Baum T, Gräbeldinger M, Räth C et al (2014) Trabecular bone structure analysis of the spine using clinical MDCT: can it predict vertebral bone strength? J Bone Miner Metab 32:56–64. https://doi.org/10.1007/s00774-013-0465-6

Article  PubMed  Google Scholar 

Hemmatian H, Bakker AD, Klein-Nulend J, Van Lenthe H, G, (1914) Aging, Osteocytes, and Mechanotransduction. Curr Osteoporos Rep. https://doi.org/10.1007/s11914-017-0402-z

Article  Google Scholar 

Russo CR, Lauretani F, Bandinelli S et al (2003) Aging bone in men and women: beyond changes in bone mineral density. Osteoporos Int 14:531–538. https://doi.org/10.1007/s00198-002-1322-y

Article  CAS  PubMed  Google Scholar 

Yu A, Huang M, Wang L et al (2023) Age and gender differences in vertebral bone marrow adipose tissue and bone mineral density based on MRI and quantitative CT. Eur J Radiol 159:110669. https://doi.org/10.1016/j.ejrad.2022.110669

Article  PubMed  Google Scholar 

Kanis JA, Cooper C, Rizzoli R, Reginster J-Y (2019) European guidance for the diagnosis and management of osteoporosis in postmenopausal women. Osteoporos Int 30:3–44. https://doi.org/10.1007/s00198-018-4704-5

Article  CAS  PubMed  Google Scholar 

Tamimi I, Cortes ARG, Sánchez-Siles J-M et al (2020) Composition and characteristics of trabecular bone in osteoporosis and osteoarthritis. Bone 140:115558. https://doi.org/10.1016/j.bone.2020.115558

Article  CAS  PubMed  Google Scholar 

Lambin P, Leijenaar RTH, Deist TM et al (2017) Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 14:749–762. https://doi.org/10.1038/nrclinonc.2017.141

Article  PubMed  Google Scholar 

Hong JH, Jung J-Y, Jo A et al (2021) Development and validation of a radiomics model for differentiating bone islands and osteoblastic bone metastases at abdominal CT. Radiology 299:626–632. https://doi.org/10.1148/radiol.2021203783

Article  PubMed  Google Scholar 

Staal FCR, van der Reijd DJ, Taghavi M et al (2021) Radiomics for the prediction of treatment outcome and survival in patients with colorectal cancer: a systematic review. Clin Colorectal Cancer 20:52–71. https://doi.org/10.1016/j.clcc.2020.11.001

Article  PubMed  Google Scholar 

Sun Q, Chen Y, Liang C et al (2021) Biologic pathways underlying prognostic radiomics phenotypes from paired MRI and RNA sequencing in glioblastoma. Radiology 301(3):203281. https://doi.org/10.1148/radiol.2021203281

Article  Google Scholar 

Hinzpeter R, Baumann L, Guggenberger R et al (2021) Radiomics for detecting prostate cancer bone metastases invisible in CT: a proof-of-concept study. Eur Radiol. https://doi.org/10.1007/s00330-021-08245-6

Article  PubMed  PubMed Central  Google Scholar 

He L, Liu Z, Liu C et al (2021) Radiomics based on lumbar spine magnetic resonance imaging to detect osteoporosis. Acad Radiol 28:e165–e171. https://doi.org/10.1016/j.acra.2020.03.046

Article  PubMed  Google Scholar 

Biamonte E, Levi R, Carrone F et al (2022) Artificial intelligence-based radiomics on computed tomography of lumbar spine in subjects with fragility vertebral fractures. J Endocrinol Invest 45:2007–2017. https://doi.org/10.1007/s40618-022-01837-z

Article  CAS  PubMed  Google Scholar 

Franke K, Gaser C (2019) Ten years of brainage as a neuroimaging biomarker of brain aging: what insights have we gained? Front Neurol 10:789. https://doi.org/10.3389/fneur.2019.00789

Article  PubMed  PubMed Central  Google Scholar 

Han Y, Wang G (2020) Skeletal bone age prediction based on a deep residual network with spatial transformer. Comput Methods Programs Biomed 197:105754. https://doi.org/10.1016/j.cmpb.2020.105754

Article  PubMed  Google Scholar 

Zwanenburg A, Vallières M, Abdalah MA et al (2020) The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology 295:328–338. https://doi.org/10.1148/radiol.2020191145

Article  PubMed  Google Scholar 

Fedorov A, Beichel R, Kalpathy-Cramer J et al (2012) 3D Slicer as an image computing platform for the quantitative imaging network. Magn Reson Imaging 30:1323–1341. https://doi.org/10.1016/j.mri.2012.05.001

Article  PubMed  PubMed Central  Google Scholar 

van Griethuysen JJM, Fedorov A, Parmar C et al (2017) Computational radiomics system to decode the radiographic phenotype. Cancer Res 77:e104–e107. https://doi.org/10.1158/0008-5472.CAN-17-0339

Article  CAS  PubMed  PubMed Central  Google Scholar 

Fortin J-P, Cullen N, Sheline YI et al (2018) Harmonization of cortical thickness measurements across scanners and sites. Neuroimage 167:104–120. https://doi.org/10.1016/j.neuroimage.2017.11.024

