{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T19:20:08Z","timestamp":1788376808230,"version":"build-2803163510"},"reference-count":223,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2024,8,28]],"date-time":"2024-08-28T00:00:00Z","timestamp":1724803200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"MOH\/NMRC, Singapore","award":["CIAINV23jan-0001"],"award-info":[{"award-number":["CIAINV23jan-0001"]}]},{"name":"MOH\/NMRC, Singapore","award":["MOH-001405"],"award-info":[{"award-number":["MOH-001405"]}]},{"name":"Singapore Ministry of Health National Medical Research Council","award":["CIAINV23jan-0001"],"award-info":[{"award-number":["CIAINV23jan-0001"]}]},{"name":"Singapore Ministry of Health National Medical Research Council","award":["MOH-001405"],"award-info":[{"award-number":["MOH-001405"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Cancers"],"abstract":"<jats:p>In spinal oncology, integrating deep learning with computed tomography (CT) imaging has shown promise in enhancing diagnostic accuracy, treatment planning, and patient outcomes. This systematic review synthesizes evidence on artificial intelligence (AI) applications in CT imaging for spinal tumors. A PRISMA-guided search identified 33 studies: 12 (36.4%) focused on detecting spinal malignancies, 11 (33.3%) on classification, 6 (18.2%) on prognostication, 3 (9.1%) on treatment planning, and 1 (3.0%) on both detection and classification. Of the classification studies, 7 (21.2%) used machine learning to distinguish between benign and malignant lesions, 3 (9.1%) evaluated tumor stage or grade, and 2 (6.1%) employed radiomics for biomarker classification. Prognostic studies included three (9.1%) that predicted complications such as pathological fractures and three (9.1%) that predicted treatment outcomes. AI\u2019s potential for improving workflow efficiency, aiding decision-making, and reducing complications is discussed, along with its limitations in generalizability, interpretability, and clinical integration. Future directions for AI in spinal oncology are also explored. In conclusion, while AI technologies in CT imaging are promising, further research is necessary to validate their clinical effectiveness and optimize their integration into routine practice.<\/jats:p>","DOI":"10.3390\/cancers16172988","type":"journal-article","created":{"date-parts":[[2024,8,28]],"date-time":"2024-08-28T03:57:06Z","timestamp":1724817426000},"page":"2988","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Oncologic Applications of Artificial Intelligence and Deep Learning Methods in CT Spine Imaging\u2014A Systematic Review"],"prefix":"10.3390","volume":"16","author":[{"given":"Wilson","family":"Ong","sequence":"first","affiliation":[{"name":"Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, 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Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4538-5031","authenticated-orcid":false,"given":"Naresh","family":"Kumar","sequence":"additional","affiliation":[{"name":"National University Spine Institute, Department of Orthopaedic Surgery, National University Health System, 1E, Lower Kent Ridge Road, Singapore 119228, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0082-9033","authenticated-orcid":false,"given":"James Thomas Patrick Decourcy","family":"Hallinan","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, 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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":"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_4","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_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":"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_7","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1097\/BRS.0000000000004889","article-title":"A New Deep Learning Algorithm for Detecting Spinal Metastases on Computed Tomography Images","volume":"49","author":"Motohashi","year":"2024","journal-title":"Spine"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Cui, Y., Zhu, J., Duan, Z., Liao, Z., Wang, S., and Liu, W. (2022). Artificial Intelligence in Spinal Imaging: Current Status and Future Directions. Int. J. Environ. Res. Public Health, 19.","DOI":"10.3390\/ijerph191811708"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"110726","DOI":"10.1016\/j.ejrad.2023.110726","article-title":"The role of Artificial intelligence in the assessment of the spine and spinal cord","volume":"161","author":"Amrhein","year":"2023","journal-title":"Eur. J. Radiol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"910","DOI":"10.31616\/asj.2020.0593","article-title":"Diagnostic Modality in Spine Disease: A Review","volume":"14","author":"Kim","year":"2020","journal-title":"Asian Spine J."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Ariyaratne, S., Jenko, N., Iyengar, K.P., James, S., Mehta, J., and Botchu, R. (2023). Primary Osseous Malignancies of the Spine. Diagnostics, 13.","DOI":"10.3390\/diagnostics13101801"},{"key":"ref_12","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_13","doi-asserted-by":"crossref","first-page":"e486","DOI":"10.1016\/S2589-7500(20)30160-6","article-title":"Artificial intelligence in medical imaging: Switching from radiographic pathological data to clinically meaningful endpoints","volume":"2","author":"Oren","year":"2020","journal-title":"Lancet Digit. Health"},{"key":"ref_14","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_15","doi-asserted-by":"crossref","first-page":"933","DOI":"10.1016\/j.media.2012.02.005","article-title":"Machine learning and radiology","volume":"16","author":"Wang","year":"2012","journal-title":"Med. Image Anal."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Najjar, R. (2023). Redefining Radiology: A Review of Artificial Intelligence Integration in Medical Imaging. Diagnostics, 13.","DOI":"10.20944\/preprints202306.1124.v1"},{"key":"ref_17","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_18","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":"Am. J. Neuroradiol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1007\/s13244-018-0639-9","article-title":"Convolutional neural networks: An overview and application in radiology","volume":"9","author":"Yamashita","year":"2018","journal-title":"Insights Imaging"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1007\/s12599-022-00750-2","article-title":"Radiologists\u2019 Usage of Diagnostic AI Systems","volume":"64","author":"Jussupow","year":"2022","journal-title":"Bus. Inf. Syst. Eng."