{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T10:45:39Z","timestamp":1783421139289,"version":"3.54.6"},"reference-count":165,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2022,8,20]],"date-time":"2022-08-20T00:00:00Z","timestamp":1660953600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"MOH\/NMRC","award":["CNIG20nov-0011, MOH-000725"],"award-info":[{"award-number":["CNIG20nov-0011, MOH-000725"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Cancers"],"abstract":"<jats:p>Spinal metastasis is the most common malignant disease of the spine. Recently, major advances in machine learning and artificial intelligence technology have led to their increased use in oncological imaging. The purpose of this study is to review and summarise the present evidence for artificial intelligence applications in the detection, classification and management of spinal metastasis, along with their potential integration into clinical practice. A systematic, detailed search of the main electronic medical databases was undertaken in concordance with the PRISMA guidelines. A total of 30 articles were retrieved from the database and reviewed. Key findings of current AI applications were compiled and summarised. The main clinical applications of AI techniques include image processing, diagnosis, decision support, treatment assistance and prognostic outcomes. In the realm of spinal oncology, artificial intelligence technologies have achieved relatively good performance and hold immense potential to aid clinicians, including enhancing work efficiency and reducing adverse events. Further research is required to validate the clinical performance of the AI tools and facilitate their integration into routine clinical practice.<\/jats:p>","DOI":"10.3390\/cancers14164025","type":"journal-article","created":{"date-parts":[[2022,8,21]],"date-time":"2022-08-21T21:05:51Z","timestamp":1661115951000},"page":"4025","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["Application of Artificial Intelligence Methods for Imaging of Spinal Metastasis"],"prefix":"10.3390","volume":"14","author":[{"given":"Wilson","family":"Ong","sequence":"first","affiliation":[{"name":"Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd., Singapore 119074, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9193-8234","authenticated-orcid":false,"given":"Lei","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of 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Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1240-5908","authenticated-orcid":false,"given":"Xi Zhen","family":"Low","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd., Singapore 119074, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yee Liang","family":"Thian","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd., Singapore 119074, Singapore"},{"name":"Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ee Chin","family":"Teo","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd., Singapore 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therapeutic opportunities","volume":"2","author":"Mundy","year":"2002","journal-title":"Nat. Rev. Cancer"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1038\/ncpneuro0116","article-title":"Surgery insight: Current management of epidural spinal cord compression from metastatic spine disease","volume":"2","author":"Witham","year":"2006","journal-title":"Nat. Clin. Pract. Neurol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1634\/theoncologist.9-2-188","article-title":"Surgical management of spinal metastases","volume":"9","author":"Klimo","year":"2004","journal-title":"Oncologist"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1053\/ctrv.2000.0210","article-title":"Metastatic bone disease: Clinical features, pathophysiology and treatment strategies","volume":"27","author":"Coleman","year":"2001","journal-title":"Cancer Treat Rev."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"41","DOI":"10.2174\/1874471011306010007","article-title":"Bone metastases radiopharmaceuticals: An overview","volume":"6","author":"Cuccurullo","year":"2013","journal-title":"Curr. Radiopharm."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1016\/j.euus.2005.09.006","article-title":"Molecular and Biological Mechanisms of Bone Metastasis","volume":"3","author":"Cecchini","year":"2005","journal-title":"EAU Update Ser."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1177\/107327481201900202","article-title":"Overview of diagnosis and management of metastatic disease to bone","volume":"19","author":"Yu","year":"2012","journal-title":"Cancer Control"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"202","DOI":"10.4329\/wjr.v7.i8.202","article-title":"Imaging of bone metastasis: An update","volume":"7","author":"Carty","year":"2015","journal-title":"World J. Radiol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1007\/s00432-016-2288-z","article-title":"Detection of vertebral metastases: A meta-analysis comparing MRI, CT, PET, BS and BS with SPECT","volume":"143","author":"Liu","year":"2017","journal-title":"J. Cancer Res. Clin. Oncol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"434","DOI":"10.2214\/AJR.14.14199","article-title":"Use of Imaging in the Management of Metastatic Spine Disease With Percutaneous Ablation and Vertebral Augmentation","volume":"205","author":"Wallace","year":"2015","journal-title":"AJR Am. J. Roentgenol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1055\/s-0030-1248703","article-title":"Whole-body magnetic resonance imaging: Assessment of skeletal metastases","volume":"14","author":"Moynagh","year":"2010","journal-title":"Semin. Musculoskelet. Radiol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1593","DOI":"10.1002\/(SICI)1097-0142(19981015)83:8<1593::AID-CNCR14>3.0.CO;2-H","article-title":"Neuroimaging and treatment implications of patients with multiple epidural spinal metastases","volume":"83","author":"Schiff","year":"1998","journal-title":"Cancer"},{"key":"ref_13","first-page":"374","article-title":"Diagnosis of bone metastasis: Recent comparative studies of imaging modalities","volume":"55","author":"Talbot","year":"2011","journal-title":"Q. J. Nucl. Med. Mol. Imaging"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1186\/s13073-021-00968-x","article-title":"Deep learning in cancer diagnosis, prognosis and treatment selection","volume":"13","author":"Tran","year":"2021","journal-title":"Genome Med."