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:: Volume 22, Issue 4 (10-2024) ::
Int J Radiat Res 2024, 22(4): 927-931 Back to browse issues page
Application value of MRI radiomics in differential diagnosis of osteoporotic and malignant neoplastic vertebral compression fractures
Y. Li , H. Zhang , Y. Qian , Zh. Gao , Zh. Han , M. Zhan , Ch. Xu
Department of Radiology, Affiliated Xiaoshan Hospital, Hangzhou Normal University, Hangzhou, Zhejiang, 310000, China , xcf628@163.com
Abstract:   (445 Views)
Background: To explore the radiomics features of osteoporotic and malignant neoplastic vertebral compression fractures (VCFs), and to analyze the application value of radiomics in differential diagnosis of osteoporotic and malignant neoplastic VCFs. Materials and Methods: Fifty-one patients with VCFs caused by malignant tumors and forty-nine patients with osteoporosis-induced VCFs treated in the Xiaoshan Hospital Affiliated to Hangzhou Normal University from January 2020 to June 2023 were retrospectively collected into a training set (70 cases) and a verification set (30 cases) according to a stratified random sampling design and a 7:3 ratio. The radiomics parameters of T2WI images of the diseased vertebral bodies were extracted, and the parameters with statistical differences were screened out by dimensionality reduction, so as to build a prediction model. Receiver operating characteristic (ROC) curves were drawn to evaluate the differential diagnosis performance of radiomics for the etiology of vertebral fractures. Results: Eight radiomics features were obtained after dimensionality reduction using the LASSO algorithm. The constructed model was effective in differentiating osteoporotic and malignant neoplastic VCFs, with an area under the ROC curve (AUC) of 0.95; while the AUC for the validation set was 0.84. Conclusions: The radiomics features of T2WI images of vertebral fractures have high efficiency in the differential diagnosis of fracture etiology.
Keywords: agnetic resonance imaging, osteoporosis, compression fractures, neoplasms.
Full-Text [PDF 688 kb]   (123 Downloads)    
Type of Study: Original Research | Subject: Radiation Biology
References
1. Perrin RG and Laxton AW (2004) Metastatic spine disease: epidemiology, pathophysiology, and evaluation of patients. Neurosurg Clin N Am, 15(4): 365-373. [DOI:10.1016/j.nec.2004.04.018] [PMID]
2. Avellino AM, Mann FA, Grady MS, et al. (2005) The misdiagnosis of acute cervical spine injuries and fractures in infants and children: the 12-year experience of a level I pediatric and adult trauma center. Childs Nerv Syst, 21(2): 122-127. [DOI:10.1007/s00381-004-1058-4] [PMID]
3. Diacinti D, Vitali C, Gussoni G, et al. (2017) Misdiagnosis of vertebral fractures on local radiographic readings of the multicentre POINT (Prevalence of Osteoporosis in INTernal medicine) study. Bone, 101: 230-235. doi: 10.1016/j.bone.2017.05.008. [DOI:10.1016/j.bone.2017.05.008] [PMID]
4. Bobholz SA, Lowman AK, Barrington A, et al. (2020) Radiomic features of multiparametric MRI present stable associations with analogous histological ffeatures in patients with brain cancer. Tomography, 6(2): 160-169. [DOI:10.18383/j.tom.2019.00029] [PMID] []
5. Antunes JT, Ofshteyn A, Bera K, et al. (2020) Radiomic Features of primary rectal cancers on baseline T2 -weighted MRI are associated with pathologic complete response to neoadjuvant chemoradiation: A multisite study. J Magn Reson Imaging, 52(5): 1531-1541. [DOI:10.1002/jmri.27140] [PMID] []
6. Hectors SJ, Lewis S, Besa C, et al. (2020) MRI radiomics features predict immuno-oncological characteristics of hepatocellular carcinoma. Eur Radiol, 30(7): 3759-3769. [DOI:10.1007/s00330-020-06675-2] [PMID] []
7. Yu Y, He Z, Ouyang J, et al. (2021) Magnetic resonance imaging radiomics predicts preoperative axillary lymph node metastasis to support surgical decisions and is associated with tumor microenvironment in invasive breast cancer: A machine learning, multicenter study. EBioMedicine, 69: 103460. [DOI:10.1016/j.ebiom.2021.103460] [PMID] []
8. Barachini O, Schaer M, Mirzaei S, et al. (2022) Evaluation of MRI-based radiomic features in heart morphologic variations as a consequence of autoimmune thyroid disorders. Medicine (Baltimore), 101(34): e30197. [DOI:10.1097/MD.0000000000030197] [PMID] []
9. Hirvasniemi J, Klein S, Bierma-Zeinstra S, et al. (2021) A machine learning approach to distinguish between knees without and with osteoarthritis using MRI-based radiomic features from tibial bone. Eur Radiol, 31(11): 8513-8521. [DOI:10.1007/s00330-021-07951-5] [PMID] []
10. Suh CH, Yun SJ, Jin W, et al. (2018) ADC as a useful diagnostic tool for differentiating benign and malignant vertebral bone marrow lesions and compression fractures: A systematic review and meta-analysis. Eur Radiol, 28(7): 2890-2902. [DOI:10.1007/s00330-018-5330-5] [PMID]
11. Sartoretti E, Sartoretti-Schefer S, van Smoorenburg L, et al. (2021) Single shot zonal oblique multislice SE-EPI diffusion-weighted imaging with low to ultra-high b-values for the differentiation of benign and malignant vertebral spinal fractures. Eur J Radiol Open, 8: 100377. [DOI:10.1016/j.ejro.2021.100377] [PMID] []
12. Tan H, Xu H, Luo F, et al. (2019) Combined intravoxel incoherent motion diffusion-weighted MR imaging and magnetic resonance spectroscopy in differentiation between osteoporotic and metastatic vertebral compression fractures. J Orthop Surg Res, 14(1): 299. [DOI:10.1186/s13018-019-1350-3] [PMID] []
13. Bacher S, Hajdu SD, Maeder Y, et al. (2021) Differentiation between benign and malignant vertebral compression fractures using qualitative and quantitative analysis of a single fast spin echo T2-weighted Dixon sequence. Eur Radiol, 31(12): 9418-9427. [DOI:10.1007/s00330-021-07947-1] [PMID] []
14. Li Y, Zhang Y, Zhang E, et al. (2021) Differential diagnosis of benign and malignant vertebral fracture on CT using deep learning. Eur Radiol, 31(12): 9612-9619. [DOI:10.1007/s00330-021-08014-5] [PMID] []
15. Choudhery S, Gomez-Cardona D, Favazza CP, et al. (2022) MRI Radiomics for assessment of molecular subtype, pathological complete response and residual cancer burden in breast cancer patients treated with neoadjuvant chemotherapy. Acad Radiol, 29 Suppl 1(Suppl 1): S145-S154. [DOI:10.1016/j.acra.2020.10.020] [PMID] []
16. Ming X, Oei RW, Zhai R, et al. (2019) MRI-based radiomics signature is a quantitative prognostic biomarker for nasopharyngeal carcinoma. Sci Rep, 9(1): 10412. [DOI:10.1038/s41598-019-46985-0] [PMID] []
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Li Y, Zhang H, Qian Y, Gao Z, Han Z, Zhan M et al . Application value of MRI radiomics in differential diagnosis of osteoporotic and malignant neoplastic vertebral compression fractures. Int J Radiat Res 2024; 22 (4) :927-931
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Volume 22, Issue 4 (10-2024) Back to browse issues page
International Journal of Radiation Research
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