},{"key":"ref_21","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_22","doi-asserted-by":"crossref","first-page":"110073","DOI":"10.1016\/j.ejrad.2021.110073","article-title":"Convolutional neural network performance compared to radiologists in detecting intracranial hemorrhage from brain computed tomography: A systematic review and meta-analysis","volume":"146","author":"Antulov","year":"2022","journal-title":"Eur. J. Radiol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.clinimag.2021.06.033","article-title":"Understanding artificial intelligence based radiology studies: CNN architecture","volume":"80","author":"Mutasa","year":"2021","journal-title":"Clin. Imaging"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Manakitsa, N., Maraslidis, G.S., Moysis, L., and Fragulis, G.F. (2024). A Review of Machine Learning and Deep Learning for Object Detection, Semantic Segmentation, and Human Action Recognition in Machine and Robotic Vision. Technologies, 12.","DOI":"10.3390\/technologies12020015"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1186\/s13244-020-00887-2","article-title":"Radiomics in medical imaging\u2014\u201dHow-to\u201d guide and critical reflection","volume":"11","author":"Cester","year":"2020","journal-title":"Insights Imaging"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1717","DOI":"10.1148\/rg.2021210037","article-title":"Radiomics in Oncology: A Practical Guide","volume":"41","author":"Shur","year":"2021","journal-title":"RadioGraphics"},{"key":"ref_27","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_28","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_29","doi-asserted-by":"crossref","first-page":"732","DOI":"10.1038\/s41597-023-02641-x","article-title":"Enhancing radiomics and Deep Learning systems through the standardization of medical imaging workflows","volume":"10","author":"Cobo","year":"2023","journal-title":"Sci. Data"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1053\/j.sult.2022.02.003","article-title":"Radiomics: A Primer on Processing Workflow and Analysis","volume":"43","author":"Avery","year":"2022","journal-title":"Semin. Ultrasound CT MR"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"3","DOI":"10.4103\/0971-6203.58777","article-title":"Automated medical image segmentation techniques","volume":"35","author":"Sharma","year":"2010","journal-title":"J. Med. Phys."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"26731","DOI":"10.1007\/s11042-022-14305-w","article-title":"Machine learning and deep learning approach for medical image analysis: Diagnosis to detection","volume":"82","author":"Rana","year":"2023","journal-title":"Multimed. Tools Appl."},{"key":"ref_33","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_34","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_35","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_36","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_37","doi-asserted-by":"crossref","first-page":"e230375","DOI":"10.1148\/ryai.230375","article-title":"Performance of an Artificial Intelligence System for Breast Cancer Detection on Screening Mammograms from BreastScreen Norway","volume":"6","author":"Larsen","year":"2024","journal-title":"Radiol. Artif. Intell."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"3303","DOI":"10.21037\/jtd.2020.03.105","article-title":"Do we need to see to believe?\u2014Radiomics for lung nodule classification and lung cancer risk stratification","volume":"12","author":"Khawaja","year":"2020","journal-title":"J. Thorac. Dis."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Astaraki, M., Yang, G., Zakko, Y., Toma-Dasu, I., Smedby, \u00d6., and Wang, C. (2021). A Comparative Study of Radiomics and Deep-Learning Based Methods for Pulmonary Nodule Malignancy Prediction in Low Dose CT Images. Front. Oncol., 11.","DOI":"10.3389\/fonc.2021.737368"},{"key":"ref_40","first-page":"5518717","article-title":"Handcrafted and Deep Learning-Based Radiomic Models Can Distinguish GBM from Brain Metastasis","volume":"2021","author":"Liu","year":"2021","journal-title":"J. Oncol."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Salmanpour, M.R., Rezaeijo, S.M., Hosseinzadeh, M., and Rahmim, A. (2023). Deep versus Handcrafted Tensor Radiomics Features: Prediction of Survival in Head and Neck Cancer Using Machine Learning and Fusion Techniques. Diagnostics, 13.","DOI":"10.3390\/diagnostics13101696"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Huang, Y., Li, J., Li, M., and Aparasu, R.R. (2023). Application of machine learning in predicting survival outcomes involving real-world data: A scoping review. BMC Med. Res. Methodol., 23.","DOI":"10.1186\/s12874-023-02078-1"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"e33440","DOI":"10.2196\/33440","article-title":"The Application and Comparison of Machine Learning Models for the Prediction of Breast Cancer Prognosis: Retrospective Cohort Study","volume":"10","author":"Xiao","year":"2022","journal-title":"JMIR Med. Inform."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1016\/j.compbiomed.2019.02.017","article-title":"Radiological images and machine learning: Trends, perspectives, and prospects","volume":"108","author":"Zhang","year":"2019","journal-title":"Comput. Biol. Med."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1186\/s40537-021-00444-8","article-title":"Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions","volume":"8","author":"Alzubaidi","year":"2021","journal-title":"J. Big Data"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"24365","DOI":"10.1007\/s11042-021-10707-4","article-title":"Medical image analysis based on deep learning approach","volume":"80","author":"Puttagunta","year":"2021","journal-title":"Multimed. Tools Appl."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"e21","DOI":"10.1017\/cts.2020.513","article-title":"Lessons and tips for designing a machine learning study using EHR data","volume":"5","author":"Arbet","year":"2020","journal-title":"J. Clin. Transl. Sci."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1330","DOI":"10.1038\/s41551-022-00898-y","article-title":"Shifting machine learning for healthcare from development to deployment and from models to data","volume":"6","author":"Zhang","year":"2022","journal-title":"Nat. Biomed. Eng."},{"key":"ref_49","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_50","doi-asserted-by":"crossref","first-page":"1191","DOI":"10.1093\/annonc\/mdx034","article-title":"Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology","volume":"28","author":"Limkin","year":"2017","journal-title":"Ann. Oncol."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1186\/s13014-022-02192-2","article-title":"A review of radiomics and genomics applications in cancers: The way towards precision medicine","volume":"17","author":"Li","year":"2022","journal-title":"Radiat. Oncol."},{"key":"ref_52","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_53","doi-asserted-by":"crossref","unstructured":"Pinto-Coelho, L. (2023). How Artificial Intelligence Is Shaping Medical Imaging Technology: A Survey of Innovations and Applications. Bioengineering, 10.","DOI":"10.20944\/preprints202311.1366.v1"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1177\/08465371221135760","article-title":"Assessment of Radiology Artificial Intelligence Software: A Validation and Evaluation Framework","volume":"74","author":"Tanguay","year":"2023","journal-title":"Can. Assoc. Radiol. J."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"38","DOI":"10.2214\/AJR.18.20224","article-title":"Peering into the Black Box of Artificial Intelligence: Evaluation Metrics of Machine Learning Methods","volume":"212","author":"Handelman","year":"2019","journal-title":"Am. J. Roentgenol."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1038\/s41586-019-1799-6","article-title":"International evaluation of an AI system for breast cancer screening","volume":"577","author":"McKinney","year":"2020","journal-title":"Nature"},{"key":"ref_57","first-page":"627","article-title":"Receiver Operating Characteristic (ROC) Curve Analysis for Medical Diagnostic Test Evaluation","volume":"4","year":"2013","journal-title":"Casp. J. Intern. Med."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1148\/radiology.143.1.7063747","article-title":"The meaning and use of the area under a receiver operating characteristic (ROC) curve","volume":"143","author":"Hanley","year":"1982","journal-title":"Radiology"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1148\/radiol.2291010898","article-title":"Receiver operating characteristic curves and their use in radiology","volume":"229","author":"Obuchowski","year":"2003","journal-title":"Radiology"},{"key":"ref_60","first-page":"37","article-title":"Evaluation: From Precision, Recall and F-Factor to ROC, Informedness, Markedness & Correlation","volume":"2","author":"Powers","year":"2011","journal-title":"Int. J. Mach. Learn. Technol."