From Mirels to Artificial Intelligence: Evolution of Fracture Risk Prediction in Metastatic Bone Disease and the Proposal of an Integrated Mirels 2.0 Framework

Background/Objectives: Impending pathological fractures remain among the most challenging complications of metastatic bone disease. Accurate fracture-risk assessment is essential for selecting patients for prophylactic stabilization while avoiding unnecessary surgery. The Mirels Classification remains widely used, but limited specificity, observer variability, and site-dependent performance have stimulated interest in cortical-involvement measures, CT-based structural rigidity analysis (CTRA), finite element analysis (FEA), multivariable prediction, radiomics, and artificial intelligence (AI). Methods: This narrative critical review synthesizes clinical, imaging, biomechanical, and computational evidence relevant to appendicular metastatic-fracture prediction. The literature search was updated through 22 October 2026, with direct metastatic-bone evidence prioritized and study design, anatomical site, sample size, prediction horizon, diagnostic performance, validation status, and major methodological limitations appraised qualitatively. Results: Mirels performance varied substantially by site. In upper-limb disease, the conventional threshold of ≥9 showed 14% sensitivity in one cohort, whereas a humerus-specific cohort reported 83.6% sensitivity, 79.5% specificity, and an AUC of 0.82 at a threshold of ≥8. A 2026 systematic review found Mirels sensitivities of 66.7–88% and specificities of 38–47.9% in femoral studies; the principal prospective CTRA comparison reported 100% sensitivity and 60.6% specificity, while formal FEA studies reported sensitivities of 80–100% and specificities of 67–86%, with important methodological heterogeneity. A 2026 Met FRAP cohort of 565 lesions reported 82% accuracy for 6-month fracture prediction versus 61% for Mirels. No externally validated AI/radiomics model directly predicting appendicular metastatic long-bone fracture was identified. Conclusions: Current evidence supports Mirels as an accessible first-layer screen rather than a stand-alone surgical indication. Integrated Mirels 2.0 is proposed as a conceptual, site-aware research framework that combines clinical assessment, quantitative imaging, biomechanics, tumor biology, patient factors, and optional AI integration with explicit uncertainty and prospective validation requirements.