Journal of Shanghai Jiao Tong University (Medical Science) >
Application of non-invasive methods of radiology to the osteoporosis
Received date: 2022-08-22
Accepted date: 2023-02-17
Online published: 2023-03-28
Supported by
Shanghai Top Academic Leaders Program of Shanghai Science and Technology Commission(20XD1402600)
Early screening and timely treatment can effectively reduce the morbidity and mortality of the osteoporotic fractures, and hence efficient and accurate non-invasive radiological method is crucial. Non-invasive imaging methods of radiology frequently encounter less resistance in the promotion of therapeutic activities than invasive procedures like bone biopsy and perfusion imaging. Although dual-energy X-ray absorption has been established as the primary diagnostic method for osteoporosis, its efficacy is relatively constrained due to various parameters, and it is challenging to accurately depict the true status of bone structure. In recent years, radiological techniques have developed rapidly. Computed tomography, magnetic resonance imaging, quantitative ultrasound and other imaging techniques have been widely used in the diagnosis of osteoporosis in the research and clinical practices, which provides more comprehensive and detailed information about bone mineral density and bone structure for early diagnosis, treatment design and prognosis monitoring. As clinic and computer science crosstalk closely, it will become possible for artificial intelligence to assist or even independently perform imaging analysis and disease screening in image data base. This article reviews the individual characteristics and latest research progress of the non-invasive radiological techniques for the osteoporosis.
Chenjun LIU , Bohao YIN , Hui SUN , Wei ZHANG . Application of non-invasive methods of radiology to the osteoporosis[J]. Journal of Shanghai Jiao Tong University (Medical Science), 2023 , 43(3) : 385 -390 . DOI: 10.3969/j.issn.1674-8115.2023.03.016
| 1 | WRIGHT N C, SAAG K G, DAWSON-HUGHES B, et al. The impact of the new National Bone Health Alliance (NBHA) diagnostic criteria on the prevalence of osteoporosis in the United States: supplementary presentation[J]. Osteoporos Int, 2017, 28(11): 3283-3284. |
| 2 | KIMMEL D B, VENNIN S, DESYATOVA A, et al. Bone architecture, bone material properties, and bone turnover in non-osteoporotic post-menopausal women with fragility fracture[J]. Osteoporos Int, 2022, 33(5): 1125-1136. |
| 3 | ADAMS J E. Advances in bone imaging for osteoporosis[J]. Nat Rev Endocrinol, 2013, 9(1): 28-42. |
| 4 | PADLINA I, GONZALEZ-RODRIGUEZ E, HANS D, et al. The lumbar spine age-related degenerative disease influences the BMD not the TBS: the Osteolaus cohort[J]. Osteoporos Int, 2017, 28(3): 909-915. |
| 5 | AMNUAYWATTAKORN S, SRITARA C, UTAMAKUL C, et al. Simulated increased soft tissue thickness artefactually decreases trabecular bone score: a phantom study[J]. BMC Musculoskelet Disord, 2016, 17(1): 17. |
| 6 | RAJAN R, CHERIAN K E, KAPOOR N, et al. Trabecular bone score-an emerging tool in the management of osteoporosis[J]. Indian J Endocrinol Metab, 2020, 24(3): 237-243. |
| 7 | MESSINA C, RINAUDO L, CESANA B M, et al. Prediction of osteoporotic fragility re-fracture with lumbar spine DXA-based derived bone strain index: a multicenter validation study[J]. Osteoporos Int, 2021, 32(1): 85-91. |
| 8 | HUMBERT L, MARTELLI Y, FONOLLà R, et al. 3D-DXA: assessing the femoral shape, the trabecular macrostructure and the cortex in 3D from DXA images[J]. IEEE Trans Med Imaging, 2017, 36(1): 27-39. |
| 9 | CLOTET J, MARTELLI Y, DI GREGORIO S, et al. Structural parameters of the proximal femur by 3-dimensional dual-energy X-ray absorptiometry software: comparison with quantitative computed tomography[J]. J Clin Densitom, 2018, 21(4): 550-562. |
| 10 | HE Q F, SUN H, SHU L Y, et al. Radiographic predictors for bone mineral loss: cortical thickness and index of the distal femur[J]. Bone Joint Res, 2018, 7(7): 468-475. |