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1016\/j.ipm.2009.03.002","article-title":"A systematic analysis of performance measures for classification tasks","volume":"45","author":"Sokolova","year":"2009","journal-title":"Inf. Process. Manag."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1049","DOI":"10.1007\/s10822-014-9759-6","article-title":"Count on kappa","volume":"28","author":"Czodrowski","year":"2014","journal-title":"J. Comput.-Aided Mol. Des."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"21300","DOI":"10.1109\/ACCESS.2023.3249759","article-title":"Assessing Inter-Annotator Agreement for Medical Image Segmentation","volume":"11","author":"Yang","year":"2023","journal-title":"IEEE Access"},{"key":"ref_64","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 SPIE Medical Imaging 2012, San Diego, CA, USA.","DOI":"10.1117\/12.911700"},{"key":"ref_65","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_66","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_67","unstructured":"Yao, J., Glocker, B., Klinder, T., and Li, S. (2015). Detection of Sclerotic Spine Metastases via Random Aggregation of Deep Convolutional Neural Network Classifications. Recent Advances in Computational Methods and Clinical Applications for Spine Imaging, Springer International Publishing."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"e17567","DOI":"10.1200\/JCO.2020.38.15_suppl.e17567","article-title":"Artificial intelligence assisted bone lesion detection and classification in computed tomography scans of prostate cancer patients","volume":"38","author":"Masoudi","year":"2020","journal-title":"J. Clin. Oncol."},{"key":"ref_69","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_70","doi-asserted-by":"crossref","first-page":"7976","DOI":"10.1007\/s00330-022-08741-3","article-title":"Deep learning-based algorithm improved radiologists\u2019 performance in bone metastases detection on CT","volume":"32","author":"Noguchi","year":"2022","journal-title":"Eur. Radiol."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"110445","DOI":"10.1016\/j.ejrad.2022.110445","article-title":"Effectiveness of temporal subtraction computed tomography images using deep learning in detecting vertebral bone metastases","volume":"154","author":"Hoshiai","year":"2022","journal-title":"Eur. J. Radiol."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"187","DOI":"10.2174\/1573405617666210412142758","article-title":"Differentiation between Osteoporotic and Neoplastic Vertebral Fractures: State of the Art and Future Perspectives","volume":"18","author":"Zarantonello","year":"2022","journal-title":"Curr. Med. Imaging"},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Gilberg, L., Teodorescu, B., Maerkisch, L., Baumgart, A., Ramaesh, R., Ataide, E., and Ko\u00e7, A. (2023). Deep Learning Enhances Radiologists\u2019 Detection of Potential Spinal Malignancies in CT Scans. Appl. Sci., 13.","DOI":"10.3390\/app13148140"},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Huo, T., Xie, Y., Fang, Y., Wang, Z., Liu, P., Duan, Y., Zhang, J., Wang, H., Xue, M., and Liu, S. (2023). Deep learning-based algorithm improves radiologists\u2019 performance in lung cancer bone metastases detection on computed tomography. Front. Oncol., 13.","DOI":"10.3389\/fonc.2023.1125637"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"1867","DOI":"10.1007\/s11548-023-02880-8","article-title":"Artificial intelligence-aided lytic spinal bone metastasis classification on CT scans","volume":"18","author":"Koike","year":"2023","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.media.2018.07.008","article-title":"Deep convolutional neural network-based segmentation and classification of difficult to define metastatic spinal lesions in 3D CT data","volume":"49","author":"Chmelik","year":"2018","journal-title":"Med. Image Anal."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"9612","DOI":"10.1007\/s00330-021-08014-5","article-title":"Differential diagnosis of benign and malignant vertebral fracture on CT using deep learning","volume":"31","author":"Li","year":"2021","journal-title":"Eur. Radiol."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"87531","DOI":"10.1109\/ACCESS.2021.3074051","article-title":"Deep Learning Based Staging of Bone Lesions From Computed Tomography Scans","volume":"9","author":"Masoudi","year":"2021","journal-title":"IEEE Access"},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Hallinan, J.T., Zhu, L., Zhang, W., Kuah, T., Lim, D.S., Low, X.Z., Cheng, A.J.L., Eide, S.E., Ong, H.Y., and Muhamat Nor, F.E. (2022). Deep Learning Model for Grading Metastatic Epidural Spinal Cord Compression on Staging CT. Cancers, 14.","DOI":"10.3390\/cancers14133219"},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Naseri, H., Skamene, S., Tolba, M., Faye, M.D., Ramia, P., Khriguian, J., Patrick, H., Andrade Hernandez, A.X., David, M., and Kildea, J. (2022). Radiomics-based machine learning models to distinguish between metastatic and healthy bone using lesion-center-based geometric regions of interest. Sci. Rep., 12.","DOI":"10.1038\/s41598-022-13379-8"},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Park, T., Yoon, M.A., Cho, Y.C., Ham, S.J., Ko, Y., Kim, S., Jeong, H., and Lee, J. (2022). Automated segmentation of the fractured vertebrae on CT and its applicability in a radiomics model to predict fracture malignancy. Sci. Rep., 12.","DOI":"10.1038\/s41598-022-10807-7"},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Wang, Q., Chen, Y., Qin, S., Liu, X., Liu, K., Xin, P., Zhao, W., Yuan, H., and Lang, N. (2022). Prognostic Value and Quantitative CT Analysis in RANKL Expression of Spinal GCTB in the Denosumab Era: A Machine Learning Approach. Cancers, 14.","DOI":"10.3390\/cancers14215201"},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Wang, Q., Zhang, Y., Zhang, E., Xing, X., Chen, Y., Nie, K., Yuan, H., Su, M.Y., and Lang, N. (2022). A Multiparametric Method Based on Clinical and CT-Based Radiomics to Predict the Expression of p53 and VEGF in Patients with Spinal Giant Cell Tumor of Bone. Front. Oncol., 12.","DOI":"10.3389\/fonc.2022.894696"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"3815","DOI":"10.1007\/s00586-023-07706-4","article-title":"A deep learning-based technique for the diagnosis of epidural spinal cord compression on thoracolumbar CT","volume":"32","author":"Hallinan","year":"2023","journal-title":"Eur. Spine J."},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Hallinan, J.T.P.D., Zhu, L., Zhang, W., Ge, S., Muhamat Nor, F.E., Ong, H.Y., Eide, S.E., Cheng, A.J.L., Kuah, T., and Lim, D.S.W. (2023). Deep learning assessment compared to radiologist reporting for metastatic spinal cord compression on CT. Front. Oncol., 13.","DOI":"10.3389\/fonc.2023.1151073"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"110899","DOI":"10.1016\/j.ejrad.2023.110899","article-title":"Differential diagnosis of benign and malignant vertebral compression fractures: Comparison and correlation of radiomics and deep learning frameworks based on spinal CT and clinical characteristics","volume":"165","author":"Duan","year":"2023","journal-title":"Eur. J. Radiol."