Wolbarst AB, Capasso P, Wyant AR. Medical imaging: essentials for physicians. New Jersey: John Wiley & Sons; 2013. doi: 10.1002/9781118480267.
DOI: 10.1002/9781118480267

Shaikh F, Franc B, Allen E, et al. Translational radiomics: defining the strategy pipeline and considerations for application — Part 1: from methodology to clinical implementation. Journal of the American College of Radiology. 2018;15(3):538–542. doi: 10.1016/j.jacr.2017.12.008.
DOI: 10.1016/j.jacr.2017.12.008

Acharya UR, Hagiwara Y, Sudarshan VK, et al. Towards precision medicine: from quantitative imaging to radiomics. Journal of Zhejiang University-SCIENCE B. 2018;19(1):6–24. doi: 10.1631/jzus.B1700260 EDN: YEROEH.
DOI: 10.1631/jzus.B1700260 EDN: YEROEH

Giardino A, Gupta S, Olson E, et al. Role of imaging in the era of precision medicine. Academic Radiology. 2017;24(5):639–649. doi: 10.1016/j.acra.2016.11.021 EDN: YXQFYH.
DOI: 10.1016/j.acra.2016.11.021 EDN: YXQFYH

Hatt M, Le Rest CC, Tixier F, et al. Radiomics: Data Are Also Images. Journal of Nuclear Medicine. 2019;60(Suppl. 2):38S–44S. doi: 10.2967/jnumed.118.220582.
DOI: 10.2967/jnumed.118.220582

Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: extracting more information from medical images using advanced feature analysis. European Journal of Cancer. 2012;48(4):441–446. doi: 10.1016/j.ejca.2011.11.036.
DOI: 10.1016/j.ejca.2011.11.036

Keek SA, Leijenaar RTH, Jochems A, Woodruff HC. A review on radiomics and the future of theranostics for patient selection in precision medicine. The British Journal of Radiology. 2018;91(1091):20170926. doi: 10.1259/bjr.20170926.
DOI: 10.1259/bjr.20170926

Scapicchio C, Gabelloni M, Barucci A, et al. A deep look into radiomics. La radiologia medica. 2021;126(10):1296–1311. doi: 10.1007/s11547-021-01389-x EDN: CFTFXK.
DOI: 10.1007/s11547-021-01389-x EDN: CFTFXK

Lambin P, Leijenaar RTH, Deist TM, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nature Reviews Clinical Oncology. 2017;14(12):749–762. doi: 10.1038/nrclinonc.2017.141.
DOI: 10.1038/nrclinonc.2017.141

Zhang X, Zhang Y, Zhang G, et al. Deep learning with radiomics for disease diagnosis and treatment: challenges and potential. Frontiers in Oncology. 2022;12:773840. doi: 10.3389/fonc.2022.773840 EDN: HKEDBO.
DOI: 10.3389/fonc.2022.773840 EDN: HKEDBO

Weng W, Zhu X. INet: convolutional networks for biomedical image segmentation. IEEE Access. 2021;9:16591–16603. doi: 10.1109/access.2021.3053408 EDN: TKNUNY.
DOI: 10.1109/access.2021.3053408 EDN: TKNUNY

Demircioğlu A. Are deep models in radiomics performing better than generic models? A systematic review. European Radiology Experimental. 2023;7(1):11. doi: 10.1186/s41747-023-00325-0.
DOI: 10.1186/s41747-023-00325-0

McCague C, Ramlee S, Reinius M, et al. Introduction to radiomics for a clinical audience. Clinical Radiology. 2023;78(2):83–98. doi: 10.1016/j.crad.2022.08.149 EDN: FTQEEU.
DOI: 10.1016/j.crad.2022.08.149 EDN: FTQEEU

Davis KW, Blankenbaker DG, Bernard S. Diagnostic imaging: musculoskeletal non-traumatic disease. 3rd ed. Amsterdam: Elsevier; 2022.

