Trial reportBMC medical imaging2022
Development and validation of a machine learning-derived radiomics model for diagnosis of osteoporosis and osteopenia using quantitative computed tomography.
Trial report in BMC medical imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it, 47 citations in OpenAlex.
- The Diagnostic Value of Image-Based Machine Learning for Osteoporosis: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Comparison of dual-energy X-ray absorptiometry and lumbar radiographs using deep learning for osteoporosis diagnosis.Journal of bone and mineral metabolism · 2026Article
- Radiomics-based quantitative assessment of the mandibular cortical index on panoramic radiographs.Scientific reports · 2026Article
- Abdominal fat volume predicts bone mass reduction in women with metabolic syndrome: an energy spectral CT analysis.BMC medical imaging · 2026Article
- Artificial Intelligence in Rheumatology: From Algorithms to Clinical Impact in Osteoporosis and Chronic Inflammatory Rheumatic Diseases.Journal of clinical medicine · 2026Article
- Screening opportunistic osteoporosis through multimodal techniques of hip joint CT images: exploring 2D and 3D deep learning, radiomics, clinical data, and their integration.Frontiers in endocrinology · 2026Article
- Radiomics in spinal research: a narrative review.Frontiers in bioengineering and biotechnology · 2026Review
- Computational and Imaging Approaches for Precision Characterization of Bone, Cartilage, and Synovial Biomolecules.Journal of personalized medicine · 2025Review
- Automated opportunistic screening for osteoporosis using deep learning-based automatic segmentation and radiomics on proximal femur images from low-dose abdominal CT.BMC musculoskeletal disorders · 2025Article
- Balancing Performance and Interpretability in Medical Image Analysis: Case study of Osteopenia.Journal of imaging informatics in medicine · 2025Article
- Article
- High prevalence of low bone mineral density in middle-aged adults in Shanghai: a cross-sectional study.BMC musculoskeletal disorders · 2024Article
- Radiomics models based on thoracic and upper lumbar spine in chest LDCT to predict low bone mineral density.Scientific reports · 2024Article
- Targeting the mTOR-Autophagy Axis: Unveiling Therapeutic Potentials in Osteoporosis.Biomolecules · 2024Review
- Feasibility study of opportunistic osteoporosis screening on chest CT using a multi-feature fusion DCNN model.Archives of osteoporosis · 2024Article
- Proteomic Insights into Osteoporosis: Unraveling Diagnostic Markers of and Therapeutic Targets for the Metabolic Bone Disease.Biomolecules · 2024Review
- Hybrid transformer convolutional neural network-based radiomics models for osteoporosis screening in routine CT.BMC medical imaging · 2024Article
- Machine learning model based on radiomics features for AO/OTA classification of pelvic fractures on pelvic radiographs.PloS one · 2024Article
- A Machine Learning-Based Preclinical Osteoporosis Screening Tool (POST): Model Development and Validation Study.JMIR aging · 2023Article
- A diagnostic approach integrated multimodal radiomics with machine learning models based on lumbar spine CT and X-ray for osteoporosis.Journal of bone and mineral metabolism · 2023Article
Corrections and comments
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Authors and funding
11 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTo develop and validate a quantitative computed tomography (QCT) based radiomics model for discriminating osteoporosis and osteopenia.
methodsA total of 635 patients underwent QCT were retrospectively included from November 2016 to November 2019. The patients with osteopenia or osteoporosis (N = 590) were divided into a training cohort (N = 414) and a test cohort (N = 176). Radiomics features were extracted from the QCT images of the third lumbar vertebra. Minimum redundancy and maximum relevance and least absolute shrinkage and selection operator were used for data dimensional reduction, features selection and radiomics model building. Multivariable logistic regression was applied to construct the combined clinical-radiomic model that incorporated radiomics signatures and clinical characteristics. The performance of the combined clinical-radiomic model was evaluated by the area under the curve of receiver operator characteristic curve (ROC-AUC), accuracy, specificity, sensitivity, positive predictive value, and negative predictive value.
resultsThe patients with osteopenia or osteoporosis were randomly divided into training and test cohort with a ratio of 7:3. Six more predictive radiomics signatures, age, alkaline phosphatase and homocysteine were selected to construct the combined clinical-radiomic model for diagnosis of osteoporosis and osteopenia. The AUC of the combined clinical-radiomic model was 0.96 (95% confidence interval (CI), 0.95 to 0.98) in the training cohort and 0.96 (95% CI 0.92 to 1.00) in the test cohort, which were superior to the clinical model alone (training-AUC = 0.81, test-AUC = 0.79). The calibration curve demonstrated that the radiomics nomogram had good agreement between prediction and observation and decision curve analysis confirmed clinically useful.
conclusionsThe combined clinical-radiomic model that incorporates the radiomics score and clinical risk factors, can serve as a reliable and powerful tool for discriminating osteoporosis and osteopenia.
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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 OpenQuestion graph.