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1303","DOI":"10.7150\/thno.30309","article-title":"The Applications of Radiomics in Precision Diagnosis and Treatment of Oncology: Opportunities and Challenges","volume":"9","author":"Liu","year":"2019","journal-title":"Theranostics"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1080\/23808993.2019.1585805","article-title":"Deep learning and radiomics in precision medicine","volume":"4","author":"Parekh","year":"2019","journal-title":"Expert Rev. Precis Med. Drug Dev."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1038\/nature21056","article-title":"Dermatologist-level classification of skin cancer with deep neural networks","volume":"542","author":"Esteva","year":"2017","journal-title":"Nature"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1122","DOI":"10.1016\/j.cell.2018.02.010","article-title":"Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning","volume":"172","author":"Kermany","year":"2018","journal-title":"Cell"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1342","DOI":"10.1038\/s41591-018-0107-6","article-title":"Clinically applicable deep learning for diagnosis and referral in retinal disease","volume":"24","author":"Ledsam","year":"2018","journal-title":"Nat. Med."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2043","DOI":"10.1007\/s11831-021-09648-w","article-title":"A Systematic Review of Artificial Intelligence Techniques in Cancer Prediction and Diagnosis","volume":"29","author":"Kumar","year":"2022","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"418","DOI":"10.1016\/j.molonc.2016.01.001","article-title":"Circulating tumor cells in breast cancer","volume":"10","author":"Bidard","year":"2016","journal-title":"Mol. Oncol."},{"key":"ref_22","first-page":"1402","article-title":"Breast Cancer Detection using Machine Learning Way","volume":"8","author":"Nallamala","year":"2019","journal-title":"Int. J. Recent Technol. Eng."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1459","DOI":"10.1007\/s12065-020-00403-x","article-title":"Automated mammogram breast cancer detection using the optimized combination of convolutional and recurrent neural network","volume":"14","author":"Patil","year":"2021","journal-title":"Evol. Intell."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"10509","DOI":"10.1038\/s41598-019-46718-3","article-title":"Pan-Renal Cell Carcinoma classification and survival prediction from histopathology images using deep learning","volume":"9","author":"Tabibu","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1016\/j.ajur.2022.05.003","article-title":"Artificial intelligence for renal cancer: From imaging to histology and beyond","volume":"9","author":"Kowalewski","year":"2022","journal-title":"Asian J. Urol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"869","DOI":"10.3389\/fneur.2019.00869","article-title":"Applications of Deep Learning to Neuro-Imaging Techniques","volume":"10","author":"Zhu","year":"2019","journal-title":"Front. Neurol"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.ejmp.2021.03.003","article-title":"Current applications of deep-learning in neuro-oncological MRI","volume":"83","author":"Zegers","year":"2021","journal-title":"Phys. Med."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"D\u00edaz-Pernas, F.J., Mart\u00ednez-Zarzuela, M., Ant\u00f3n-Rodr\u00edguez, M., and Gonz\u00e1lez-Ortega, D. (2021). A Deep Learning Approach for Brain Tumor Classification and Segmentation Using a Multiscale Convolutional Neural Network. Healthcare, 9.","DOI":"10.3390\/healthcare9020153"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1296","DOI":"10.1002\/jemt.23688","article-title":"Brain tumor detection and multi-classification using advanced deep learning techniques","volume":"84","author":"Sadad","year":"2021","journal-title":"Microsc. Res. Tech."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"594580","DOI":"10.3389\/fonc.2020.594580","article-title":"Artificial Intelligence in Medical Imaging and Its Application in Sonography for the Management of Liver Tumor","volume":"10","author":"Nishida","year":"2020","journal-title":"Front. Oncol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"6825","DOI":"10.3748\/wjg.v27.i40.6825","article-title":"Emerging artificial intelligence applications in liver magnetic resonance imaging","volume":"27","author":"Hill","year":"2021","journal-title":"World J. Gastroenterol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"100443","DOI":"10.1016\/j.jhepr.2022.100443","article-title":"Artificial intelligence in liver diseases: Improving diagnostics, prognostics and response prediction","volume":"4","author":"Nam","year":"2022","journal-title":"JHEP Rep."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Taha, A., Ochs, V., Kayhan, L.N., Enodien, B., Frey, D.M., Kr\u00e4henb\u00fchl, L., and Taha-Mehlitz, S. (2022). Advancements of Artificial Intelligence in Liver-Associated Diseases and Surgery. Medicina, 58.","DOI":"10.3390\/medicina58040459"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"23S","DOI":"10.1177\/2192568220961353","article-title":"Applications of Machine Learning to Imaging of Spinal Disorders: Current Status and Future Directions","volume":"11","author":"Merali","year":"2021","journal-title":"Glob. Spine J."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"n71","DOI":"10.1136\/bmj.n71","article-title":"The PRISMA 2020 statement: An updated guideline for reporting systematic reviews","volume":"372","author":"Page","year":"2021","journal-title":"Bmj"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"b2700","DOI":"10.1136\/bmj.b2700","article-title":"The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: Explanation and elaboration","volume":"339","author":"Liberati","year":"2009","journal-title":"Bmj"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1347","DOI":"10.1056\/NEJMra1814259","article-title":"Machine Learning in Medicine","volume":"380","author":"Rajkomar","year":"2019","journal-title":"N. Engl. J. Med."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1016\/j.jacr.2017.12.026","article-title":"Artificial Intelligence and Machine Learning in Radiology: Opportunities, Challenges, Pitfalls, and Criteria for Success","volume":"15","author":"Thrall","year":"2018","journal-title":"J. Am. Coll Radiol."