Liu S, Wang B, Fan S, et al. Global burden of musculoskeletal disorders and attributable factors in 204 countries and territories: a secondary analysis of the Global Burden of Disease 2019 study. BMJ Open. 2022;12(6):e062183. doi: 10.1136/bmjopen-2022-062183 EDN: JPFMCK.
DOI: 10.1136/bmjopen-2022-062183 EDN: JPFMCK

Jiang YW, Xu XJ, Wang R, Chen CM. Radiomics analysis based on lumbar spine CT to detect osteoporosis. European Radiology. 2022;32(11):8019–8026. doi: 10.1007/s00330-022-08805-4 EDN: FFIEJM.
DOI: 10.1007/s00330-022-08805-4 EDN: FFIEJM

Lim HK, Ha HI, Park SY, Han J. Prediction of femoral osteoporosis using machine-learning analysis with radiomics features and abdomen-pelvic CT: a retrospective single center preliminary study. PLOS ONE. 2021;16(3):e0247330. doi: 10.1371/journal.pone.0247330 EDN: BXSLMY.
DOI: 10.1371/journal.pone.0247330 EDN: BXSLMY

Xue Z, Huo J, Sun X, et al. Using radiomic features of lumbar spine CT images to differentiate osteoporosis from normal bone density. BMC Musculoskeletal Disorders. 2022;23(1):1–9. doi: 10.1186/s12891-022-05309-6 EDN: QMPUNB.
DOI: 10.1186/s12891-022-05309-6 EDN: QMPUNB

Xue Z, Wang L, Sun Q, et al. Radiomics analysis using MR imaging of subchondral bone for identification of knee osteoarthritis. Journal of Orthopaedic Surgery and Research. 2022;17(1):1–11. doi: 10.1186/s13018-022-03314-y EDN: MUEEVI.
DOI: 10.1186/s13018-022-03314-y EDN: MUEEVI

Hirvasniemi J, Klein S, Bierma-Zeinstra S, et al. A machine learning approach to distinguish between knees without and with osteoarthritis using MRI-based radiomic features from tibial bone. European Radiology. 2021;31(11):8513–8521. doi: 10.1007/s00330-021-07951-5 EDN: OQYJMS.
DOI: 10.1007/s00330-021-07951-5 EDN: OQYJMS

Yu K, Ying J, Zhao T, et al. Prediction model for knee osteoarthritis using magnetic resonance–based radiomic features from the infrapatellar fat pad: data from the osteoarthritis initiative. Quantitative Imaging in Medicine and Surgery. 2023;13(1):352–369. doi: 10.21037/qims-22-368 EDN: XMHHBS.
DOI: 10.21037/qims-22-368 EDN: XMHHBS

Colelli G, Barzaghi L, Paoletti M, et al. Radiomics and machine learning applied to STIR sequence for prediction of quantitative parameters in facioscapulohumeral disease. Frontiers in Neurology. 2023;14:1105276. doi: 10.3389/fneur.2023.1105276 EDN: TFSEOI.
DOI: 10.3389/fneur.2023.1105276 EDN: TFSEOI

Zhang MZ, Ou-Yang HQ, Jiang L, et al. Optimal machine learning methods for radiomic prediction models: clinical application for preoperative T2*-weighted images of cervical spondylotic myelopathy. JOR SPINE. 2021;4(4):e1178. doi: 10.1002/jsp2.1178 EDN: QCNMEY.
DOI: 10.1002/jsp2.1178 EDN: QCNMEY

Çorbacıoğlu ŞK, Aksel G. Receiver operating characteristic curve analysis in diagnostic accuracy studies: a guide to interpreting the area under the curve value. Turkish Journal of Emergency Mededicine. 2023;23(4):195–198. doi: 10.4103/tjem.tjem_182_23.
DOI: 10.4103/tjem.tjem_182_23

Chun KJ. Bone densitometry. Seminars in Nuclear Medicine. 2011;41(3):220–228. doi: 10.1053/j.semnuclmed.2010.12.002 EDN: XZBHAJ.
DOI: 10.1053/j.semnuclmed.2010.12.002 EDN: XZBHAJ

