What type of study is this?

This is a Systematic Review study (also classified as: Meta-Analysis).

August 26, 2025Open Access

Systematic review and meta-analysis of artificial intelligence for image-based lung cancer classification and prognostic evaluation

Puntos clave

AI demonstrates high diagnostic sensitivity and specificity, showing values of 0.86 for both in lung cancer diagnosis.
Pooled analysis of studies indicates an AUC of 0.92 for lung cancer diagnosis, confirming strong predictive ability.
In prognosis, the pooled hazard ratio suggests AI can significantly differentiate between risk levels in lung cancer patients.
Challenges remain for clinical implementation, highlighting the need for prospective multicenter validation of AI applications.

Resumen

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.

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