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"500","DOI":"10.1038\/s41568-018-0016-5","article-title":"Artificial intelligence in radiology","volume":"18","author":"Hosny","year":"2018","journal-title":"Nat. Rev. Cancer"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"40","DOI":"10.35330\/1991-6639-2020-2-94-40-47","article-title":"Architecture of CAD for distributed artificial intelligence based on self-organizing neuro-cognitive architectures","volume":"2","author":"Nagoev","year":"2020","journal-title":"News Kabard.\u2013Balkar Sci. Cent. RAS"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1146\/annurev-vision-082114-035447","article-title":"Deep Neural Networks: A New Framework for Modeling Biological Vision and Brain Information Processing","volume":"1","author":"Kriegeskorte","year":"2015","journal-title":"Annu. Rev. Vis. Sci."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1148\/rg.2017160130","article-title":"Machine Learning for Medical Imaging","volume":"37","author":"Erickson","year":"2017","journal-title":"Radiographics"},{"key":"ref_43","first-page":"1","article-title":"Introduction to Semi-Supervised Learning","volume":"3","author":"Zhu","year":"2009","journal-title":"Synth. Lect. Artif. Intell. Mach. Learn."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1186\/s12874-019-0681-4","article-title":"Machine learning in medicine: A practical introduction","volume":"19","year":"2019","journal-title":"BMC Med. Res. Methodol."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Cao, B., Araujo, A., and Sim, J. (2020, January 23\u201328). Unifying Deep Local and Global Features for Image Search. Proceedings of the Computer Vision\u2013ECCV 2020, Glasgow, UK.","DOI":"10.1007\/978-3-030-58565-5_43"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1561\/2200000006","article-title":"Learning Deep Architectures for AI","volume":"2","author":"Bengio","year":"2009","journal-title":"Found. Trends Mach. Learn."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1186\/s13244-019-0832-5","article-title":"Deep learning workflow in radiology: A primer","volume":"11","author":"Montagnon","year":"2020","journal-title":"Insights Imaging"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1016\/j.radonc.2018.06.025","article-title":"Unsupervised machine learning of radiomic features for predicting treatment response and overall survival of early stage non-small cell lung cancer patients treated with stereotactic body radiation therapy","volume":"129","author":"Li","year":"2018","journal-title":"Radiother. Oncol."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"101618","DOI":"10.1016\/j.media.2019.101618","article-title":"Regularized siamese neural network for unsupervised outlier detection on brain multiparametric magnetic resonance imaging: Application to epilepsy lesion screening","volume":"60","author":"Alaverdyan","year":"2020","journal-title":"Med. Image Anal."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Tlusty, T., Amit, G., and Ben-Ari, R. (2018, January 20\u201324). Unsupervised clustering of mammograms for outlier detection and breast density estimation. Proceedings of the 2018 24th International Conference on Pattern Recognition (ICPR), Beijing, China.","DOI":"10.1109\/ICPR.2018.8545588"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1776","DOI":"10.3174\/ajnr.A5543","article-title":"Deep Learning in Neuroradiology","volume":"39","author":"Zaharchuk","year":"2018","journal-title":"AJNR Am. J. Neuroradiol."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1177\/0846537120954293","article-title":"Artificial intelligence and deep learning in neuroradiology: Exploring the new frontier","volume":"72","author":"Kaka","year":"2021","journal-title":"Can. Assoc. Radiol. J."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1427","DOI":"10.1148\/rg.2021200210","article-title":"Deep Learning: An Update for Radiologists","volume":"41","author":"Cheng","year":"2021","journal-title":"RadioGraphics"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"16","DOI":"10.2174\/1573405615666190206153321","article-title":"Overview of Computer Aided Detection and Computer Aided Diagnosis Systems for Lung Nodule Detection in Computed Tomography","volume":"16","author":"Ziyad","year":"2020","journal-title":"Curr. Med. Imaging Rev."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1186\/s13244-020-00887-2","article-title":"Radiomics in medical imaging\u2014\u201chow-to\u201d guide and critical reflection","volume":"11","author":"Cester","year":"2020","journal-title":"Insights Imaging"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Faiella, E., Santucci, D., Calabrese, A., Russo, F., Vadala, G., Zobel, B.B., Soda, P., Iannello, G., de Felice, C., and Denaro, V. (2022). Artificial Intelligence in Bone Metastases: An MRI and CT Imaging Review. Int. J. Environ. Res. Public Health, 19.","DOI":"10.3390\/ijerph19031880"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"338","DOI":"10.1097\/RLI.0000000000000448","article-title":"Texture Analysis and Machine Learning for Detecting Myocardial Infarction in Noncontrast Low-Dose Computed Tomography: Unveiling the Invisible","volume":"53","author":"Mannil","year":"2018","journal-title":"Investig. Radiol."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1636","DOI":"10.1001\/jamaoncol.2016.2631","article-title":"The Potential of Radiomic-Based Phenotyping in Precision Medicine: A Review","volume":"2","author":"Aerts","year":"2016","journal-title":"JAMA Oncol."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1016\/j.ejca.2011.11.036","article-title":"Radiomics: Extracting more information from medical images using advanced feature analysis","volume":"48","author":"Lambin","year":"2012","journal-title":"Eur. J. Cancer"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"2023","DOI":"10.1002\/mp.14045","article-title":"Physical imaging phantoms for simulation of tumor heterogeneity in PET, CT, and MRI: An overview of existing designs","volume":"47","author":"Valladares","year":"2020","journal-title":"Med. Phys."