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Updated: Apr 22, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
Published on: August 16, 2020
Beyond the Field of View: Enhancing Detection of Orthopaedic Metastases in Cancer Staging
Abstract:
The rising prevalence of metastatic bone disease (MBD), driven by improved oncological survival, places increasing demands on accurate staging. Standard computed tomography of the chest, abdomen, and pelvis (CT-CAP) often may fail to identify orthopaedically relevant lesions, which may contribute to pathological fractures and poorer outcomes. A retrospective single-centre review was conducted of 135 patients who underwent surgery for non- spinal MBD between 2005 and 2024. Preoperative staging imaging was re-evaluated to assess lesion visibility, anatomical distribution, and detection rates across modalities. Lesions were analysed with respect to fracture occurrence, Mirels scores, and postoperative survival. Interobserver agreement for Mirels scoring was calculated. Survival was analysed using Kaplan-Meier methods and a multivariate Cox proportional hazards model. Lesions not identified on staging imaging were associated with significantly higher rates of pathological fracture (p = 0.01) and shorter postoperative survival (median 6 vs. 25 months, p = 0.03). CT-CAP detected fewer orthopaedically relevant lesions than alternative imaging modalities in this retrospective real-world cohort (p < 0.01). Seven clinically relevant lesions were visible only on CT scout images but lay outside the diagnostic field of view; four subsequently fractured. Detection varied by anatomical region, with proximal femoral lesions identified most frequently. In this retrospective cohort, reliance on CT-CAP alone was associated with missed clinically significant MBD lesions, particularly outside the standard field of view. Routine review of full-body scout images may improve detection and potentially reduce preventable fractures. Integration of automated analysis techniques could further strengthen diagnostic accuracy.
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