},{"key":"ref_87","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":"2021","journal-title":"J. Neurosurg. Spine"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"100354","DOI":"10.1016\/j.jbo.2021.100354","article-title":"Prediction of the early recurrence in spinal giant cell tumor of bone using radiomics of preoperative CT: Long-term outcome of 62 consecutive patients","volume":"27","author":"Wang","year":"2021","journal-title":"J. Bone Oncol."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"263","DOI":"10.3171\/2022.1.SPINE211284","article-title":"Evaluating frailty, mortality, and complications associated with metastatic spine tumor surgery using machine learning-derived body composition analysis","volume":"37","author":"Massaad","year":"2022","journal-title":"J. Neurosurg. Spine"},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"3221","DOI":"10.1007\/s00586-023-07963-3","article-title":"Predicting vertebral compression fracture prior to spinal SBRT using radiomics from planning CT","volume":"33","author":"Seol","year":"2023","journal-title":"Eur. Spine J."},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Delrieu, L., Blanc, D., Bouhamama, A., Reyal, F., Pilleul, F., Racine, V., Hamy, A.S., Crochet, H., Marchal, T., and Heudel, P.E. (2024). Automatic deep learning method for third lumbar selection and body composition evaluation on CT scans of cancer patients. Front. Nucl. Med., 3.","DOI":"10.3389\/fnume.2023.1292676"},{"key":"ref_92","first-page":"291","article-title":"Machine learning-based detection of sarcopenic obesity and association with adverse outcomes in patients undergoing surgical treatment for spinal metastases","volume":"40","author":"Khalid","year":"2024","journal-title":"J. Neurosurg. Spine"},{"key":"ref_93","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_94","doi-asserted-by":"crossref","first-page":"516","DOI":"10.1016\/j.ijrobp.2022.06.083","article-title":"An Automated Treatment Planning Framework for Spinal Radiation Therapy and Vertebral-Level Second Check","volume":"114","author":"Netherton","year":"2022","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"e30164","DOI":"10.1002\/pbc.30164","article-title":"Automating the treatment planning process for 3D-conformal pediatric craniospinal irradiation therapy","volume":"70","author":"Hernandez","year":"2023","journal-title":"Pediatr. Blood Cancer"},{"key":"ref_96","first-page":"e51440","article-title":"The Importance of Early Detection of Spinal Tumors Through Magnetic Resonance Imaging in Chiropractic Practices","volume":"16","author":"Mok","year":"2024","journal-title":"Cureus"},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"606","DOI":"10.1016\/S1470-2045(09)70088-9","article-title":"Imaging bone metastases in breast cancer: Techniques and recommendations for diagnosis","volume":"10","author":"Costelloe","year":"2009","journal-title":"Lancet Oncol."},{"key":"ref_98","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_99","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"},{"key":"ref_100","first-page":"71","article-title":"The treatment of spinal metastases","volume":"108","author":"Delank","year":"2011","journal-title":"Dtsch. Arztebl. Int."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"757","DOI":"10.1093\/neuros\/nyx567","article-title":"State of the Art Treatment of Spinal Metastatic Disease","volume":"82","author":"Barzilai","year":"2018","journal-title":"Neurosurgery"},{"key":"ref_102","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_103","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_104","doi-asserted-by":"crossref","first-page":"103205","DOI":"10.1016\/j.critrevonc.2020.103205","article-title":"Management of acute spinal cord compression in multiple myeloma","volume":"160","author":"Chen","year":"2021","journal-title":"Crit. Rev. Oncol. Hematol."},{"key":"ref_105","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_106","doi-asserted-by":"crossref","unstructured":"Ong, W., Zhu, L., Zhang, W., Kuah, T., Lim, D.S.W., Low, X.Z., Thian, Y.L., Teo, E.C., Tan, J.H., and Kumar, N. (2022). Application of Artificial Intelligence Methods for Imaging of Spinal Metastasis. Cancers, 14.","DOI":"10.3390\/cancers14164025"},{"key":"ref_107","doi-asserted-by":"crossref","unstructured":"Hallinan, J.T.P.D., Ge, S., Zhu, L., Zhang, W., Lim, Y.T., Thian, Y.L., Jagmohan, P., Kuah, T., Lim, D.S.W., and Low, X.Z. (2022). Diagnostic Accuracy of CT for Metastatic Epidural Spinal Cord Compression. Cancers, 14.","DOI":"10.3390\/cancers14174231"},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"811","DOI":"10.1148\/radiol.2423060260","article-title":"Lytic metastases in thoracolumbar spine: Computer-aided detection at CT\u2014Preliminary study","volume":"242","author":"Yao","year":"2007","journal-title":"Radiology"},{"key":"ref_109","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_110","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":"Skeletal Radiol."},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"P2239","DOI":"10.1016\/j.acra.2023.12.006","article-title":"Artificial Intelligence for Breast Cancer Detection on Mammography: Factors Related to Cancer Detection","volume":"31","author":"Yoen","year":"2024","journal-title":"Acad. Radiol."},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"e703","DOI":"10.1016\/S2589-7500(23)00153-X","article-title":"Artificial intelligence for breast cancer detection in screening mammography in Sweden: A prospective, population-based, paired-reader, non-inferiority study","volume":"5","author":"Dembrower","year":"2023","journal-title":"Lancet Digit. Health"},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"111457","DOI":"10.1016\/j.ejrad.2024.111457","article-title":"Artificial Intelligence for breast cancer detection: Technology, challenges, and prospects","volume":"175","author":"Sechopoulos","year":"2024","journal-title":"Eur. J. Radiol."},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"1241","DOI":"10.3348\/kjr.2022.0263","article-title":"Use of Artificial Intelligence for Reducing Unnecessary Recalls at Screening Mammography: A Simulation Study","volume":"23","author":"Kim","year":"2022","journal-title":"Korean J. Radiol."},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"1808","DOI":"10.1177\/02841851221140556","article-title":"Artificial intelligence assistance for women who had spot compression view: Reducing recall rates for digital mammography","volume":"64","author":"Lee","year":"2023","journal-title":"Acta Radiol."},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"327","DOI":"10.2174\/1573405617666210806125953","article-title":"Lung Nodule Detectability of Artificial Intelligence-assisted CT Image Reading in Lung Cancer Screening","volume":"18","author":"Zhang","year":"2022","journal-title":"Curr. Med. Imaging."},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1038\/s43856-023-00388-5","article-title":"Deep learning for the detection of benign and malignant pulmonary nodules in non-screening chest CT scans","volume":"3","author":"Hendrix","year":"2023","journal-title":"Commun. Med."},{"key":"ref_118","first-page":"1","article-title":"Artificial intelligence for early diagnosis of lung cancer through incidental nodule detection in low- and middle-income countries-acceleration during the COVID-19 pandemic but here to stay","volume":"12","author":"Goncalves","year":"2022","journal-title":"Am. J. Cancer Res."