Yao Q, Liu M, Yuan K, et al. Radiomics nomogram based on dual-energy spectral CT imaging to diagnose low bone mineral density. BMC Musculoskeletal Disorders. 2022;23(1):1–10. doi: 10.1186/s12891-022-05389-4 EDN: ZJCBIU.
DOI: 10.1186/s12891-022-05389-4 EDN: ZJCBIU

Dai H, Wang Y, Fu R, et al. Radiomics and stacking regression model for measuring bone mineral density using abdominal computed tomography. Acta Radiologica. 2021;64(1):228–236. doi: 10.1177/02841851211068149 EDN: KCQOEI.
DOI: 10.1177/02841851211068149 EDN: KCQOEI

Rastegar S, Vaziri M, Qasempour Y, et al. Radiomics for classification of bone mineral loss: a machine learning study. Diagnostic and Interventional Imaging. 2020;101(9):599–610. doi: 10.1016/j.diii.2020.01.008 EDN: MXTTFU.
DOI: 10.1016/j.diii.2020.01.008 EDN: MXTTFU

Tenório APM, Ferreira-Junior JR, Dalto VF, et al. Radiomic quantification for MRI assessment of sacroiliac joints of patients with spondyloarthritis. Journal of Digital Imaging. 2022;35(1):29–38. doi: 10.1007/s10278-021-00559-7 EDN: EDSNBE.
DOI: 10.1007/s10278-021-00559-7 EDN: EDSNBE

Xie Q, Chen Y, Hu Y, et al. Development and validation of a machine learning-derived radiomics model for diagnosis of osteoporosis and osteopenia using quantitative computed tomography. BMC Medical Imaging. 2022;22(1):1–9. doi: 10.1186/s12880-022-00868-5 EDN: TBGMNV.
DOI: 10.1186/s12880-022-00868-5 EDN: TBGMNV

Wang J, Zhou S, Chen S, et al. Prediction of osteoporosis using radiomics analysis derived from single source dual energy CT. BMC Musculoskeletal Disorders. 2023;24(1):100. doi: 10.1186/s12891-022-06096-w EDN: CSYYUO.
DOI: 10.1186/s12891-022-06096-w EDN: CSYYUO

Huang CB, Hu JS, Tan K, et al. Application of machine learning model to predict osteoporosis based on abdominal computed tomography images of the psoas muscle: a retrospective study. BMC Geriatrics. 2022;22(1):796. doi: 10.1186/s12877-022-03502-9.
DOI: 10.1186/s12877-022-03502-9

He L, Liu Z, Liu C, et al. Radiomics based on lumbar spine magnetic resonance imaging to detect osteoporosis. Academic Radiology. 2021;28(6):e165–e171. doi: 10.1016/j.acra.2020.03.046 EDN: UHSGDH.
DOI: 10.1016/j.acra.2020.03.046 EDN: UHSGDH

Zhao Y, Zhao T, Chen S, et al. Fully automated radiomic screening pipeline for osteoporosis and abnormal bone density with a deep learning-based segmentation using a short lumbar mDixon sequence. Quantitative Imaging in Medicine and Surgery. 2022;12(2):1198–1213. doi: 10.21037/qims-21-587 EDN: NZYATO.
DOI: 10.21037/qims-21-587 EDN: NZYATO

Kutsal FY, Ergin Ergani GO. Vertebral compression fractures: Still an unpredictable aspect of osteoporosis. Turkish Journal of Medical Sciences. 2021;51(2):393–399. doi: 10.3906/sag-2005-315 EDN: YTBYNY.
DOI: 10.3906/sag-2005-315 EDN: YTBYNY

Wang M, Chen X, Cui W, et al. A computed tomography-based radiomics nomogram for predicting osteoporotic vertebral fractures: a longitudinal study. The Journal of Clinical Endocrinology and Metabolism. 2022;108(6):e283–e294. doi: 10.1210/clinem/dgac722 EDN: ZDBNTO.
DOI: 10.1210/clinem/dgac722 EDN: ZDBNTO