| 11 | LIM H K, HA H I, PARK S Y, et al. Comparison of the diagnostic performance of CT Hounsfield unit histogram analysis and dual-energy X-ray absorptiometry in predicting osteoporosis of the femur[J]. Eur Radiol, 2019, 29(4): 1831-1840. |
| 12 | LI Y L, WONG K H, LAW M W M, et al. Opportunistic screening for osteoporosis in abdominal computed tomography for Chinese population[J]. Arch Osteoporos, 2018, 13(1): 76. |
| 13 | POOLE K E, TREECE G M, PEARSON R A, et al. Romosozumab enhances vertebral bone structure in women with low bone density[J]. J Bone Miner Res, 2022, 37(2): 256-264. |
| 14 | SU Y B, WANG L, LIU X Y, et al. Lack of periosteal apposition in the head and neck of femur after menopause in Chinese women with high risk for hip fractures—a cross-sectional study with QCT[J]. Bone, 2020, 139: 115545. |
| 15 | 程晓光, 王亮, 曾强, 等. 中国定量CT骨质疏松症诊断指南(2018)[J]. 中华健康管理学杂志, 2019, 5(3): 195-200. |
| 15 | CHEN X G, WANG L, ZENG Q, et al. Chinese guideline for the diagnosis of osteoporosis with quantitative computed tomography(2018)[J]. Chin J Health Manage, 2019, 5(3): 195-200. |
| 16 | SCHULTE F A, CHRISTEN P, BADILATTI S D, et al. Virtual supersampling as post-processing step preserves the trabecular bone morphometry in human peripheral quantitative computed tomography scans[J]. PLoS One, 2019, 14(2): e0212280. |
| 17 | HANSEN S, SHANBHOGUE V, FOLKESTAD L, et al. Bone microarchitecture and estimated strength in 499 adult Danish women and men: a cross-sectional, population-based high-resolution peripheral quantitative computed tomographic study on peak bone structure[J]. Calcif Tissue Int, 2014, 94(3): 269-281. |
| 18 | SAMELSON E J, BROE K E, XU H F, et al. Cortical and trabecular bone microarchitecture as an independent predictor of incident fracture risk in older women and men in the Bone Microarchitecture International Consortium (BoMIC): a prospective study[J]. Lancet Diabetes Endocrinol, 2019, 7(1): 34-43. |
| 19 | BURT L A, LIANG Z Y, SAJOBI T T, et al. Sex- and site-specific normative data curves for HR-pQCT[J]. J Bone Miner Res, 2016, 31(11): 2041-2047. |
| 20 | ALVARENGA J C, CAPARBO V F, DOMICIANO D S, et al. Age-related reference data of bone microarchitecture, volumetric bone density, and bone strength parameters in a population of healthy Brazilian men: an HR-pQCT study[J]. Osteoporos Int, 2022, 33(6): 1309-1321. |
| 21 | RAMCHAND S K, DAVID N L, LEE H, et al. Effects of combination denosumab and high-dose teriparatide administration on bone microarchitecture and estimated strength: the DATA-HD HR-pQCT study[J]. J Bone Miner Res, 2021, 36(1): 41-51. |
| 22 | NILSSON A G, SUNDH D, JOHANSSON L, et al. Type 2 diabetes mellitus is associated with better bone microarchitecture but lower bone material strength and poorer physical function in elderly women: a population-based study[J]. J Bone Miner Res, 2017, 32(5): 1062-1071. |
| 23 | MANSKE S L, DAVISON E M, BURT L A, et al. The estimation of second-generation HR-pQCT from first-generation HR-pQCT using in vivo cross-calibration[J]. J Bone Miner Res, 2017, 32(7): 1514-1524. |
| 24 | SODICKSON A D, KERALIYA A, CZAKOWSKI B, et al. Dual energy CT in clinical routine: how it works and how it adds value[J]. Emerg Radiol, 2021, 28(1): 103-117. |
| 25 | ZHOU S W, ZHU L, YOU T, et al. In vivo quantification of bone mineral density of lumbar vertebrae using fast kVp switching dual-energy CT: correlation with quantitative computed tomography[J]. Quant Imaging Med Surg, 2021, 11(1): 341-350. |
| 26 | SHEN W, SCHERZER R, GANTZ M, et al. Relationship between MRI-measured bone marrow adipose tissue and hip and spine bone mineral density in African-American and Caucasian participants: the CARDIA study[J]. J Clin Endocrinol Metab, 2012, 97(4): 1337-1346. |
| 27 | LI J, CHEN X, LU L Y, et al. The relationship between bone marrow adipose tissue and bone metabolism in postmenopausal osteoporosis[J]. Cytokine Growth Factor Rev, 2020, 52: 88-98. |
| 28 | LIU Z H, ZHANG Y T, LIU Z, et al. Dual-energy computed tomography virtual noncalcium technique in diagnosing osteoporosis: correlation with quantitative computed tomography[J]. J Comput Assist Tomogr, 2021, 45(3): 452-457. |
