ArticleBMC medical imaging2025
Radiomics and machine learning for osteoporosis detection using abdominal computed tomography: a retrospective multicenter study.
Article in BMC medical imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence for opportunistic screening of osteoporosis across multiple imaging modalities: a systematic review.Frontiers in medicine · 2026Pooled it
- Externally validated yet undertrained: sample size deficits in machine learning-based radiomics.European radiology · 2026Article
- An Anatomically Guided and Optimization-Refined Radiomics Framework for Opportunistic Osteoporosis Assessment from Lumbar Spine MRI.Diagnostics (Basel, Switzerland) · 2026Article
- Local fragility index and Radiomics-lite modeling of CT-Derived Hounsfield units for osteoporosis risk assessment in spine surgery patients.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026Article
- Osteoporosis prediction using lumbar CT Hounsfield units: comparative performance and clinical implications of seven machine learning models.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026Article
- Artificial Intelligence in Rheumatology: From Algorithms to Clinical Impact in Osteoporosis and Chronic Inflammatory Rheumatic Diseases.Journal of clinical medicine · 2026Article
- Sequence-specific radiomics for diagnosis of spinal bone loss.Frontiers in endocrinology · 2026Article
- Artificial intelligence in thyroid ultrasound: clinical applications and perspectives.Frontiers in endocrinology · 2026Review
- Artificial Intelligence for Osteoporosis Diagnosis, Risk Prediction and Therapy: Current Advances, Clinical Challenges, and Future Perspectives.Clinical interventions in aging · 2026Review
- Artificial intelligence in osteoporosis assessment using CT imaging: a scoping review.Frontiers in medicine · 2026Review
- Transferable CNN-based data mining approaches for medical imaging: application to spine DXA scans for osteoporosis detection.Frontiers in computational neuroscience · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThis study aimed to develop and validate a predictive model to detect osteoporosis using radiomic features and machine learning (ML) approaches from lumbar spine computed tomography (CT) images during an abdominal CT examination.
methodsA total of 509 patients who underwent both quantitative CT (QCT) and abdominal CT examinations (training group, n = 279; internal validation group, n = 120; external validation group, n = 110) were analyzed in this retrospective study from two centers. Radiomic features were extracted from the lumbar spine CT images. Seven radiomic-based ML models, including logistic regression (LR), Bernoulli, Gaussian NB, SGD, decision tree, support vector machine (SVM), and K-nearest neighbor (KNN) models, were constructed. The performance of the models was assessed using the area under the curve (AUC) of receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA).
resultsThe radiomic model based on LR in the internal validation group and external validation group had excellent performance, with an AUC of 0.960 and 0.786 for differentiating osteoporosis from normal BMD and osteopenia, respectively. The radiomic model based on LR in the internal validation group and Gaussian NB model in the external validation group yielded the highest performance, with an AUC of 0.905 and 0.839 for discriminating normal BMD from osteopenia and osteoporosis, respectively. DCA in the internal validation group revealed that the LR model had greater net benefit than the other models in differentiating osteoporosis from normal BMD and osteopenia.
conclusionRadiomic-based ML approaches may be used to predict osteoporosis from abdominal CT images and as a tool for opportunistic osteoporosis screening.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.