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"46349","DOI":"10.1038\/srep46349","article-title":"Radiomics-based Prognosis Analysis for Non-Small Cell Lung Cancer","volume":"7","author":"Zhang","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"4742","DOI":"10.1007\/s00330-019-06024-y","article-title":"Identifying EGFR mutations in lung adenocarcinoma by noninvasive imaging using radiomics features and random forest modeling","volume":"29","author":"Jia","year":"2019","journal-title":"Eur. Radiol."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"20190948","DOI":"10.1259\/bjr.20190948","article-title":"Radiomics: From qualitative to quantitative imaging","volume":"93","author":"Rogers","year":"2020","journal-title":"Br. J. Radiol."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1186\/s41747-018-0068-z","article-title":"Radiomics: The facts and the challenges of image analysis","volume":"2","author":"Rizzo","year":"2018","journal-title":"Eur. Radiol. Exp."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1038\/nrclinonc.2017.141","article-title":"Radiomics: The bridge between medical imaging and personalized medicine","volume":"14","author":"Lambin","year":"2017","journal-title":"Nat. Rev. Clin. Oncol."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"208","DOI":"10.3174\/ajnr.A5391","article-title":"Radiomics in Brain Tumor: Image Assessment, Quantitative Feature Descriptors, and Machine-Learning Approaches","volume":"39","author":"Zhou","year":"2018","journal-title":"AJNR Am. J. Neuroradiol."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"1151","DOI":"10.3389\/fonc.2020.01151","article-title":"Comparison of Radiomics-Based Machine-Learning Classifiers in Diagnosis of Glioblastoma From Primary Central Nervous System Lymphoma","volume":"10","author":"Chen","year":"2020","journal-title":"Front. Oncol."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"5437","DOI":"10.21873\/anticanres.12875","article-title":"Prediction of Response to Stereotactic Radiosurgery for Brain Metastases Using Convolutional Neural Networks","volume":"38","author":"Cha","year":"2018","journal-title":"Anticancer Res."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.neucom.2015.09.116","article-title":"Deep learning for visual understanding: A review","volume":"187","author":"Guo","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_71","unstructured":"Ciresan, D.C., Meier, U., Masci, J., Gambardella, L.M., and Schmidhuber, J. (2011, January 16\u201322). Flexible, high performance convolutional neural networks for image classification. Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, Barcelona, Spain."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.ejmp.2021.03.009","article-title":"Artificial intelligence: Deep learning in oncological radiomics and challenges of interpretability and data harmonization","volume":"83","author":"Papadimitroulas","year":"2021","journal-title":"Phys. Med."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1007\/s11604-018-0795-3","article-title":"Technical and clinical overview of deep learning in radiology","volume":"37","author":"Ueda","year":"2019","journal-title":"Jpn. J. Radiol."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1186\/s41199-020-00053-7","article-title":"Applications of radiomics in precision diagnosis, prognostication and treatment planning of head and neck squamous cell carcinomas","volume":"5","author":"Haider","year":"2020","journal-title":"Cancers Head Neck"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.compbiomed.2017.03.024","article-title":"A multi-resolution approach for spinal metastasis detection using deep Siamese neural networks","volume":"84","author":"Wang","year":"2017","journal-title":"Comput. Biol. Med."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"601699","DOI":"10.3389\/fonc.2021.601699","article-title":"Differentiating Between Multiple Myeloma and Metastasis Subtypes of Lumbar Vertebra Lesions Using Machine Learning-Based Radiomics","volume":"11","author":"Xiong","year":"2021","journal-title":"Front. Oncol."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1007\/s11547-018-0935-y","article-title":"Identification of the most significant magnetic resonance imaging (MRI) radiomic features in oncological patients with vertebral bone marrow metastatic disease: A feasibility study","volume":"124","author":"Filograna","year":"2019","journal-title":"Radiol. Med."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Zhong, X., Li, L., Jiang, H., Yin, J., Lu, B., Han, W., Li, J., and Zhang, J. (2020). Cervical spine osteoradionecrosis or bone metastasis after radiotherapy for nasopharyngeal carcinoma? The MRI-based radiomics for characterization. BMC Med. Imaging, 20.","DOI":"10.1186\/s12880-020-00502-2"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"109586","DOI":"10.1016\/j.ejrad.2021.109586","article-title":"Radiomic Machine Learning Classifiers in Spine Bone Tumors: A Multi-Software, Multi-Scanner Study","volume":"137","author":"Chianca","year":"2021","journal-title":"Eur. J. Radiol."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"572","DOI":"10.1007\/s00330-021-08150-y","article-title":"Vertebral MRI-based radiomics model to differentiate multiple myeloma from metastases: Influence of features number on logistic regression model performance","volume":"32","author":"Liu","year":"2022","journal-title":"Eur. Radiol."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"605746","DOI":"10.3389\/fmed.2020.605746","article-title":"Texture Analysis of (18)F-FDG PET\/CT for Differential Diagnosis Spinal Metastases","volume":"7","author":"Fan","year":"2020","journal-title":"Front. Med. (Lausanne)"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"9866","DOI":"10.1038\/s41598-022-13379-8","article-title":"Radiomics-based machine learning models to distinguish between metastatic and healthy bone using lesion-center-based geometric regions of interest","volume":"12","author":"Naseri","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"792581","DOI":"10.3389\/fmed.2021.792581","article-title":"Single-Photon Emission Computed Tomography\/Computed Tomography Image-Based Radiomics for Discriminating Vertebral Bone Metastases From Benign Bone Lesions in Patients With Tumors","volume":"8","author":"Jin","year":"2021","journal-title":"Front. Med. (Lausanne)"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"E347","DOI":"10.1097\/BRS.0000000000004307","article-title":"Automated Differentiation Between Osteoporotic Vertebral Fracture and Malignant Vertebral Fracture on MRI Using a Deep Convolutional Neural Network","volume":"47","author":"Yoda","year":"2022","journal-title":"Spine (Phila Pa 1976)"},{"key":"ref_85","first-page":"2779390","article-title":"Deep Learning-Based Identification of Spinal Metastasis in Lung Cancer Using Spectral CT Images","volume":"2021","author":"Fan","year":"2021","journal-title":"Sci. Program."