},{"key":"ref_119","doi-asserted-by":"crossref","unstructured":"Twilt, J.J., van Leeuwen, K.G., Huisman, H.J., F\u00fctterer, J.J., and de Rooij, M. (2021). Artificial Intelligence Based Algorithms for Prostate Cancer Classification and Detection on Magnetic Resonance Imaging: A Narrative Review. Diagnostics, 11.","DOI":"10.3390\/diagnostics11060959"},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"17562872221128791","DOI":"10.1177\/17562872221128791","article-title":"A review of artificial intelligence in prostate cancer detection on imaging","volume":"14","author":"Bhattacharya","year":"2022","journal-title":"Ther. Adv. Urol."},{"key":"ref_121","doi-asserted-by":"crossref","unstructured":"Li, H., Lee, C.H., Chia, D., Lin, Z., Huang, W., and Tan, C.H. (2022). Machine Learning in Prostate MRI for Prostate Cancer: Current Status and Future Opportunities. Diagnostics, 12.","DOI":"10.3390\/diagnostics12020289"},{"key":"ref_122","doi-asserted-by":"crossref","unstructured":"Schalekamp, S., van Leeuwen, K., Calli, E., Murphy, K., Rutten, M., Geurts, B., Peters-Bax, L., van Ginneken, B., and Prokop, M. (2024). Performance of AI to exclude normal chest radiographs to reduce radiologists\u2019 workload. Eur. Radiol., 1\u20139.","DOI":"10.1007\/s00330-024-10794-5"},{"key":"ref_123","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_124","doi-asserted-by":"crossref","first-page":"20210406","DOI":"10.1259\/bjr.20210406","article-title":"Artificial intelligence in medical imaging: Implications for patient radiation safety","volume":"94","author":"Seah","year":"2021","journal-title":"Br. J. Radiol."},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"2087","DOI":"10.1007\/s00247-021-05114-8","article-title":"How does artificial intelligence in radiology improve efficiency and health outcomes?","volume":"52","author":"Schalekamp","year":"2022","journal-title":"Pediatr. Radiol."},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"2875","DOI":"10.1111\/jgh.15522","article-title":"Artificial intelligence assists identifying malignant versus benign liver lesions using contrast-enhanced ultrasound","volume":"36","author":"Hu","year":"2021","journal-title":"J. Gastroenterol. Hepatol."},{"key":"ref_127","doi-asserted-by":"crossref","first-page":"100484","DOI":"10.1016\/j.ejro.2023.100484","article-title":"Optimizing MRI-based brain tumor classification and detection using AI: A comparative analysis of neural networks, transfer learning, data augmentation, and the cross-transformer network","volume":"10","author":"Sarasti","year":"2023","journal-title":"Eur. J. Radiol. Open"},{"key":"ref_128","doi-asserted-by":"crossref","unstructured":"Wan, Y.L., Wu, P.W., Huang, P.C., Tsay, P.K., Pan, K.T., Trang, N.N., Chuang, W.Y., Wu, C.Y., and Lo, S.B. (2020). The Use of Artificial Intelligence in the Differentiation of Malignant and Benign Lung Nodules on Computed Tomograms Proven by Surgical Pathology. Cancers, 12.","DOI":"10.3390\/cancers12082211"},{"key":"ref_129","doi-asserted-by":"crossref","unstructured":"Ong, W., Zhu, L., Tan, Y.L., Teo, E.C., Tan, J.H., Kumar, N., Vellayappan, B.A., Ooi, B.C., Quek, S.T., and Makmur, A. (2023). Application of Machine Learning for Differentiating Bone Malignancy on Imaging: A Systematic Review. Cancers, 15.","DOI":"10.3390\/cancers15061837"},{"key":"ref_130","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_131","doi-asserted-by":"crossref","first-page":"e831","DOI":"10.1259\/bjr\/27487871","article-title":"MRI to differentiate benign from malignant soft-tissue tumours of the extremities: A simplified systematic imaging approach using depth, size and heterogeneity of signal intensity","volume":"85","author":"Chung","year":"2012","journal-title":"Br. J. Radiol."},{"key":"ref_132","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1002\/jmri.10061","article-title":"Diffusion-weighted MRI in the characterization of soft-tissue tumors","volume":"15","author":"Kunz","year":"2002","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_133","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1007\/s002560050434","article-title":"Classification and detection of bone marrow lesions with magnetic resonance imaging","volume":"27","author":"Malghem","year":"1998","journal-title":"Skeletal Radiol."},{"key":"ref_134","doi-asserted-by":"crossref","unstructured":"Davies, A.M., Sundaram, M., and James, S.L. (2009). Imaging of Bone Tumors and Tumor-like Lesions: Techniques and Applications, Springer.","DOI":"10.1007\/978-3-540-77984-1"},{"key":"ref_135","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_136","doi-asserted-by":"crossref","unstructured":"Xiong, X., Wang, J., Hu, S., Dai, Y., Zhang, Y., and Hu, C. (2021). Differentiating Between Multiple Myeloma and Metastasis Subtypes of Lumbar Vertebra Lesions Using Machine Learning-Based Radiomics. Front. Oncol., 11.","DOI":"10.3389\/fonc.2021.601699"},{"key":"ref_137","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_138","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_139","doi-asserted-by":"crossref","first-page":"627","DOI":"10.1007\/s00330-004-2627-3","article-title":"Metastatic disease of the spine","volume":"15","author":"Andreula","year":"2005","journal-title":"Eur. Radiol."},{"key":"ref_140","doi-asserted-by":"crossref","first-page":"1584","DOI":"10.3174\/ajnr.A5528","article-title":"Review of the Imaging Features of Benign Osteoporotic and Malignant Vertebral Compression Fractures","volume":"39","author":"Mauch","year":"2018","journal-title":"AJNR Am. J. Neuroradiol."},{"key":"ref_141","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1016\/j.ejrnm.2016.11.001","article-title":"Differentiation of acute osteoporotic from malignant vertebral compression fractures with conventional MRI and diffusion MR imaging","volume":"48","author":"Abowarda","year":"2017","journal-title":"Egypt. J. Radiol. Nucl. Med."},{"key":"ref_142","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.crad.2003.07.005","article-title":"Malignant and benign compression fractures: Differentiation and diagnostic pitfalls on MRI","volume":"59","author":"Uetani","year":"2004","journal-title":"Clin. Radiol."},{"key":"ref_143","first-page":"20190031","article-title":"The role of artificial intelligence in medical imaging research","volume":"2","author":"Tang","year":"2020","journal-title":"BJR Open"},{"key":"ref_144","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1148\/radiology.194.1.7997579","article-title":"Acute vertebral collapse: CT findings in benign and malignant nontraumatic cases","volume":"194","author":"Laredo","year":"1995","journal-title":"Radiology"},{"key":"ref_145","doi-asserted-by":"crossref","first-page":"712","DOI":"10.1097\/01.rct.0000175500.41836.24","article-title":"High-resolution imaging of the spine using multidetector-row computed tomography: Differentiation between benign and malignant vertebral compression fractures","volume":"29","author":"Kubota","year":"2005","journal-title":"J. Comput. Assist. Tomogr."},{"key":"ref_146","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. Off. J. Am. Soc. Clin. Oncol."