Artificial intelligence for bone metastases: a systematic review and meta-analysis of diagnostic and prognostic performance

Le résumé fourni par la source

Bone metastases are a major complication of advanced solid tumors and are associated with substantial morbidity and reduced survival. Imaging plays a central role in detection and monitoring, yet conventional modalities and qualitative risk scores remain limited by suboptimal specificity and inter-reader variability. Artificial intelligence (AI) has emerged as a potential tool to improve diagnostic accuracy and prognostic assessment. We performed a systematic review and meta-analysis of studies published between January 2008 and January 2026 evaluating AI-based models for the diagnosis and/or prognosis of bone metastases. PubMed/MEDLINE, Scopus, and Web of Science were searched. Studies reporting quantitative performance metrics were included. Logit-transformed area under the curve (AUC) values were pooled using a random-effects model with restricted maximum likelihood estimation. Twenty-two studies met the eligibility criteria, encompassing highly heterogeneous datasets ranging from small single-center cohorts to large population-based registries and multicenter imaging databases. Most were retrospective and single-center. The overall AUC was 0.911 (95% CI 0.868–0.940), with substantial heterogeneity (I² = 98.8%) Radiomics-based models showed consistently high performance with minimal heterogeneity, after group stratification, whereas clinical-only models demonstrated lower discriminative ability. Prognostic studies were heterogeneous and were synthesized narratively. AI-based models demonstrate high diagnostic performance for bone metastases across imaging modalities. However, methodological variability and limited external validation currently restrict clinical translation, underscoring the need for prospective multicenter studies. Most included studies were retrospective, single-center investigations with limited external validation. Therefore, despite the encouraging diagnostic performance, prospective multicenter studies and standardized reporting remain necessary before routine clinical implementation. Unlike previous systematic reviews, this study provides a quantitative synthesis of diagnostic performance together with a structured appraisal of methodological quality and clinical translational readiness. This systematic review and meta-analysis was conducted in accordance with the PRISMA 2020 statement and prospectively registered (CRD420261350260). Systematic review registration: https://www.crd.york.ac.uk/PROSPERO/view/CRD420261350260 , identifier CRD420261350260.

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