Share this content in WeChat
Application of radiomics in spinal diseases
Pahati·Tuxunjiang   YANG Laihong  HE Xiong  CHANG Yushan  GUO Hui 

Cite this article as: Citation:Tuxunjiang P, Yang LH, He X, et al. Application of radiomics in spinal diseases[J]. Chin J Magn Reson Imaging, 2022, 13(5): 162-166. DOI:10.12015/issn.1674-8034.2022.05.035.

[Abstract] Radiomics, which extracts and quantifies feature information from medical images that cannot be recognized by traditional image examination methods, has gradually become a research hot spot in the clinical implementation of precision medicine and personalized medicine. The clinical symptoms of spinal diseases are single, and traditional imaging methods are still challenging for accurate localization, diagnosis and differential diagnosis of some spinal diseases. The cross fusion of artificial intelligence and images has greatly improved the accuracy of disease diagnosis by front-line workers and realized the prediction of unknown data of diseases. At present, there is no systematic review of the application of imaging in the diagnosis of spinal diseases. Therefore, the present situation and progress of the application of radiomics in spinal diseases are emphatically summarized, and the challenges and future development direction of spinal radiography are proposed.
[Keywords] spinal disease;machine leaning;deep learning;artificial intelligence;radiomics

Pahati·Tuxunjiang    YANG Laihong   HE Xiong   CHANG Yushan   GUO Hui*  

Department of Radiology, Affiliated First Hospital of Xinjiang Medical University, Urumqi 830054, China

Guo H, E-mail:

Conflicts of interest   None.

ACKNOWLEDGMENTS General Projects of Natural Science Foundation of Xinjiang Uygur Autonomous Region (No. 2017D01C300); Graduate Innovation and Entrepreneurship Project of Xinjiang Medical University (No. CXCY2021017).
Received  2021-12-24
Accepted  2022-04-13
DOI: 10.12015/issn.1674-8034.2022.05.035
Cite this article as: Citation:Tuxunjiang P, Yang LH, He X, et al. Application of radiomics in spinal diseases[J]. Chin J Magn Reson Imaging, 2022, 13(5): 162-166. DOI:10.12015/issn.1674-8034.2022.05.035.