},{"key":"ref_147","doi-asserted-by":"crossref","first-page":"741","DOI":"10.1038\/s41416-023-02317-8","article-title":"Radiogenomics: A key component of precision cancer medicine","volume":"129","author":"Liu","year":"2023","journal-title":"Br. J. Cancer"},{"key":"ref_148","doi-asserted-by":"crossref","unstructured":"Shui, L., Ren, H., Yang, X., Li, J., Chen, Z., Yi, C., Zhu, H., and Shui, P. (2020). The Era of Radiogenomics in Precision Medicine: An Emerging Approach to Support Diagnosis, Treatment Decisions, and Prognostication in Oncology. Front. Oncol., 10.","DOI":"10.3389\/fonc.2020.570465"},{"key":"ref_149","doi-asserted-by":"crossref","unstructured":"Rabasco Meneghetti, A., Zwanenburg, A., Linge, A., Lohaus, F., Grosser, M., Baretton, G.B., Kalinauskaite, G., Tinhofer, I., Guberina, M., and Stuschke, M. (2022). Integrated radiogenomics analyses allow for subtype classification and improved outcome prognosis of patients with locally advanced HNSCC. Sci. Rep., 12.","DOI":"10.1038\/s41598-022-21159-7"},{"key":"ref_150","doi-asserted-by":"crossref","first-page":"641","DOI":"10.1038\/s41416-021-01387-w","article-title":"Radiomics and radiogenomics in gliomas: A contemporary update","volume":"125","author":"Singh","year":"2021","journal-title":"Br. J. Cancer"},{"key":"ref_151","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1210\/er.2003-0027","article-title":"Vascular endothelial growth factor: Basic science and clinical progress","volume":"25","author":"Ferrara","year":"2004","journal-title":"Endocr. Rev."},{"key":"ref_152","doi-asserted-by":"crossref","first-page":"1205","DOI":"10.1002\/jor.22873","article-title":"Prognostic role of nuclear factor\/IB and bone remodeling proteins in metastatic giant cell tumor of bone: A retrospective study","volume":"33","author":"Quattrini","year":"2015","journal-title":"J. Orthop. Res."},{"key":"ref_153","doi-asserted-by":"crossref","first-page":"e617","DOI":"10.1016\/j.wneu.2018.08.135","article-title":"Prognostic Analysis of Clinical and Immunohistochemical Factors for Patients with Spinal Schwannoma","volume":"120","author":"Li","year":"2018","journal-title":"World Neurosurg."},{"key":"ref_154","doi-asserted-by":"crossref","first-page":"31","DOI":"10.4103\/ortho.IJOrtho_153_17","article-title":"The Values of Receptor Activator Nuclear Kappa-B Ligand Expression in Stage III Giant Cell Tumor of the Bone","volume":"52","author":"Ghani","year":"2018","journal-title":"Indian J. Orthop."},{"key":"ref_155","doi-asserted-by":"crossref","unstructured":"Tsukamoto, S., Mavrogenis, A.F., Kido, A., and Errani, C. (2021). Current Concepts in the Treatment of Giant Cell Tumors of Bone. Cancers, 13.","DOI":"10.3390\/cancers13153647"},{"key":"ref_156","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1097\/BRS.0000000000003728","article-title":"Denosumab Treatment for Giant Cell Tumor of the Spine Including the Sacrum","volume":"46","author":"Bukata","year":"2021","journal-title":"Spine"},{"key":"ref_157","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_158","doi-asserted-by":"crossref","first-page":"1779","DOI":"10.2147\/JMDH.S410301","article-title":"Machine Learning and AI in Cancer Prognosis, Prediction, and Treatment Selection: A Critical Approach","volume":"16","author":"Zhang","year":"2023","journal-title":"J. Multidiscip. Healthc."},{"key":"ref_159","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1038\/s41571-021-00560-7","article-title":"Predicting cancer outcomes with radiomics and artificial intelligence in radiology","volume":"19","author":"Bera","year":"2022","journal-title":"Nat. Rev. Clin. Oncol."},{"key":"ref_160","doi-asserted-by":"crossref","unstructured":"Tan, H., Gao, X., Li, X., Huang, Y., Cao, Q., and Wan, T. (2022). Sarcopenia in Patients With Spinal Metastasis: A Systematic Review and Meta-Analysis of Retrospective Cohort Studies. Front. Oncol., 12.","DOI":"10.3389\/fonc.2022.864501"},{"key":"ref_161","doi-asserted-by":"crossref","first-page":"22","DOI":"10.21037\/tgh.2019.10.13","article-title":"Assessment of body composition and impact of sarcopenia and sarcopenic obesity in patients with gastric cancer","volume":"5","author":"Nishigori","year":"2020","journal-title":"Transl. Gastroenterol. Hepatol."},{"key":"ref_162","doi-asserted-by":"crossref","first-page":"595","DOI":"10.1016\/j.spinee.2021.10.011","article-title":"Body composition predictors of mortality on computed tomography in patients with spinal metastases undergoing surgical treatment","volume":"22","author":"Bongers","year":"2022","journal-title":"Spine J."},{"key":"ref_163","doi-asserted-by":"crossref","first-page":"4283","DOI":"10.21037\/apm-20-967","article-title":"Possible sarcopenia: Early screening and intervention-narrative review","volume":"9","author":"Xie","year":"2020","journal-title":"Ann. Palliat. Med."},{"key":"ref_164","doi-asserted-by":"crossref","first-page":"1934","DOI":"10.3348\/kjr.2021.0775","article-title":"Automated CT-Based Body Composition Analysis: A Golden Opportunity","volume":"22","author":"Pickhardt","year":"2021","journal-title":"Korean J. Radiol."},{"key":"ref_165","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1097\/MCO.0000000000000974","article-title":"Nutritional interventions in prehabilitation for cancer surgery","volume":"26","author":"Wall","year":"2023","journal-title":"Curr. Opin. Clin. Nutr. Metab. Care"},{"key":"ref_166","doi-asserted-by":"crossref","unstructured":"Cepeda, S., Luppino, L.T., P\u00e9rez-N\u00fa\u00f1ez, A., Solheim, O., Garc\u00eda-Garc\u00eda, S., Velasco-Casares, M., Karlberg, A., Eikenes, L., Sarabia, R., and Arrese, I. (2023). Predicting Regions of Local Recurrence in Glioblastomas Using Voxel-Based Radiomic Features of Multiparametric Postoperative MRI. Cancers, 15.","DOI":"10.3390\/cancers15061894"},{"key":"ref_167","doi-asserted-by":"crossref","first-page":"1575","DOI":"10.21037\/tlcr-23-5","article-title":"Review of the use of radiomics to assess the risk of recurrence in early-stage non-small cell lung cancer","volume":"12","author":"Libling","year":"2023","journal-title":"Transl. Lung Cancer Res."},{"key":"ref_168","first-page":"e59954","article-title":"Unveiling the Influence of AI Predictive Analytics on Patient Outcomes: A Comprehensive Narrative Review","volume":"16","author":"Dixon","year":"2024","journal-title":"Cureus"},{"key":"ref_169","doi-asserted-by":"crossref","unstructured":"Liao, J., Li, X., Gan, Y., Han, S., Rong, P., Wang, W., Li, W., and Zhou, L. (2022). Artificial intelligence assists precision medicine in cancer treatment. Front. Oncol., 12.","DOI":"10.3389\/fonc.2022.998222"},{"key":"ref_170","doi-asserted-by":"crossref","first-page":"900","DOI":"10.1158\/2159-8290.CD-21-0090","article-title":"Artificial Intelligence in Cancer Research and Precision Medicine","volume":"11","author":"Bhinder","year":"2021","journal-title":"Cancer Discov."},{"key":"ref_171","doi-asserted-by":"crossref","unstructured":"Gong, X.-Q., Liu, N., Tao, Y.-Y., Li, L., Li, Z.-M., Yang, L., and Zhang, X.-M. (2023). Radiomics models based on multisequence MRI for predicting PD-1\/PD-L1 expression in hepatocellular carcinoma. Sci. Rep., 13.","DOI":"10.1038\/s41598-023-34763-y"},{"key":"ref_172","doi-asserted-by":"crossref","unstructured":"Li, Q., Han, J., Yang, Y., and Chen, Y. (2022). PD-1\/PD-L1 checkpoint inhibitors in advanced hepatocellular carcinoma immunotherapy. Front. Immunol., 13.","DOI":"10.3389\/fimmu.2022.1070961"},{"key":"ref_173","doi-asserted-by":"crossref","unstructured":"Bove, S., Fanizzi, A., Fadda, F., Comes, M.C., Catino, A., Cirillo, A., Cristofaro, C., Montrone, M., Nardone, A., and Pizzutilo, P. (2023). A CT-based transfer learning approach to predict NSCLC recurrence: The added-value of peritumoral region. PLoS ONE, 18.","DOI":"10.1371\/journal.pone.0285188"},{"key":"ref_174","doi-asserted-by":"crossref","first-page":"72860","DOI":"10.18632\/oncotarget.20529","article-title":"Stereotactic ablative body radiotherapy for spinal metastasis from hepatocellular carcinoma: Its oncologic outcomes and risk of vertebral compression fracture","volume":"8","author":"Yoo","year":"2017","journal-title":"Oncotarget"},{"key":"ref_175","doi-asserted-by":"crossref","unstructured":"Al Taha, K., Lauper, N., Bauer, D.E., Tsoupras, A., Tessitore, E., Biver, E., and Dominguez, D.E. (2024). Multidisciplinary and Coordinated Management of Osteoporotic Vertebral Compression Fractures: Current State of the Art. J. Clin. Med., 13.","DOI":"10.3390\/jcm13040930"},{"key":"ref_176","doi-asserted-by":"crossref","first-page":"101158","DOI":"10.1016\/j.adro.2022.101158","article-title":"Spinal Cord Delineation Based on Computed Tomography Myelogram Versus T2 Magnetic Resonance Imaging in Spinal Stereotactic Body Radiation Therapy","volume":"8","author":"Hammoudeh","year":"2023","journal-title":"Adv. Radiat. Oncol."