Ge C, Chen Z, Lin Y, et al. Preoperative prediction of residual back pain after vertebral augmentation for osteoporotic vertebral compression fractures: initial application of a radiomics score based nomogram. Frontiers in Endocrinology. 2022;13:1093508. doi: 10.3389/fendo.2022.1093508 EDN: SHBOCJ.
DOI: 10.3389/fendo.2022.1093508 EDN: SHBOCJ

Yang H, Yan S, Li J, et al. Prediction of acute versus chronic osteoporotic vertebral fracture using radiomics-clinical model on CT. European Journal of Radiology. 2022;149:110197. doi: 10.1016/j.ejrad.2022.110197 EDN: HVGSBS.
DOI: 10.1016/j.ejrad.2022.110197 EDN: HVGSBS

Biamonte E, Levi R, Carrone F, et al. Artificial intelligence-based radiomics on computed tomography of lumbar spine in subjects with fragility vertebral fractures. Journal of Endocrinological Investigation. 2022;45(10):2007–2017. doi: 10.1007/s40618-022-01837-z EDN: EAIWLD.
DOI: 10.1007/s40618-022-01837-z EDN: EAIWLD

Erdes ShF, Badokin VV, Bochkova AG, et al. On the terminology of spondyloarthritis. Rheumatology Science and Practice. 2015;53(6):657–660. doi: 10.14412/1995-4484-2015-657-660 EDN: WYDFJB.
DOI: 10.14412/1995-4484-2015-657-660 EDN: WYDFJB

Ramiro S, Nikiphorou E, Sepriano A, et al. ASAS-EULAR recommendations for the management of axial spondyloarthritis: 2022 update. Annals of the Rheumatic Diseases. 2023;82(1):19–34. doi: 10.1136/ard-2022-223296 EDN: DLBUZY.
DOI: 10.1136/ard-2022-223296 EDN: DLBUZY

Ye L, Miao S, Xiao Q, et al. A predictive clinical-radiomics nomogram for diagnosing of axial spondyloarthritis using MRI and clinical risk factors. Rheumatology. 2021;61(4):1440–1447. doi: 10.1093/rheumatology/keab542 EDN: FELRLG.
DOI: 10.1093/rheumatology/keab542 EDN: FELRLG

Tenório APM, Faleiros MC, Junior JRF, et al. A study of MRI-based radiomics biomarkers for sacroiliitis and spondyloarthritis. International Journal of Computer Assisted Radiology and Surgery. 2020;15(10):1737–1748. doi: 10.1007/s11548-020-02219-7 EDN: RLWIQG.
DOI: 10.1007/s11548-020-02219-7 EDN: RLWIQG

Zheng M, Miao S, Chen D, et al. Can radiomics replace the SPARCC scoring system in evaluating bone marrow edema of sacroiliac joints in patients with axial spondyloarthritis? Clinical Rheumatology. 2023;42(6):1675–1682. doi: 10.1007/s10067-023-06543-6 EDN: FHQBFF.
DOI: 10.1007/s10067-023-06543-6 EDN: FHQBFF

Maksymowych WP, Mallon C, Morrow S, et al. Development and validation of the spondyloarthritis research consortium of canada (SPARCC) enthesitis index. Annals of the Rheumatic Diseases. 2009;68(6):948–953. doi: 10.1136/ARD.2007.084244.
DOI: 10.1136/ARD.2007.084244

Lacerda C, Linhas R, Duarte R. Tuberculous spondylitis: a report of different clinical scenarios and literature update. Case Reports in Medicine. 2017;2017:1–4. doi: 10.1155/2017/4165301.
DOI: 10.1155/2017/4165301

Wu S, Wei Y, Li H, et al. A predictive clinical-radiomics nomogram for differentiating tuberculous spondylitis from pyogenic spondylitis using CT and clinical risk factors. Infection and Drug Resistance. 2022;15:7327–7338. doi: 10.2147/IDR.S388868 EDN: TBVWYF.
DOI: 10.2147/IDR.S388868 EDN: TBVWYF