| 29 | WICHMANN J L, BOOZ C, WESARG S, et al. Dual-energy CT-based phantomless in vivo three-dimensional bone mineral density assessment of the lumbar spine[J]. Radiology, 2014, 271(3): 778-784. |
| 30 | BOOZ C, HOFMANN P C, SEDLMAIR M, et al. Evaluation of bone mineral density of the lumbar spine using a novel phantomless dual-energy CT post-processing algorithm in comparison with dual-energy X-ray absorptiometry[J]. Eur Radiol Exp, 2017, 1(1): 11. |
| 31 | CHANG G, HONIG S, LIU Y X, et al. 7 Tesla MRI of bone microarchitecture discriminates between women without and with fragility fractures who do not differ by bone mineral density[J]. J Bone Miner Metab, 2015, 33(3): 285-293. |
| 32 | RAJAPAKSE C S, HOTCA A, NEWMAN B T, et al. Patient-specific hip fracture strength assessment with microstructural MR imaging-based finite element modeling[J]. Radiology, 2017, 283(3): 854-861. |
| 33 | RAJAPAKSE C S, FARID A R, KARGILIS D C, et al. MRI-based assessment of proximal femur strength compared to mechanical testing[J]. Bone, 2020, 133: 115227. |
| 34 | ZHANG L Y, WANG L, FU R S, et al. In vivo assessment of age- and loading configuration-related changes in multiscale mechanical behavior of the human proximal femur using MRI-based finite element analysis[J]. J Magn Reson Imaging, 2021, 53(3): 905-912. |
| 35 | JERBAN S, MA Y J, JANG H, et al. Water proton density in human cortical bone obtained from ultrashort echo time (UTE) MRI predicts bone microstructural properties[J]. Magn Reson Imaging, 2020, 67: 85-89. |
| 36 | BAUM T, YAP S P, KARAMPINOS D C, et al. Does vertebral bone marrow fat content correlate with abdominal adipose tissue, lumbar spine bone mineral density, and blood biomarkers in women with type 2 diabetes mellitus?[J]. J Magn Reson Imaging, 2012, 35(1): 117-124. |
| 37 | HE J, FANG H, LI X N. Vertebral bone marrow fat content in normal adults with varying bone densities at 3T magnetic resonance imaging[J]. Acta Radiol, 2019, 60(4): 509-515. |
| 38 | LI X J, SHET K, XU K P, et al. Unsaturation level decreased in bone marrow fat of postmenopausal women with low bone density using high resolution magic angle spinning (HRMAS) 1H NMR spectroscopy[J]. Bone, 2017, 105: 87-92. |
| 39 | WOODS G N, EWING S K, SCHAFER A L, et al. Saturated and unsaturated bone marrow lipids have distinct effects on bone density and fracture risk in older adults[J]. J Bone Miner Res, 2022, 37(4): 700-710. |
| 40 | GUO Y H, CHEN Y J, ZHANG X T, et al. Magnetic susceptibility and fat content in the lumbar spine of postmenopausal women with varying bone mineral density[J]. J Magn Reson Imaging, 2019, 49(4): 1020-1028. |
| 41 | MOMENI M, ASADZADEH M, MOWLA K, et al. Sensitivity and specificity assessment of DWI and ADC for the diagnosis of osteoporosis in postmenopausal patients[J]. Radiol med, 2020, 125(1): 68-74. |
| 42 | RAUM K, GRIMAL Q, VARGA P, et al. Ultrasound to assess bone quality[J]. Curr Osteoporos Rep, 2014, 12(2): 154-162. |
| 43 | MOAYYERI A, ADAMS J E, ADLER R A, et al. Quantitative ultrasound of the heel and fracture risk assessment: an updated meta-analysis[J]. Osteoporos Int, 2012, 23(1): 143-153. |
| 44 | MCCLOSKEY E V, KANIS J A, ODéN A, et al. Predictive ability of heel quantitative ultrasound for incident fractures: an individual-level meta-analysis[J]. Osteoporos Int, 2015, 26(7): 1979-1987. |
| 45 | LIU Z J, ZHANG C, MA C, et al. Automatic phantom-less QCT system with high precision of BMD measurement for osteoporosis screening: technique optimisation and clinical validation[J]. J Orthop Transl, 2022, 33: 24-30. |
| 46 | LI Y C, CHEN H H, LU H H S, et al. Can a deep-learning model for the automated detection of vertebral fractures approach the performance level of human subspecialists?[J]. Clin Orthop Relat Res, 2021, 479(7): 1598-1612. |
| 47 | FANG Y J, LI W, CHEN X J, et al. Opportunistic osteoporosis screening in multi-detector CT images using deep convolutional neural networks[J]. Eur Radiol, 2021, 31(4): 1831-1842. |
/
| 〈 |
|
〉 |