ArticleNature communications2025
A clinically applicable AI system for detection and diagnosis of bone metastases using CT scans.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Bone organoids and mitochondrial reprogramming.Journal of orthopaedic translation · 2026Review
- Artificial Intelligence for Detection and Characterisation of Bone Metastases on MRI: A Scoping Review.Cancers · 2026Review
- Bootstrapping multimodal large language model with medical knowledge for automatic esophagogastroduodenoscopy diagnosis and reporting.Nature communications · 2026Article
- Opportunistic Osteoporosis Screening from Routine Knee Radiographs Using a Multi-Stage CNN Framework with External Validation.Journal of clinical medicine · 2026Article
- A multimodal feature disentanglement model for lymphadenopathy diagnosis based on BUS and CDFI ultrasound videos: a retrospective, prospective, multicenter study.European radiology · 2026Article
- Assessing the Accuracy and Readability of Generative Artificial Intelligence Responses for Esophageal and Gastric Cancer Patients.Journal of clinical medicine · 2026Article
- Review
- Artificial Intelligence and Machine Learning in Bone Metastasis Management: A Narrative Review.Current oncology (Toronto, Ont.) · 2026Review
- Structure-aware multi-task learning with domain generalization for robust vertebrae analysis in spinal CT.NPJ digital medicine · 2026Article
- Enabling earlier detection of spinal lesions in CT imaging with artificial intelligence-a case study.Frontiers in artificial intelligence · 2026Article
- Anchoring Pelvic AI: A Hip Bone Reference Frame for Sacral Tumor Classification in Noncontrast CT.Radiology. Imaging cancer · 2026Article
- Assessing the generalizability of artificial intelligence in radiology: a systematic review of performance across different clinical settings.Annals of medicine and surgery (2012) · 2025Article
- AI for Lesion Detection in Musculoskeletal Radiology.Journal of the Korean Society of Radiology · 2025Review
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Authors and funding
24 authors.
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Abstract
Manual interpretation of CT images for bone metastasis (BM) detection in primary cancer remains challenging. We present an automated Bone Lesion Detection System (BLDS) developed using CT scans from 2518 patients (9177 BMs; 12,824 non-BM lesions) across five hospitals. The system, developed on 1271 patients and tested on 1247 multicenter cases, demonstrates 89.1% lesion-wise sensitivity (1.40 false-positives/case [FPPC]) in detecting bone lesions on non-contrast CT scans, with 92.3% and 91.1% accuracy in classifying BM/non-BM lesions for internal and external test sets, respectively. Outperforming radiologists in lesion detection (40.5% sensitivity; 0.65 FPPC), BLDS shows lower BM detection sensitivity than junior radiologists, though comparable to trainees. BLDS improves radiologists' lesion-wise sensitivity by 22.2% in BM detection and reduces reading time by 26.4%, while maintaining 90.2% patient-wise sensitivity and 98.2% negative predictive value in real-world validation (n = 54,610). The system demonstrates significant potential to enhance CT-based BM interpretation, particularly benefiting trainees.
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