Development and validation of a machine learning-derived radiomics model for diagnosis of osteoporosis and osteopenia using quantitative computed tomography

[21] Succio G(2016)Approaches to regularized regression - a comparison between gradient boosting and the lasso Methods Inf Med 55 392-663

[22] Zhang B(2013)Volumetric CT-based segmentation of NSCLC using 3D-Slicer Sci Rep 3 174-1587

[23] Tian J(2020)Radiomics for classification of bone mineral loss: a machine learning study Diagn Interv Imaging 9 2518-372

[24] Dong D(2020)Evaluation of transfer learning with deep convolutional neural networks for screening osteoporosis in dental panoramic radiographs J Clin Med 29 654-266

[25] Gu D(2011)Analysis of bone mineral density distribution at trabecular bones in thoracic and lumbar vertebrae using X-ray CT images J Bone Miner Metab 33 1582-373

[26] Dong Y(2008)Effects of high bone turnover on the biomechanical properties of the L3 vertebra in an ovine model of early stage osteoporosis Spine (Phila Pa 1976) 27 366-1274

[27] Zhang L(2012)QCT measures of bone strength at the thoracic and lumbar spine: the Framingham Study J Bone Miner Res 24 1534-24

[28] Dong D(2017)Thoracic quantitative computed tomography (QCT) can sensitively monitor bone mineral metabolism: comparison of thoracic QCT vs lumbar QCT and dual-energy X-ray absorptiometry in detection of age-relative change in bone mineral density Acad Radiol 33 169-177

[29] Tang L(2008)Relationships between densitometric and morphological parameters as measured by peripheral computed tomography and the compressive behavior of lumbar vertebral bodies from macaques (Macaca fascicularis) Spine (Phila Pa 1976) 10 15-743

[30] Li ZY(2020)Deep learning for osteoporosis classification using hip radiographs and patient clinical covariates Biomolecules 15 258-537

[21] Succio G(2016)Approaches to regularized regression - a comparison between gradient boosting and the lasso Methods Inf Med 55 392-663

[22] Zhang B(2013)Volumetric CT-based segmentation of NSCLC using 3D-Slicer Sci Rep 3 174-1587

[23] Tian J(2020)Radiomics for classification of bone mineral loss: a machine learning study Diagn Interv Imaging 9 2518-372

[24] Dong D(2020)Evaluation of transfer learning with deep convolutional neural networks for screening osteoporosis in dental panoramic radiographs J Clin Med 29 654-266

[25] Gu D(2011)Analysis of bone mineral density distribution at trabecular bones in thoracic and lumbar vertebrae using X-ray CT images J Bone Miner Metab 33 1582-373

[26] Dong Y(2008)Effects of high bone turnover on the biomechanical properties of the L3 vertebra in an ovine model of early stage osteoporosis Spine (Phila Pa 1976) 27 366-1274

[27] Zhang L(2012)QCT measures of bone strength at the thoracic and lumbar spine: the Framingham Study J Bone Miner Res 24 1534-24

[28] Dong D(2017)Thoracic quantitative computed tomography (QCT) can sensitively monitor bone mineral metabolism: comparison of thoracic QCT vs lumbar QCT and dual-energy X-ray absorptiometry in detection of age-relative change in bone mineral density Acad Radiol 33 169-177

[29] Tang L(2008)Relationships between densitometric and morphological parameters as measured by peripheral computed tomography and the compressive behavior of lumbar vertebral bodies from macaques (Macaca fascicularis) Spine (Phila Pa 1976) 10 15-743

[30] Li ZY(2020)Deep learning for osteoporosis classification using hip radiographs and patient clinical covariates Biomolecules 15 258-537