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"024504","DOI":"10.1117\/1.JMI.4.2.024504","article-title":"Mixed spine metastasis detection through positron emission tomography\/computed tomography synthesis and multiclassifier","volume":"4","author":"Yao","year":"2017","journal-title":"J. Med. Imaging (Bellingham)"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"903","DOI":"10.1007\/s11548-019-01933-1","article-title":"Random forest classifiers aid in the detection of incidental osteoblastic osseous metastases in DEXA studies","volume":"14","author":"Mehta","year":"2019","journal-title":"Int. J. Comput. Assist Radiol. Surg."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1007\/s00256-021-03873-x","article-title":"Automated detection and segmentation of sclerotic spinal lesions on body CTs using a deep convolutional neural network","volume":"51","author":"Chang","year":"2022","journal-title":"Skelet. Radiol."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/978-3-319-14148-0_1","article-title":"Detection of Sclerotic Spine Metastases via Random Aggregation of Deep Convolutional Neural Network Classifications","volume":"20","author":"Roth","year":"2014","journal-title":"Lect. Notes Comput. Vis. Biomech."},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Wiese, T., Yao, J., Burns, J.E., and Summers, R.M. (2012, January 4\u20139). Detection of sclerotic bone metastases in the spine using watershed algorithm and graph cut. Proceedings of the Medical Imaging 2012: Computer-Aided Diagnosis, San Diego, CA, USA.","DOI":"10.1117\/12.911700"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1148\/radiol.13121351","article-title":"Automated detection of sclerotic metastases in the thoracolumbar spine at CT","volume":"268","author":"Burns","year":"2013","journal-title":"Radiology"},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"1862","DOI":"10.1007\/s00330-013-2774-5","article-title":"Automatic detection of lytic and blastic thoracolumbar spine metastases on computed tomography","volume":"23","author":"Hammon","year":"2013","journal-title":"Eur. Radiol."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"811","DOI":"10.1148\/radiol.2423060260","article-title":"Lytic metastases in thoracolumbar spine: Computer-aided detection at CT\u2013preliminary study","volume":"242","author":"Yao","year":"2007","journal-title":"Radiology"},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"Hallinan, J.T.P.D., Zhu, L., Zhang, W., Lim, D.S.W., Baskar, S., Low, X.Z., Yeong, K.Y., Teo, E.C., Kumarakulasinghe, N.B., and Yap, Q.V. (2022). Deep Learning Model for Classifying Metastatic Epidural Spinal Cord Compression on MRI. Front. Oncol., 12.","DOI":"10.3389\/fonc.2022.849447"},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.phro.2022.02.003","article-title":"Clinical utility of convolutional neural networks for treatment planning in radiotherapy for spinal metastases","volume":"21","author":"Arends","year":"2022","journal-title":"Phys. Imaging Radiat. Oncol."},{"key":"ref_96","unstructured":"Hille, G., Steffen, J., D\u00fcnnwald, M., Becker, M., Saalfeld, S., and T\u00f6nnies, K. (2020). Spinal Metastases Segmentation in MR Imaging using Deep Convolutional Neural Networks. arXiv."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1016\/j.mri.2019.02.013","article-title":"Differentiation of spinal metastases originated from lung and other cancers using radiomics and deep learning based on DCE-MRI","volume":"64","author":"Lang","year":"2019","journal-title":"Magn. Reson. Imaging"},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"12908","DOI":"10.1038\/s41598-021-92363-0","article-title":"A predictive model for pain response following radiotherapy for treatment of spinal metastases","volume":"11","author":"Wakabayashi","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"294","DOI":"10.3171\/2021.3.SPINE201534","article-title":"Radiomic modeling to predict risk of vertebral compression fracture after stereotactic body radiation therapy for spinal metastases","volume":"36","author":"Gui","year":"2022","journal-title":"J. Neurosurg. Spine"},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"752","DOI":"10.1002\/jmri.26238","article-title":"A Triple-Classification Radiomics Model for the Differentiation of Primary Chordoma, Giant Cell Tumor, and Metastatic Tumor of Sacrum Based on T2-Weighted and Contrast-Enhanced T1-Weighted MRI","volume":"49","author":"Yin","year":"2019","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.mri.2022.05.012","article-title":"Radiomics analysis based on multiple parameters MR imaging in the spine: Predicting treatment response of osteolytic bone metastases to chemotherapy in breast cancer patients","volume":"92","author":"Shi","year":"2022","journal-title":"Magn. Reson. Imaging"},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"5142","DOI":"10.1002\/mp.15137","article-title":"MRI-based radiomics analysis for predicting the EGFR mutation based on thoracic spinal metastases in lung adenocarcinoma patients","volume":"48","author":"Ren","year":"2021","journal-title":"Med. Phys."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"215008","DOI":"10.1088\/1361-6560\/ac2ea7","article-title":"Subregional radiomics analysis for the detection of the EGFR mutation on thoracic spinal metastases from lung cancer","volume":"66","author":"Fan","year":"2021","journal-title":"Phys. Med. Biol."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"e9","DOI":"10.1016\/j.acra.2021.06.004","article-title":"MRI-Based Radiomics Nomogram as a Potential Biomarker to Predict the EGFR Mutations in Exon 19 and 21 Based on Thoracic Spinal Metastases in Lung Adenocarcinoma","volume":"29","author":"Cao","year":"2022","journal-title":"Acad. Radiol."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1111\/os.12334","article-title":"Spinal Metastatic Disease: A Review of the Role of the Multidisciplinary Team","volume":"9","author":"Curtin","year":"2017","journal-title":"Orthop. Surg."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1097\/00007632-200102010-00016","article-title":"Surgical strategy for spinal metastases","volume":"26","author":"Tomita","year":"2001","journal-title":"Spine (Phila Pa 1976)"},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"259","DOI":"10.3747\/co.19.1011","article-title":"Bone-targeted agents and skeletal-related events in breast cancer patients with bone metastases: The state of the art","volume":"19","author":"Clemons","year":"2012","journal-title":"Curr. Oncol."},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"2942","DOI":"10.1200\/JCO.2004.08.181","article-title":"Bone imaging in metastatic breast cancer","volume":"22","author":"Hamaoka","year":"2004","journal-title":"J. Clin. Oncol."},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1634\/theoncologist.4-6-459","article-title":"The diagnosis and treatment of metastatic spinal tumor","volume":"4","author":"Bilsky","year":"1999","journal-title":"Oncologist"},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"850","DOI":"10.1097\/WCO.0000000000000761","article-title":"Applications of artificial intelligence in neuro-oncology","volume":"32","author":"Aneja","year":"2019","journal-title":"Curr. Opin. Neurol"},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1016\/j.nic.2020.07.003","article-title":"Diverse Applications of Artificial Intelligence in Neuroradiology","volume":"30","author":"Duong","year":"2020","journal-title":"Neuroimaging Clin. N. Am."},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1016\/j.nic.2020.07.004","article-title":"Brief History of Artificial Intelligence","volume":"30","author":"Muthukrishnan","year":"2020","journal-title":"Neuroimaging Clin. N. Am."},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1148\/radiol.2021203555","article-title":"AI-based Strategies to Reduce Workload in Breast Cancer Screening with Mammography and Tomosynthesis: A Retrospective Evaluation","volume":"300","year":"2021","journal-title":"Radiology"},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"6243s","DOI":"10.1158\/1078-0432.CCR-06-0931","article-title":"Clinical features of metastatic bone disease and risk of skeletal morbidity","volume":"12","author":"Coleman","year":"2006","journal-title":"Clin. Cancer Res."},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"2028","DOI":"10.1200\/JCO.2005.00.067","article-title":"Systematic Review of the Diagnosis and Management of Malignant Extradural Spinal Cord Compression: The Cancer Care Ontario Practice Guidelines Initiative\u2018s Neuro-Oncology Disease Site Group","volume":"23","author":"Perry","year":"2005","journal-title":"J. Clin. Oncol."},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"744","DOI":"10.1177\/2192568217697691","article-title":"Spinal Instability Neoplastic Score (SINS): Reliability Among Spine Fellows and Resident Physicians in Orthopedic Surgery and Neurosurgery","volume":"7","author":"Fox","year":"2017","journal-title":"Glob. Spine J."},{"key":"ref_117","doi-asserted-by":"crossref","unstructured":"Boon, I.S., Au Yong, T.P.T., and Boon, C.S. (2018). Assessing the Role of Artificial Intelligence (AI) in Clinical Oncology: Utility of Machine Learning in Radiotherapy Target Volume Delineation. Medicines, 5.","DOI":"10.3390\/medicines5040131"},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"9128527","DOI":"10.1155\/2018\/9128527","article-title":"Tumor Segmentation in Contrast-Enhanced Magnetic Resonance Imaging for Nasopharyngeal Carcinoma: Deep Learning with Convolutional Neural Network","volume":"2018","author":"Li","year":"2018","journal-title":"BioMed Res. Int."},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.radonc.2019.10.019","article-title":"Comparing deep learning-based auto-segmentation of organs at risk and clinical target volumes to expert inter-observer variability in radiotherapy planning","volume":"144","author":"Wong","year":"2020","journal-title":"Radiother. Oncol."},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"272","DOI":"10.1002\/acm2.13097","article-title":"Evaluation of deep learning-based auto-segmentation algorithms for delineating clinical target volume and organs at risk involving data for 125 cervical cancer patients","volume":"21","author":"Wang","year":"2020","journal-title":"J. Appl. Clin. Med. Phys."},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"6377","DOI":"10.1002\/mp.12602","article-title":"Automatic segmentation of the clinical target volume and organs at risk in the planning CT for rectal cancer using deep dilated convolutional neural networks","volume":"44","author":"Men","year":"2017","journal-title":"Med. Phys."},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"e929","DOI":"10.1002\/mp.14320","article-title":"Auto-segmentation of organs at risk for head and neck radiotherapy planning: From atlas-based to deep learning methods","volume":"47","author":"Vrtovec","year":"2020","journal-title":"Med. Phys."},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"108918","DOI":"10.1016\/j.ejrad.2020.108918","article-title":"Validation of a fully automated liver segmentation algorithm using multi-scale deep reinforcement learning and comparison versus manual segmentation","volume":"126","author":"Winkel","year":"2020","journal-title":"Eur. J. Radiol."},{"key":"ref_124","doi-asserted-by":"crossref","unstructured":"Cardobi, N., Dal Pal\u00f9, A., Pedrini, F., Bele\u00f9, A., Nocini, R., De Robertis, R., Ruzzenente, A., Salvia, R., Montemezzi, S., and D\u2019Onofrio, M. (2021). An Overview of Artificial Intelligence Applications in Liver and Pancreatic Imaging. Cancers, 13.","DOI":"10.3390\/cancers13092162"},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"555","DOI":"10.4111\/icu.20200086","article-title":"Expert-level segmentation using deep learning for volumetry of polycystic kidney and liver","volume":"61","author":"Shin","year":"2020","journal-title":"Investig. Clin. Urol."},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"1475","DOI":"10.1016\/j.jacr.2020.06.033","article-title":"Three-Dimensional Neural Network to Automatically Assess Liver Tumor Burden Change on Consecutive Liver MRIs","volume":"17","author":"Goehler","year":"2020","journal-title":"J. Am. Coll. Radiol."},{"key":"ref_127","doi-asserted-by":"crossref","first-page":"2186","DOI":"10.1097\/01.brs.0000180401.06919.a5","article-title":"A Revised Scoring System for Preoperative Evaluation of Metastatic Spine Tumor Prognosis","volume":"30","author":"Tokuhashi","year":"2005","journal-title":"Spine"},{"key":"ref_128","doi-asserted-by":"crossref","first-page":"1110","DOI":"10.1097\/00007632-199011010-00005","article-title":"Scoring System for the Preoperative Evaluation of Metastatic Spine Tumor Prognosis","volume":"15","author":"Tokuhashi","year":"1990","journal-title":"Spine"},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1186\/s12957-018-1439-x","article-title":"Prognostic factors in patients with metastatic spine tumors derived from lung cancer\u2014a novel scoring system for predicting life expectancy","volume":"16","author":"Uei","year":"2018","journal-title":"World J. Surg. Oncol."},{"key":"ref_130","doi-asserted-by":"crossref","first-page":"7754","DOI":"10.1038\/s41598-019-44226-y","article-title":"Metastatic Tumor Burden and Loci as Predictors of First Line Sunitinib Treatment Efficacy in Patients with Renal Cell Carcinoma","volume":"9","author":"Czarnecka","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_131","doi-asserted-by":"crossref","unstructured":"Saxena, S., Jena, B., Gupta, N., Das, S., Sarmah, D., Bhattacharya, P., Nath, T., Paul, S., Fouda, M.M., and Kalra, M. (2022). Role of Artificial Intelligence in Radiogenomics for Cancers in the Era of Precision Medicine. Cancers, 14.","DOI":"10.3390\/cancers14122860"},{"key":"ref_132","doi-asserted-by":"crossref","unstructured":"Fathi Kazerooni, A., Bagley, S.J., Akbari, H., Saxena, S., Bagheri, S., Guo, J., Chawla, S., Nabavizadeh, A., Mohan, S., and Bakas, S. (2021). Applications of Radiomics and Radiogenomics in High-Grade Gliomas in the Era of Precision Medicine. Cancers, 13.","DOI":"10.3390\/cancers13235921"},{"key":"ref_133","doi-asserted-by":"crossref","first-page":"872","DOI":"10.1183\/09031936.00018013","article-title":"EGFR, KRAS, BRAF and ALK gene alterations in lung adenocarcinomas: Patient outcome, interplay with morphology and immunophenotype","volume":"43","author":"Warth","year":"2014","journal-title":"Eur. Respir. J."},{"key":"ref_134","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1186\/s43042-022-00310-z","article-title":"The role of radiogenomics in the diagnosis of breast cancer: A systematic review","volume":"23","author":"Darvish","year":"2022","journal-title":"Egypt. J. Med. Hum. Genet."},{"key":"ref_135","doi-asserted-by":"crossref","first-page":"58","DOI":"10.3174\/ajnr.A4491","article-title":"MR Imaging-Based Analysis of Glioblastoma Multiforme: Estimation of IDH1 Mutation Status","volume":"37","author":"Yamashita","year":"2016","journal-title":"AJNR Am. J. Neuroradiol."},{"key":"ref_136","doi-asserted-by":"crossref","first-page":"1884","DOI":"10.1093\/brain\/awl108","article-title":"Histological growth patterns and genotype in oligodendroglial tumours: Correlation with MRI features","volume":"129","author":"Jenkinson","year":"2006","journal-title":"Brain"},{"key":"ref_137","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1136\/jnnp.2009.178806","article-title":"Relationship between radiological characteristics and combined 1p and 19q deletion in World Health Organization grade III oligodendroglial tumours","volume":"82","author":"Kim","year":"2011","journal-title":"J. Neurol Neurosurg. Psychiatry"},{"key":"ref_138","doi-asserted-by":"crossref","first-page":"678","DOI":"10.3174\/ajnr.A5070","article-title":"Genetically Defined Oligodendroglioma Is Characterized by Indistinct Tumor Borders at MRI","volume":"38","author":"Johnson","year":"2017","journal-title":"AJNR Am. J. Neuroradiol."},{"key":"ref_139","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1007\/s00234-011-0947-y","article-title":"Imaging parameters of high grade gliomas in relation to the MGMT promoter methylation status: The CT, diffusion tensor imaging, and perfusion MR imaging","volume":"54","author":"Moon","year":"2012","journal-title":"Neuroradiology"},{"key":"ref_140","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1007\/s00330-012-2601-4","article-title":"Apparent diffusion coefficient obtained by magnetic resonance imaging as a prognostic marker in glioblastomas: Correlation with MGMT promoter methylation status","volume":"23","author":"Romano","year":"2013","journal-title":"Eur. Radiol."},{"key":"ref_141","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1002\/jmrs.103","article-title":"Using the apparent diffusion coefficient to identifying MGMT promoter methylation status early in glioblastoma: Importance of analytical method","volume":"62","author":"Day","year":"2015","journal-title":"J. Med. Radiat. Sci."},{"key":"ref_142","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1002\/jmri.23838","article-title":"Correlation of apparent diffusion coefficient values measured by diffusion MRI and MGMT promoter methylation semiquantitatively analyzed with MS-MLPA in patients with glioblastoma multiforme","volume":"37","author":"Sunwoo","year":"2013","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_143","doi-asserted-by":"crossref","first-page":"1599","DOI":"10.1016\/j.acra.2020.06.016","article-title":"A Review of Radiomics and Deep Predictive Modeling in Glioma Characterization","volume":"28","author":"Gore","year":"2021","journal-title":"Acad. Radiol."},{"key":"ref_144","doi-asserted-by":"crossref","first-page":"20170930","DOI":"10.1259\/bjr.20170930","article-title":"Quantitative magnetic resonance imaging and radiogenomic biomarkers for glioma characterisation: A systematic review","volume":"91","author":"Seow","year":"2018","journal-title":"Br. J. Radiol."},{"key":"ref_145","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1016\/j.crad.2019.01.028","article-title":"Deep learning can see the unseeable: Predicting molecular markers from MRI of brain gliomas","volume":"74","author":"Korfiatis","year":"2019","journal-title":"Clin. Radiol."},{"key":"ref_146","doi-asserted-by":"crossref","first-page":"732","DOI":"10.1148\/radiol.2018172171","article-title":"Precision Medicine and Radiogenomics in Breast Cancer: New Approaches toward Diagnosis and Treatment","volume":"287","author":"Pinker","year":"2018","journal-title":"Radiology"},{"key":"ref_147","doi-asserted-by":"crossref","first-page":"570465","DOI":"10.3389\/fonc.2020.570465","article-title":"The Era of Radiogenomics in Precision Medicine: An Emerging Approach to Support Diagnosis, Treatment Decisions, and Prognostication in Oncology","volume":"10","author":"Shui","year":"2020","journal-title":"Front. Oncol."},{"key":"ref_148","doi-asserted-by":"crossref","first-page":"94","DOI":"10.3171\/2010.3.SPINE09202","article-title":"Diagnosis and management of metastatic spine disease. A review","volume":"13","author":"Sciubba","year":"2010","journal-title":"J. Neurosurg. Spine"},{"key":"ref_149","doi-asserted-by":"crossref","first-page":"542","DOI":"10.7861\/clinmedicine.14-5-542","article-title":"Metastatic spinal cord compression: A rare but important complication of cancer","volume":"14","author":"Robson","year":"2014","journal-title":"Clin. Med. (Lond.)"},{"key":"ref_150","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1007\/s10195-015-0344-0","article-title":"Bone metastases of unknown origin: Epidemiology and principles of management","volume":"16","author":"Piccioli","year":"2015","journal-title":"J. Orthop. Traumatol."},{"key":"ref_151","doi-asserted-by":"crossref","first-page":"2178","DOI":"10.3174\/ajnr.A3104","article-title":"Characterizing hypervascular and hypovascular metastases and normal bone marrow of the spine using dynamic contrast-enhanced MR imaging","volume":"33","author":"Khadem","year":"2012","journal-title":"AJNR Am. J. Neuroradiol."},{"key":"ref_152","doi-asserted-by":"crossref","first-page":"1285","DOI":"10.1016\/j.mri.2012.10.006","article-title":"Differentiation of myeloma and metastatic cancer in the spine using dynamic contrast-enhanced MRI","volume":"31","author":"Lang","year":"2013","journal-title":"Magn. Reson. Imaging"},{"key":"ref_153","doi-asserted-by":"crossref","first-page":"584","DOI":"10.1007\/s00586-012-2600-9","article-title":"Cervical spine complications after treatment of nasopharyngeal carcinoma","volume":"22","author":"Cheung","year":"2013","journal-title":"Eur. Spine J."},{"key":"ref_154","doi-asserted-by":"crossref","first-page":"20140436","DOI":"10.1259\/bjr.20140436","article-title":"Osteoradionecrosis of the subaxial cervical spine following treatment for head and neck carcinomas","volume":"88","author":"Khorsandi","year":"2015","journal-title":"Br. J. Radiol."},{"key":"ref_155","doi-asserted-by":"crossref","first-page":"629","DOI":"10.1016\/j.ejrad.2008.12.016","article-title":"Osteoradionecrosis of the upper cervical spine: MR imaging following radiotherapy for nasopharyngeal carcinoma","volume":"73","author":"King","year":"2010","journal-title":"Eur. J. Radiol."},{"key":"ref_156","doi-asserted-by":"crossref","first-page":"428","DOI":"10.3171\/2017.2.SPINE16976","article-title":"Reirradiation spine stereotactic body radiation therapy for spinal metastases: Systematic review","volume":"27","author":"Myrehaug","year":"2017","journal-title":"J. Neurosurg. Spine"},{"key":"ref_157","doi-asserted-by":"crossref","first-page":"295","DOI":"10.3171\/2017.1.SPINE16684","article-title":"Stereotactic body radiotherapy for de novo spinal metastases: Systematic review","volume":"27","author":"Husain","year":"2017","journal-title":"J. Neurosurg. Spine"},{"key":"ref_158","doi-asserted-by":"crossref","first-page":"430","DOI":"10.3171\/2013.2.SPINE12739","article-title":"Radiation-induced vertebral compression fracture following spine stereotactic radiosurgery: Clinicopathological correlation","volume":"18","author":"Smith","year":"2013","journal-title":"J. Neurosurg. Spine"},{"key":"ref_159","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1111\/1754-9485.13032","article-title":"Imaging response assessment following stereotactic body radiotherapy for solid tumour metastases of the spine: Current challenges and future directions","volume":"64","author":"McDermott","year":"2020","journal-title":"J. Med. Imaging Radiat. Oncol."},{"key":"ref_160","doi-asserted-by":"crossref","first-page":"387","DOI":"10.3174\/ajnr.A4528","article-title":"Osseous Pseudoprogression in Vertebral Bodies Treated with Stereotactic Radiosurgery: A Secondary Analysis of Prospective Phase I\/II Clinical Trials","volume":"37","author":"Amini","year":"2016","journal-title":"AJNR Am. J. Neuroradiol."},{"key":"ref_161","doi-asserted-by":"crossref","first-page":"848","DOI":"10.1016\/j.ijrobp.2016.07.034","article-title":"A Study of Pseudoprogression After Spine Stereotactic Body Radiation Therapy","volume":"96","author":"Bahig","year":"2016","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"ref_162","doi-asserted-by":"crossref","first-page":"534","DOI":"10.3171\/2014.10.SPINE14444","article-title":"Tumor pseudoprogression of spinal metastasis after radiosurgery: A novel concept and case reports","volume":"22","author":"Taylor","year":"2015","journal-title":"J. Neurosurg. Spine"},{"key":"ref_163","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1093\/neuonc\/noy133","article-title":"Incorporating diffusion- and perfusion-weighted MRI into a radiomics model improves diagnostic performance for pseudoprogression in glioblastoma patients","volume":"21","author":"Kim","year":"2019","journal-title":"Neuro Oncol"},{"key":"ref_164","doi-asserted-by":"crossref","first-page":"12516","DOI":"10.1038\/s41598-018-31007-2","article-title":"Prediction of Pseudoprogression versus Progression using Machine Learning Algorithm in Glioblastoma","volume":"8","author":"Jang","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_165","doi-asserted-by":"crossref","first-page":"e595","DOI":"10.1016\/S1470-2045(15)00166-7","article-title":"Response assessment after stereotactic body radiotherapy for spinal metastasis: A report from the SPIne response assessment in Neuro-Oncology (SPINO) group","volume":"16","author":"Thibault","year":"2015","journal-title":"Lancet Oncol."}],"container-title":["Cancers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-6694\/14\/16\/4025\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:12:51Z","timestamp":1760141571000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-6694\/14\/16\/4025"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,20]]},"references-count":165,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2022,8]]}},"alternative-id":["cancers14164025"],"URL":"https:\/\/doi.org\/10.3390\/cancers14164025","relation":{},"ISSN":["2072-6694"],"issn-type":[{"value":"2072-6694","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,20]]}}}