Yu G, Yang W, Zhang J, et al. Application of a nomogram to radiomics labels in the treatment prediction scheme for lumbar disc herniation. BMC Medical Imaging. 2022;22(1):51. doi: 10.1186/s12880-022-00778-6 EDN: WCRFUE.
DOI: 10.1186/s12880-022-00778-6 EDN: WCRFUE

Song M, Yang H, Yang H, et al. MR imaging radiomics analysis based on lumbar soft tissue to evaluate lumbar fascia changes in patients with low back pain. Academic Radiology. 2023;30(11):2450–2457. doi: 10.1016/j.acra.2023.02.038 EDN: UWOCTW.
DOI: 10.1016/j.acra.2023.02.038 EDN: UWOCTW

Klontzas ME, Manikis GC, Nikiforaki K, et al. Radiomics and machine learning can differentiate transient osteoporosis from avascular necrosis of the hip. Diagnostics. 2021;11(9):1686. doi: 10.3390/diagnostics11091686 EDN: WCRMFH.
DOI: 10.3390/diagnostics11091686 EDN: WCRMFH

Kim S, Kim BR, Chae HD, et al. Deep radiomics–based approach to the diagnosis of osteoporosis using hip radiographs. Radiology: Artificial Intelligence. 2022;4(4):e210212. doi: 10.1148/ryai.210212 EDN: QKWOZX.
DOI: 10.1148/ryai.210212 EDN: QKWOZX

Castañeda S, Roman-Blas JA, Largo R, Herrero-Beaumont G. Subchondral bone as a key target for osteoarthritis treatment. Biochemical Pharmacology. 2012;83(3):315–323. doi: 10.1016/j.bcp.2011.09.018.
DOI: 10.1016/j.bcp.2011.09.018

Lin T, Peng S, Lu S, et al. Prediction of knee pain improvement over two years for knee osteoarthritis using a dynamic nomogram based on MRI-derived radiomics: a proof-of-concept study. Osteoarthritis and Cartilage. 2023;31(2):267–278. doi: 10.1016/j.joca.2022.10.014 EDN: HEPKTM.
DOI: 10.1016/j.joca.2022.10.014 EDN: HEPKTM

Xie Y, Dan Y, Tao H, et al. Radiomics feature analysis of cartilage and subchondral bone in differentiating knees predisposed to posttraumatic osteoarthritis after anterior cruciate ligament reconstruction from healthy knees. BioMed Research International. 2021;2021(1):4351499. doi: 10.1155/2021/4351499 EDN: GEGKRA.
DOI: 10.1155/2021/4351499 EDN: GEGKRA

Sander R. Asymptomatic osteoporosis masks the importance of taking medication. Nursing Older People. 2007;19(10):23–23. doi: 10.7748/nop.19.10.23.s21.
DOI: 10.7748/nop.19.10.23.s21

Li W, Feng J, Zhu D, et al. Nomogram model based on radiomics signatures and age to assist in the diagnosis of knee osteoarthritis. Experimental Gerontology. 2023;171:112031. doi: 10.1016/j.exger.2022.112031 EDN: OJHMQD.
DOI: 10.1016/j.exger.2022.112031 EDN: OJHMQD

Valesan LF, Da-Cas CD, Réus JC, et al. Prevalence of temporomandibular joint disorders: a systematic review and meta-analysis. Clinical Oral Investigations. 2021;25(2):441–453. doi: 10.1007/s00784-020-03710-w EDN: LUZGIY.
DOI: 10.1007/s00784-020-03710-w EDN: LUZGIY

Orhan K, Driesen L, Shujaat S, et al. Development and validation of a magnetic resonance imaging-based machine learning model for TMJ pathologies. BioMed Research International. 2021;2021(1):6656773. doi: 10.1155/2021/6656773 EDN: IKONFS.
DOI: 10.1155/2021/6656773 EDN: IKONFS

Muraoka H, Kaneda T, Hirahara N, et al. Magnetic resonance image texture analysis of the lateral pterygoid muscle in patients with rheumatoid arthritis: a preliminary report. Oral Radiology. 2022;39(2):242–247. doi: 10.1007/s11282-022-00625-y EDN: ZDPFQW.
DOI: 10.1007/s11282-022-00625-y EDN: ZDPFQW