Waldrop R, Cheng J, Devin C, et al. The burden of spinal disorders in the elderly[J]. Neurosurgery, 2015, 77(Suppl 4): S46-S50. DOI: 10.1227/NEU.0000000000000950.
Chin CT. Spine imaging[J]. Semin Neurol, 2002, 22(2): 205-220. DOI: 10.1055/s-2002-36544.
Dodson SC, Koontz NA. Spinal manifestations of systemic disease[J]. Radiol Clin North Am, 2019, 57(2): 281-306. DOI: 10.1016/j.rcl.2018.10.005.
Yang HL, Liu T, Wang XM, et al. Diagnosis of bone metastases: a meta-analysis comparing 18FDG PET, CT, MRI and bone scintigraphy[J]. Eur Radiol, 2011, 21(12): 2604-2617. DOI: 10.1007/s00330-011-2221-4.
Duong MT, Rauschecker AM, Mohan S. Diverse applications of artificial intelligence in neuroradiology[J]. Neuroimaging Clin N Am, 2020, 30(4): 505-516. DOI: 10.1016/j.nic.2020.07.003.
Mekov E, Miravitlles M, Petkov R. Artificial intelligence and machine learning in respiratory medicine[J]. Expert Rev Respir Med, 2020, 14(6): 559-564. DOI: 10.1080/17476348.2020.1743181.
Gyftopoulos S, Lin DN, Knoll F, et al. Artificial intelligence in musculoskeletal imaging: current status and future directions[J]. AJR Am J Roentgenol, 2019, 213(3): 506-513. DOI: 10.2214/AJR.19.21117.
Lim LJ, Tison GH, Delling FN. Artificial intelligence in cardiovascular imaging[J]. Methodist Debakey Cardiovasc J, 2020, 16(2): 138-145. DOI: 10.14797/mdcj-16-2-138.
le Berre C, Sandborn WJ, Aridhi S, et al. Application of artificial intelligence to gastroenterology and hepatology[J]. Gastroenterology, 2020, 158(1): 76-94.e2. DOI: 10.1053/j.gastro.2019.08.058.
Morgan MB, Mates JL. Applications of artificial intelligence in breast imaging[J]. Radiol Clin North Am, 2021, 59(1): 139-148. DOI: 10.1016/j.rcl.2020.08.007.
Wang RJ, Pan W, Jin L, et al. Artificial intelligence in reproductive medicine[J]. Reproduction, 2019, 158(4): R139-R154. DOI: 10.1530/REP-18-0523.
Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: extracting more information from medical images using advanced feature analysis[J]. Eur J Cancer, 2012, 48(4): 441-446. DOI: 10.1016/j.ejca.2011.11.036.
Kumar V, Gu YH, Basu S, et al. Radiomics: the process and the challenges[J]. Magn Reson Imaging, 2012, 30(9): 1234-1248. DOI: 10.1016/j.mri.2012.06.010.
Mayerhoefer ME, Materka A, Langs G, et al. Introduction to radiomics[J]. J Nucl Med, 2020, 61(4): 488-495. DOI: 10.2967/jnumed.118.222893.
Gardin I, Grégoire V, Gibon D, et al. Radiomics: principles and radiotherapy applications[J]. Crit Rev Oncol Hematol, 2019, 138: 44-50. DOI: 10.1016/j.critrevonc.2019.03.015.
Yip SSF, Aerts HJWL. Applications and limitations of radiomics[J]. Phys Med Biol, 2016, 61(13): R150-R166. DOI: 10.1088/0031-9155/61/13/R150.
Ligero M, Jordi-Ollero O, Bernatowicz K, et al. Minimizing acquisition-related radiomics variability by image resampling and batch effect correction to allow for large-scale data analysis[J] Eur Radiol, 2020, 31(3): 1460-1470. DOI: 10.1007/s00330-020-07174-0.
Langs G, Röhrich S, Hofmanninger J, et al. Machine learning: from radiomics to discovery and routine[J]. Radiologe, 2018, 58(Suppl 1): 1-6. DOI: 10.1007/s00117-018-0407-3.
Velazquez ER, Parmar C, Jermoumi M, et al. Volumetric CT-based segmentation of NSCLC using 3D-slicer[J]. Sci Rep, 2013, 3: 3529. DOI: 10.1038/srep03529.
Yushkevich PA, Piven J, Hazlett HC, et al. User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability[J]. Neuroimage, 2006, 31(3): 1116-1128. DOI: 10.1016/j.neuroimage.2006.01.015.
Fedorov A, Beichel R, Kalpathy-Cramer J, et al. 3D Slicer as an image computing platform for the Quantitative Imaging Network[J]. Magn Reson Imaging, 2012, 30(9): 1323-1341. DOI: 10.1016/j.mri.2012.05.001.
Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data[J]. Radiology, 2016, 278(2): 563-577. DOI: 10.1148/radiol.2015151169.
Parmar C, Grossmann P, Bussink J, et al. Machine learning methods for quantitative radiomic biomarkers[J]. Sci Rep, 2015, 5: 13087. DOI: 10.1038/srep13087.
Suri A, Jones BC, Ng G, et al. A deep learning system for automated, multi-modality 2D segmentation of vertebral bodies and intervertebral discs[J]. Bone, 2021, 149: 115972. DOI: 10.1016/j.bone.2021.115972.
Zhou YJ, Liu Y, Chen Q, et al. Automatic lumbar MRI detection and identification based on deep learning[J]. J Digit Imaging, 2019, 32(3): 513-520. DOI: 10.1007/s10278-018-0130-7.
Galbusera F, Casaroli G, Bassani T. Artificial intelligence and machine learning in spine research[J]. JOR Spine, 2019, 2(1): e1044. DOI: 10.1002/jsp2.1044.
