0.83 (0.81–0.86), specificity of 0.83 (0.80–0.86), and AUC of 0.90 (0.87–0.92). Additionally, 53 studies differentiated between low- and high-risk patients, with a pooled hazard ratio of 2.53 (2.22–2.89) for overall survival and 2.80 (2.42–3.23) for progression-free survival. Subgroup analyses revealed an acceptable performance. AI exhibits strong potential for LC management but requires prospective multicenter validation to address clinical implementation challenges.
Yuan et al. (Tue,) studied this question.
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