Ricardo ALF, da Silva GA, Ogawa CM, et al. Magnetic resonance imaging texture analysis for quantitative evaluation of the mandibular condyle in juvenile idiopathic arthritis. Oral Radiology. 2022;39(2):329–340. doi: 10.1007/s11282-022-00641-y EDN: LWUNPK.
DOI: 10.1007/s11282-022-00641-y EDN: LWUNPK

Wang L, Wen D, Yin Y, et al. Musculoskeletal ultrasound image-based radiomics for the diagnosis of achilles tendinopathy in skiers. Journal of Ultrasound in Medicine. 2022;42(2):363–371. doi: 10.1002/jum.16059 EDN: RFSUOT.
DOI: 10.1002/jum.16059 EDN: RFSUOT

Gulani V, Seiberlich N. Quantitative MRI: rationale and challenges. In: Seiberlich N, Gulani V, Calamante F, editors. Quantitative magnetic resonance imaging. London: Elsevier; 2020. P. xxxvii–li. doi: 10.1016/B978-0-12-817057-1.00001-9.
DOI: 10.1016/B978-0-12-817057-1.00001-9

Jiang H, Chen L, Zhao Y, et al. Machine learning-based ultrasomics for predicting subacromial impingement syndrome stages. Journal of Ultrasound in Medicine. 2021;41(9):2279–2285. doi: 10.1002/jum.15914 EDN: YEOHCZ.
DOI: 10.1002/jum.15914 EDN: YEOHCZ

Yin R, Jiang M, Lv WZ, et al. Study processes and applications of ultrasomics in precision medicine. Frontiers in Oncology. 2020;10:1736. doi: 10.3389/fonc.2020.01736 EDN: QZLABO.
DOI: 10.3389/fonc.2020.01736 EDN: QZLABO

AL Qurri A, Almekkawy M. Improved UNet with attention for medical image segmentation. Sensors. 2023;23(20):8589. doi: 10.3390/s23208589 EDN: KNTAZU.
DOI: 10.3390/s23208589 EDN: KNTAZU

Bach Cuadra M, Favre J, Omoumi P. Quantification in musculoskeletal imaging using computational analysis and machine learning: segmentation and radiomics. Seminars in Musculoskeletal Radiology. 2020;24(1):50–64. doi: 10.1055/s-0039-3400268 EDN: KXRSQY.
DOI: 10.1055/s-0039-3400268 EDN: KXRSQY

Anwar SM, Majid M, Qayyum A, et al. Medical image analysis using convolutional neural networks: a review. Journal of Medical Systems. 2018;42(11):226. doi: 10.1007/s10916-018-1088-1 EDN: ZTZIEH.
DOI: 10.1007/s10916-018-1088-1 EDN: ZTZIEH

Miranda J, Horvat N, Fonseca GM, et al. Current status and future perspectives of radiomics in hepatocellular carcinoma. World Journal of Gastroenterology. 2023;29(1):43–60. doi: 10.3748/wjg.v29.i1.43 EDN: QDGABL.
DOI: 10.3748/wjg.v29.i1.43 EDN: QDGABL

Kocak B, Akinci D’Antonoli T, Mercaldo N, et al. Methodological radiomics score (METRICS): a quality scoring tool for radiomics research endorsed by EUSUMII. Insights into Imaging. 2024;15(1):8. doi: 10.1186/s13244-023-01572-w EDN: CINMDC.
DOI: 10.1186/s13244-023-01572-w EDN: CINMDC

Aghakhanyan G, Filidei T, Febi M, et al. Advancing pediatric sarcomas through radiomics: a systematic review and prospective assessment using radiomics quality score (RQS) and methodological radiomics score (METRICS). Diagnostics. 2024;14(8):832. doi: 10.3390/diagnostics14080832 EDN: GFBZWJ.
DOI: 10.3390/diagnostics14080832 EDN: GFBZWJ