Lessmann N, van Ginneken B, de Jong PA, et al. Iterative fully convolutional neural networks for automatic vertebra segmentation and identification[J]. Med Image Anal, 2019, 53: 142-155. DOI: 10.1016/
Kim YJ, Ganbold B, Kim KG. Web-based spine segmentation using deep learning in computed tomography images[J]. Healthc Inform Res, 2020, 26(1): 61-67. DOI: 10.4258/hir.2020.26.1.61.
Zawy Alsofy S, Stroop R, Fusek I, et al. Virtual reality-based evaluation of surgical planning and outcome of monosegmental, unilateral cervical foraminal Stenosis[J]. World Neurosurg, 2019, 129: e857-e865. DOI: 10.1016/j.wneu.2019.06.057.
Chang M, Canseco JA, Nicholson KJ, et al. The role of machine learning in spine surgery: the future is now[J]. Front Surg, 2020, 7: 54. DOI: 10.3389/fsurg.2020.00054.
Vania M, Mureja D, Lee D. Automatic spine segmentation from CT images using Convolutional Neural Network via redundant generation of class labels[J]. J Comput Des Eng, 2019, 6(2): 224-232. DOI: 10.1016/j.jcde.2018.05.002.
Niemeyer F, Galbusera F, Tao YP, et al. A deep learning model for the accurate and reliable classification of disc degeneration based on MRI data[J]. Invest Radiol, 2021, 56(2): 78-85. DOI: 10.1097/RLI.0000000000000709.
Galbusera F, Niemeyer F, Wilke HJ, et al. Fully automated radiological analysis of spinal disorders and deformities: a deep learning approach[J]. Eur Spine J, 2019, 28(5): 951-960. DOI: 10.1007/s00586-019-05944-z.
Torres C, Zakhari N. Imaging of spine infection[J]. Semin Roentgenol, 2017, 52(1): 17-26. DOI: 10.1053/
Liu XY, Zheng MM, Sun JM, et al. A diagnostic model for differentiating tuberculous spondylitis from pyogenic spondylitis on computed tomography images[J]. Eur Radiol, 2021, 31(10): 7626-7636. DOI: 10.1007/s00330-021-07812-1.
Yin P, Mao N, Zhao C, et al. Comparison of radiomics machine-learning classifiers and feature selection for differentiation of sacral chordoma and sacral giant cell tumour based on 3D computed tomography features[J]. Eur Radiol, 2019, 29(4): 1841-1847. DOI: 10.1007/s00330-018-5730-6.
Chianca V, Cuocolo R, Gitto S, et al. Radiomic machine learning classifiers in spine bone tumors: a multi-software, multi-scanner study[J]. Eur J Radiol, 2021, 137: 109586. DOI: 10.1016/j.ejrad.2021.109586.
Chee CG, Yoon MA, Kim KW, et al. Combined radiomics-clinical model to predict malignancy of vertebral compression fractures on CT[J]. Eur Radiol, 2021, 31(9): 6825-6834. DOI: 10.1007/s00330-021-07832-x.
Liu JF, Wang CJ, Guo W, et al. A preliminary study using spinal MRI-based radiomics to predict high-risk cytogenetic abnormalities in multiple myeloma[J]. Radiol Med, 2021, 126(9): 1226-1235. DOI: 10.1007/s11547-021-01388-y.
Hwang EJ, Jung JY, Lee SK, et al. Machine learning for diagnosis of hematologic diseases in magnetic resonance imaging of lumbar spines[J]. Sci Rep, 2019, 9(1): 6046. DOI: 10.1038/s41598-019-42579-y.
He L, Liu Z, Liu CY, et al. Radiomics based on lumbar spine magnetic resonance imaging to detect osteoporosis[J]. Acad Radiol, 2021, 28(6): e165-e171. DOI: 10.1016/j.acra.2020.03.046.
Wang SQ, Hu Y, Shen YY, et al. Classification of diffusion tensor metrics for the diagnosis of a myelopathic cord using machine learning[J]. Int J Neural Syst, 2018, 28(2): 1750036. DOI: 10.1142/S0129065717500368.
Ito S, Ando K, Kobayashi K, et al. Automated detection of spinal schwannomas utilizing deep learning based on object detection from magnetic resonance imaging[J]. Spine (Phila Pa 1976), 2021, 46(2): 95-100. DOI: 10.1097/BRS.0000000000003749.
Siccoli A, de Wispelaere MP, Schröder ML, et al. Machine learning-based preoperative predictive analytics for lumbar spinal stenosis[J]. Neurosurg Focus, 2019, 46(5): E5. DOI: 10.3171/2019.2.FOCUS18723.
Wirries A, Geiger F, Hammad A, et al. Artificial intelligence facilitates decision-making in the treatment of lumbar disc herniations[J]. Eur Spine J, 2021, 30(8): 2176-2184. DOI: 10.1007/s00586-020-06613-2.
Berenguer R, Pastor-Juan MDR, Canales-Vázquez J, et al. Radiomics of CT features may be nonreproducible and redundant: influence of CT acquisition parameters[J]. Radiology, 2018, 288(2): 407-415. DOI: 10.1148/radiol.2018172361.
Lecler A, Duron L, Balvay D, et al. Combining multiple magnetic resonance imaging sequences provides independent reproducible radiomics features[J]. Sci Rep, 2019, 9(1): 2068. DOI: 10.1038/s41598-018-37984-8.

PREV Research progress of multimodality MRI in the diagnosis and treatment of intrahepatic mass-forming cholangiocarcinoma
NEXT The research progress of diagnosing meniscus injury in MRI based on deep learning

Tel & Fax: +8610-67113815    E-mail: