ArticleJournal of endocrinological investigation2022
Artificial intelligence-based radiomics on computed tomography of lumbar spine in subjects with fragility vertebral fractures.
Article in Journal of endocrinological investigation, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 3 of them syntheses that pooled it.
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Who cites it
27 citing papers in PubMed, 3 syntheses or guidelines pooled it, 37 citations in OpenAlex.
- Artificial intelligence for opportunistic screening of osteoporosis across multiple imaging modalities: a systematic review.Frontiers in medicine · 2026Pooled it
- Artificial intelligence in risk prediction and diagnosis of vertebral fractures.Scientific reports · 2024Pooled it
- CT image-based biomarkers for opportunistic screening of osteoporotic fractures: a systematic review and meta-analysis.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2024Pooled it
- AI-enhanced routine CT for opportunistic osteoporosis screening and risk assessment.Skeletal radiology · 2026Review
- Artificial intelligence on opportunistic computed tomography for predicting vertebral fracture risk in women undergoing estrogen deprivation therapy for breast cancer.La Radiologia medica · 2026Article
- Radiomics and Back Pain.Global spine journal · 2026Review
- Four MRI-Based Radiomics Models for Diagnosis of Lumbar Intervertebral Disc Degeneration.Journal of imaging informatics in medicine · 2026Article
- Deep learning-based lightweight model for automated lumbar foraminal stenosis classification: sagittal CT diagnostic performance compared to clinical subspecialists.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
- Article
- Artificial Intelligence for Osteoporosis Diagnosis, Risk Prediction and Therapy: Current Advances, Clinical Challenges, and Future Perspectives.Clinical interventions in aging · 2026Review
- Radiomics-based machine learning model integrating preoperative vertebral computed tomography and clinical features to predict cage subsidence after single-level anterior cervical discectomy and fusion with a zero-profile anchored spacer.Journal of orthopaedic surgery and research · 2025Article
- Fragility fractures in well-differentiated gastroenteropancreatic neuroendocrine tumors: Results from a multicentered retrospective study.Journal of neuroendocrinology · 2025Article
- Advanced radiomic prediction of osteoporosis in primary hyperparathyroidism: a machine learning-based analysis of CT images.La Radiologia medica · 2025Article
- Advanced feature fusion of radiomics and deep learning for accurate detection of wrist fractures on X-ray images.BMC musculoskeletal disorders · 2025Article
- Conventional chest computed tomography-based radiomics for predicting the risk of thoracolumbar osteoporotic vertebral fractures.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2025Article
- Development of a deep learning radiomics model combining lumbar CT, multi-sequence MRI, and clinical data to predict high-risk cage subsidence after lumbar fusion: a retrospective multicenter study.Biomedical engineering online · 2025Article
- CT-based radiomics predicts adjacent vertebral fracture after percutaneous vertebral augmentation.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 · 2025Article
- MRI-based 2.5D deep learning radiomics nomogram for the differentiation of benign versus malignant vertebral compression fractures.Frontiers in oncology · 2025Article
- Radiomics based on lumbar CT to identify high-risk patients for OVCF in postmenopausal women.Frontiers in aging · 2025Article
Corrections and comments
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Authors and funding
21 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThere is emerging evidence that radiomics analyses can improve detection of skeletal fragility. In this cross-sectional study, we evaluated radiomics features (RFs) on computed tomography (CT) images of the lumbar spine in subjects with or without fragility vertebral fractures (VFs).
methodsTwo-hundred-forty consecutive individuals (mean age 60.4 ± 15.4, 130 males) were evaluated by radiomics analyses on opportunistic lumbar spine CT. VFs were diagnosed in 58 subjects by morphometric approach on CT or XR-ray spine (D4-L4) images. DXA measurement of bone mineral density (BMD) was performed on 17 subjects with VFs.
resultsTwenty RFs were used to develop the machine learning model reaching 0.839 and 0.789 of AUROC in the train and test datasets, respectively. After correction for age, VFs were significantly associated with RFs obtained from non-fractured vertebrae indicating altered trabecular microarchitecture, such as low-gray level zone emphasis (LGLZE) [odds ratio (OR) 1.675, 95% confidence interval (CI) 1.215-2.310], gray level non-uniformity (GLN) (OR 1.403, 95% CI 1.023-1.924) and neighboring gray-tone difference matrix (NGTDM) contrast (OR 0.692, 95% CI 0.493-0.971). Noteworthy, no significant differences in LGLZE (p = 0.94), GLN (p = 0.40) and NGDTM contrast (p = 0.54) were found between fractured subjects with BMD T score < - 2.5 SD and those in whom VFs developed in absence of densitometric diagnosis of osteoporosis.
conclusionsArtificial intelligence-based analyses on spine CT images identified RFs associated with fragility VFs. Future studies are needed to test the predictive value of RFs on opportunistic CT scans in identifying subjects with primary and secondary osteoporosis at high risk of fracture.
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