},{"key":"ref_177","doi-asserted-by":"crossref","first-page":"S76","DOI":"10.1093\/neuonc\/noad225","article-title":"Stereotactic body radiation therapy for spinal metastases: A new standard of care","volume":"26","author":"Sacino","year":"2024","journal-title":"Neuro-Oncology"},{"key":"ref_178","doi-asserted-by":"crossref","unstructured":"Kuah, T., Vellayappan, B., Makmur, A., Nair, S., Song, J., Tan, J.H., Kumar, N., Quek, S., and Hallinan, J. (2022). State-of-the-Art Imaging Techniques in Metastatic Spinal Cord Compression. Cancers, 14.","DOI":"10.3390\/cancers14133289"},{"key":"ref_179","doi-asserted-by":"crossref","first-page":"1445","DOI":"10.1007\/s11548-020-02222-y","article-title":"Manual segmentation versus semi-automated segmentation for quantifying vestibular schwannoma volume on MRI","volume":"15","author":"McGrath","year":"2020","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"ref_180","doi-asserted-by":"crossref","first-page":"101470","DOI":"10.1016\/j.adro.2024.101470","article-title":"Review of Deep Learning Based Autosegmentation for Clinical Target Volume: Current Status and Future Directions","volume":"9","author":"Matoska","year":"2024","journal-title":"Adv. Radiat. Oncol."},{"key":"ref_181","doi-asserted-by":"crossref","unstructured":"Lastrucci, A., Wandael, Y., Ricci, R., Maccioni, G., and Giansanti, D. (2024). The Integration of Deep Learning in Radiotherapy: Exploring Challenges, Opportunities, and Future Directions through an Umbrella Review. Diagnostics, 14.","DOI":"10.3390\/diagnostics14090939"},{"key":"ref_182","doi-asserted-by":"crossref","first-page":"578","DOI":"10.1111\/1754-9485.13286","article-title":"Deep learning for segmentation in radiation therapy planning: A review","volume":"65","author":"Samarasinghe","year":"2021","journal-title":"J. Med. Imaging Radiat. Oncol."},{"key":"ref_183","doi-asserted-by":"crossref","unstructured":"Wang, M., Zhang, Q., Lam, S., Cai, J., and Yang, R. (2020). A Review on Application of Deep Learning Algorithms in External Beam Radiotherapy Automated Treatment Planning. Front. Oncol., 10.","DOI":"10.3389\/fonc.2020.580919"},{"key":"ref_184","doi-asserted-by":"crossref","first-page":"1096","DOI":"10.1016\/j.ijrobp.2020.10.038","article-title":"Automatic Segmentation Using Deep Learning to Enable Online Dose Optimization During Adaptive Radiation Therapy of Cervical Cancer","volume":"109","author":"Rigaud","year":"2021","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"ref_185","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.radonc.2022.05.018","article-title":"Training, validation, and clinical implementation of a deep-learning segmentation model for radiotherapy of loco-regional breast cancer","volume":"173","author":"Almberg","year":"2022","journal-title":"Radiother. Oncol."},{"key":"ref_186","doi-asserted-by":"crossref","first-page":"e13668","DOI":"10.1002\/acm2.13668","article-title":"Comparison of Eclipse Smart Segmentation and MIM Atlas Segment for liver delineation for yttrium-90 selective internal radiation therapy","volume":"23","author":"Li","year":"2022","journal-title":"J. Appl. Clin. Med. Phys."},{"key":"ref_187","doi-asserted-by":"crossref","unstructured":"Buchanan, L., Hamdan, S., Zhang, Y., Chen, X., and Li, X.A. (2023). Deep learning-based prediction of deliverable adaptive plans for MR-guided adaptive radiotherapy: A feasibility study. Front. Oncol., 13.","DOI":"10.3389\/fonc.2023.939951"},{"key":"ref_188","doi-asserted-by":"crossref","first-page":"e210097","DOI":"10.1148\/ryai.2021210097","article-title":"Toward Generalizability in the Deployment of Artificial Intelligence in Radiology: Role of Computation Stress Testing to Overcome Underspecification","volume":"3","author":"Eche","year":"2021","journal-title":"Radiol. Artif. Intell."},{"key":"ref_189","doi-asserted-by":"crossref","first-page":"731","DOI":"10.1007\/s44174-023-00063-2","article-title":"Drawbacks of Artificial Intelligence and Their Potential Solutions in the Healthcare Sector","volume":"1","author":"Khan","year":"2023","journal-title":"Biomed. Mater. Devices"},{"key":"ref_190","doi-asserted-by":"crossref","unstructured":"Petersson, L., Larsson, I., Nygren, J.M., Nilsen, P., Neher, M., Reed, J.E., Tyskbo, D., and Svedberg, P. (2022). Challenges to implementing artificial intelligence in healthcare: A qualitative interview study with healthcare leaders in Sweden. BMC Health Serv. Res., 22.","DOI":"10.1186\/s12913-022-08215-8"},{"key":"ref_191","first-page":"48","article-title":"Exploring the opportunities and challenges of implementing artificial intelligence in healthcare: A systematic literature review","volume":"42","author":"Wubineh","year":"2024","journal-title":"Urol. Oncol. Semin. Orig. Investig."},{"key":"ref_192","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1097\/RLI.0000000000000666","article-title":"Variability and Standardization of Quantitative Imaging: Monoparametric to Multiparametric Quantification, Radiomics, and Artificial Intelligence","volume":"55","author":"Hagiwara","year":"2020","journal-title":"Investig. Radiol."},{"key":"ref_193","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1007\/s00330-020-07148-2","article-title":"Challenges and solutions for introducing artificial intelligence (AI) in daily clinical workflow","volume":"31","author":"Kotter","year":"2021","journal-title":"Eur. Radiol."},{"key":"ref_194","doi-asserted-by":"crossref","unstructured":"Brady, A.P., and Neri, E. (2020). Artificial Intelligence in Radiology-Ethical Considerations. Diagnostics, 10.","DOI":"10.3390\/diagnostics10040231"},{"key":"ref_195","doi-asserted-by":"crossref","first-page":"20190286","DOI":"10.1259\/bjr.20190286","article-title":"Machine learning for differentiating metastatic and completely responded sclerotic bone lesion in prostate cancer: A retrospective radiomics study","volume":"92","author":"Acar","year":"2019","journal-title":"Br. J. Radiol."},{"key":"ref_196","doi-asserted-by":"crossref","unstructured":"Joshi, G., Jain, A., Araveeti, S.R., Adhikari, S., Garg, H., and Bhandari, M. (2024). FDA-Approved Artificial Intelligence and Machine Learning (AI\/ML)-Enabled Medical Devices: An Updated Landscape. Electronics, 13.","DOI":"10.3390\/electronics13030498"},{"key":"ref_197","doi-asserted-by":"crossref","first-page":"6384","DOI":"10.1007\/s00330-021-07709-z","article-title":"Deep learning-based metal artefact reduction in PET\/CT imaging","volume":"31","author":"Arabi","year":"2021","journal-title":"Eur. Radiol."},{"key":"ref_198","doi-asserted-by":"crossref","first-page":"e14166","DOI":"10.1002\/acm2.14166","article-title":"Preclinical validation of a novel deep learning-based metal artifact correction algorithm for orthopedic CT imaging","volume":"24","author":"Guo","year":"2023","journal-title":"J. Appl. Clin. Med. Phys."},{"key":"ref_199","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1007\/s00330-022-08971-5","article-title":"Motion artifact removal in coronary CT angiography based on generative adversarial networks","volume":"33","author":"Zhang","year":"2023","journal-title":"Eur. Radiol."