Multimodal imaging and artificial intelligence for bone tumor diagnosis: Comparative performance and emerging technologies.

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Abstract

Multimodal imaging is central to the diagnosis, staging, and management of bone tumors. Radiography and computed tomography remain essential for initial assessment and cortical evaluation, magnetic resonance imaging provides superior soft tissue contrast and local staging accuracy, and positron emission tomography enhances detection of metastatic disease and treatment response. No single technique suffices across clinical scenarios. Advances in artificial intelligence and radiomics offer avenues to augment this framework, with demonstrated potential in lesion classification, segmentation, prognostication, and response prediction. This review advances a specific argument rather than surveying available technologies: that the principal constraint on artificial intelligence in musculoskeletal oncology is the stability of the reference standard rather than algorithmic capability. Ground truth in bone tumors is unstable on four grounds. Core needle biopsy samples a limited volume and may miss the highest-grade component: agreement between biopsy and resection grade in central chondrosarcoma falls from a weighted kappa of 0.63 in long bones to 0.12 in the pelvis. Expert interpretation is itself inconsistent, with interobserver kappa for grading cartilaginous tumors of 0.44 among musculoskeletal pathologists and 0.35 among radiologists. Molecular criteria have redefined entities within the past decade, so older datasets contain classes that no longer exist as defined. And histological classification does not reliably predict clinical behavior. Consequently, measurable accuracy has a ceiling set by the reference standard rather than the algorithm, and apparent failure on external validation may reflect label divergence between institutions rather than model deficiency. Evidence is graded throughout, and clinical readiness distinguished from technical performance. No system for primary bone tumor characterization has regulatory clearance in the United States or Europe, and formal appraisal of this literature confirms low methodological compliance and infrequent external validation. Progress will depend on multicenter validation and protocol harmonization, and, distinctively for this field, on composite reference standards, outcome-based endpoints, and explicit uncertainty modelling, since ground-truth instability can be mitigated but not eliminated.

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