Article  PubMed  Google Scholar 

Ozaki Y, Tanigaki Y, Watanabe S, Onishi M (2020) Multiobjective tree-structured parzen estimator for computationally expensive optimization problems. In: Proceedings of the 2020 Genetic and Evolutionary Computation Conference. ACM, New York, NY, USA, pp 533–541

Hsieh C-I, Zheng K, Lin C et al (2021) Automated bone mineral density prediction and fracture risk assessment using plain radiographs via deep learning. Nat Commun 12:5472. https://doi.org/10.1038/s41467-021-25779-x

Article  CAS  PubMed  PubMed Central  Google Scholar 

Smets J, Shevroja E, Hügle T et al (2021) Machine Learning Solutions for Osteoporosis—A Review. J Bone Miner Res 36:833–851. https://doi.org/10.1002/jbmr.4292

Article  PubMed  Google Scholar 

Fang Y, Li W, Chen X et al (2021) Opportunistic osteoporosis screening in multi-detector CT images using deep convolutional neural networks. Eur Radiol 31:1831–1842. https://doi.org/10.1007/s00330-020-07312-8

Article  PubMed  Google Scholar 

Valentinitsch A, Trebeschi S, Kaesmacher J et al (2019) Opportunistic osteoporosis screening in multi-detector CT images via local classification of textures. Osteoporos Int 30:1275–1285. https://doi.org/10.1007/s00198-019-04910-1

Article  CAS  PubMed  PubMed Central  Google Scholar 

Kawashima Y, Fujita A, Buch K et al (2019) Using texture analysis of head CT images to differentiate osteoporosis from normal bone density. Eur J Radiol 116:212–218. https://doi.org/10.1016/j.ejrad.2019.05.009

Article  PubMed  Google Scholar 

Sun W, Liu S, Guo J et al (2021) A CT-based radiomics nomogram for distinguishing between benign and malignant bone tumours. Cancer Imaging 21:20. https://doi.org/10.1186/s40644-021-00387-6

Article  PubMed  PubMed Central  Google Scholar 

Dionísio FCF, Oliveira LS, Hernandes MA et al (2020) Manual and semiautomatic segmentation of bone sarcomas on MRI have high similarity. Brazilian J Med Biol Res. https://doi.org/10.1590/1414-431x20198962

Article  Google Scholar 

Eweje FR, Bao B, Wu J et al (2021) Deep learning for classification of bone lesions on routine MRI. EBioMedicine 68:103402. https://doi.org/10.1016/j.ebiom.2021.103402

Article  PubMed  PubMed Central  Google Scholar 

Geirhos R, Jacobsen J-H, Michaelis C et al (2020) Shortcut learning in deep neural networks. Nat Mach Intell 2:665–673. https://doi.org/10.1038/s42256-020-00257-z

Article  Google Scholar 

DeGrave AJ, Janizek JD, Lee S-I (2021) AI for radiographic COVID-19 detection selects shortcuts over signal. Nat Mach Intell 3:610–619. https://doi.org/10.1038/s42256-021-00338-7

Article  Google Scholar 

Berenguer R, del Pastor-Juan M, R, Canales-Vázquez J, et al (2018) Radiomics of CT features may be nonreproducible and redundant: influence of CT acquisition parameters. Radiology 288:407–415. https://doi.org/10.1148/radiol.2018172361

Article  PubMed  Google Scholar 

van Hamersvelt RW, Schilham AMR, Engelke K et al (2017) Accuracy of bone mineral density quantification using dual-layer spectral detector CT: a phantom study. Eur Radiol 27:4351–4359. https://doi.org/10.1007/s00330-017-4801-4

Article  PubMed  PubMed Central  Google Scholar 

Euler A, Nowak T, Bucher B et al (2021) Assessment of bone mineral density from a computed tomography topogram of photon-counting detector computed tomography—effect of phantom size and tube voltage. Invest Radiol 56:614–620. https://doi.org/10.1097/RLI.0000000000000781

Article  CAS  PubMed  Google Scholar 

Niu YT, Olszewski ME, Zhang YX et al (2011) Experimental study and optimization of scan parameters that influence radiation dose in temporal bone high-resolution multidetector row CT. Am J Neuroradiol 32:1783–1788. https://doi.org/10.3174/ajnr.A2609

Article  CAS  PubMed  PubMed Central  Google Scholar 

Topol EJ (2019) High-performance medicine: the convergence of human and artificial intelligence. Nat Med 25:44–56. https://doi.org/10.1038/s41591-018-0300-7

Article  CAS  PubMed  Google Scholar 

Bogowicz M, Jochems A, Deist TM et al (2020) Privacy-preserving distributed learning of radiomics to predict overall survival and HPV status in head and neck cancer. Sci Rep 10:4542. https://doi.org/10.1038/s41598-020-61297-4

Article  CAS  PubMed  PubMed Central  Google Scholar 

Murray N, Le M, Ebrahimzadeh O et al (2017) Imaging the spine with dual-energy CT. Curr Radiol Rep 5:9. https://doi.org/10.1007/s40134-017-0236-6

Article  Google Scholar