},{"key":"ref_200","doi-asserted-by":"crossref","first-page":"100573","DOI":"10.1016\/j.imu.2021.100573","article-title":"Metal artifact reduction in computed tomography images based on developed generative adversarial neural network","volume":"24","author":"Khaleghi","year":"2021","journal-title":"Inform. Med. Unlocked"},{"key":"ref_201","doi-asserted-by":"crossref","first-page":"80","DOI":"10.4103\/jmp.jmp_87_22","article-title":"Evaluation of the Effects of Differences in Metal Artifact Type and Location on Image Quality in Computed Tomography Scans","volume":"48","author":"Lim","year":"2023","journal-title":"J. Med. Phys."},{"key":"ref_202","doi-asserted-by":"crossref","first-page":"695","DOI":"10.1007\/s00062-021-01101-6","article-title":"Iterative Metal Artifact Reduction (iMAR) of the Non-adhesive Liquid Embolic Agent Onyx in Computed Tomography: An Experimental Study","volume":"32","author":"Schmitt","year":"2022","journal-title":"Clin. Neuroradiol."},{"key":"ref_203","doi-asserted-by":"crossref","first-page":"e221257","DOI":"10.1148\/radiol.221257","article-title":"Deep Learning Image Reconstruction for CT: Technical Principles and Clinical Prospects","volume":"306","author":"Koetzier","year":"2023","journal-title":"Radiology"},{"key":"ref_204","doi-asserted-by":"crossref","first-page":"111541","DOI":"10.1016\/j.radphyschem.2024.111541","article-title":"Potential of Metal Artifact Reduction (MAR) and Deep Learning-based Reconstruction (DLR) algorithms integration in CT Metal Artifact Correction: A review","volume":"218","author":"Njiti","year":"2024","journal-title":"Radiat. Phys. Chem."},{"key":"ref_205","doi-asserted-by":"crossref","first-page":"111276","DOI":"10.1016\/j.ejrad.2023.111276","article-title":"Advances in metal artifact reduction in CT images: A review of traditional and novel metal artifact reduction techniques","volume":"170","author":"Selles","year":"2024","journal-title":"Eur. J. Radiol."},{"key":"ref_206","doi-asserted-by":"crossref","first-page":"440","DOI":"10.2174\/1874325001206010440","article-title":"Prevalence of known and unknown primary tumor sites in spinal metastasis patients","volume":"6","author":"Paholpak","year":"2012","journal-title":"Open Orthop. J."},{"key":"ref_207","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_208","doi-asserted-by":"crossref","first-page":"2451","DOI":"10.1097\/CORR.0000000000001241","article-title":"Does PET\/CT Aid in Detecting Primary Carcinoma in Patients with Skeletal Metastases of Unknown Primary?","volume":"478","author":"Lawrenz","year":"2020","journal-title":"Clin. Orthop. Relat. Res."},{"key":"ref_209","doi-asserted-by":"crossref","unstructured":"Liu, K., Qin, S., Ning, J., Xin, P., Wang, Q., Chen, Y., Zhao, W., Zhang, E., and Lang, N. (2023). Prediction of Primary Tumor Sites in Spinal Metastases Using a ResNet-50 Convolutional Neural Network Based on MRI. Cancers, 15.","DOI":"10.3390\/cancers15112974"},{"key":"ref_210","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_211","doi-asserted-by":"crossref","first-page":"5147085","DOI":"10.1155\/2022\/5147085","article-title":"Differentiating Primary Tumors for Brain Metastasis with Integrated Radiomics from Multiple Imaging Modalities","volume":"2022","author":"Cao","year":"2022","journal-title":"Dis. Markers"},{"key":"ref_212","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_213","doi-asserted-by":"crossref","first-page":"1510","DOI":"10.1007\/s00259-023-06108-4","article-title":"Automated quantification of PET\/CT skeletal tumor burden in prostate cancer using artificial intelligence: The PET index","volume":"50","author":"Larsson","year":"2023","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"ref_214","doi-asserted-by":"crossref","unstructured":"Oprea-Lager, D.E., Cysouw, M.C.F., Boellaard, R., Deroose, C.M., de Geus-Oei, L.F., Lopci, E., Bidaut, L., Herrmann, K., Fournier, L.S., and B\u00e4uerle, T. (2021). Bone Metastases Are Measurable: The Role of Whole-Body MRI and Positron Emission Tomography. Front. Oncol., 11.","DOI":"10.3389\/fonc.2021.772530"},{"key":"ref_215","doi-asserted-by":"crossref","unstructured":"Moreau, N., Rousseau, C., Fourcade, C., Santini, G., Ferrer, L., Lacombe, M., Guillerminet, C., Campone, M., Colombi\u00e9, M., and Rubeaux, M. (2020, January 20\u201324). Deep learning approaches for bone and bone lesion segmentation on 18FDG PET\/CT imaging in the context of metastatic breast cancer. Proceedings of the 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Montreal, QC, Canada.","DOI":"10.1109\/EMBC44109.2020.9175904"},{"key":"ref_216","doi-asserted-by":"crossref","first-page":"51","DOI":"10.2147\/CMAR.S340114","article-title":"Deep Learning for the Automatic Diagnosis and Analysis of Bone Metastasis on Bone Scintigrams","volume":"14","author":"Liu","year":"2022","journal-title":"Cancer Manag. Res."},{"key":"ref_217","doi-asserted-by":"crossref","unstructured":"Saeed, M.U., Dikaios, N., Dastgir, A., Ali, G., Hamid, M., and Hajjej, F. (2023). An Automated Deep Learning Approach for Spine Segmentation and Vertebrae Recognition Using Computed Tomography Images. Diagnostics, 13.","DOI":"10.3390\/diagnostics13162658"},{"key":"ref_218","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.ejca.2022.06.055","article-title":"An artificial intelligence model predicts the survival of solid tumour patients from imaging and clinical data","volume":"174","author":"Schutte","year":"2022","journal-title":"Eur. J. Cancer"},{"key":"ref_219","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1186\/s41824-023-00172-7","article-title":"AI-based quantification of whole-body tumour burden on somatostatin receptor PET\/CT","volume":"7","author":"Enqvist","year":"2023","journal-title":"Eur. J. Hybrid Imaging"},{"key":"ref_220","doi-asserted-by":"crossref","first-page":"51","DOI":"10.17294\/2330-0698.2029","article-title":"Patient Perspectives on the Use of Artificial Intelligence in Health Care: A Scoping Review","volume":"11","author":"Moy","year":"2024","journal-title":"J. Patient Cent. Res. Rev."},{"key":"ref_221","first-page":"e45255","article-title":"Clinicians\u2019 Views on Using Artificial Intelligence in Healthcare: Opportunities, Challenges, and Beyond","volume":"15","author":"Alanazi","year":"2023","journal-title":"Cureus"},{"key":"ref_222","doi-asserted-by":"crossref","unstructured":"Daniyal, M., Qureshi, M., Marzo, R.R., Aljuaid, M., and Shahid, D. (2024). Exploring clinical specialists\u2019 perspectives on the future role of AI: Evaluating replacement perceptions, benefits, and drawbacks. BMC Health Serv. Res., 24.","DOI":"10.1186\/s12913-024-10928-x"},{"key":"ref_223","doi-asserted-by":"crossref","unstructured":"Khanna, N.N., Maindarkar, M.A., Viswanathan, V., Fernandes, J.F.E., Paul, S., Bhagawati, M., Ahluwalia, P., Ruzsa, Z., Sharma, A., and Kolluri, R. (2022). Economics of Artificial Intelligence in Healthcare: Diagnosis vs. Treatment. Healthcare, 10.","DOI":"10.3390\/healthcare10122493"}],"container-title":["Cancers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-6694\/16\/17\/2988\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:43:59Z","timestamp":1760111039000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-6694\/16\/17\/2988"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,28]]},"references-count":223,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["cancers16172988"],"URL":"https:\/\/doi.org\/10.3390\/cancers16172988","relation":{},"ISSN":["2072-6694"],"issn-type":[{"value":"2072-6694","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,28]]}}}