{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,19]],"date-time":"2026-10-19T20:36:56Z","timestamp":1792442216668,"version":"4.5.0"},"reference-count":42,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","ejradiology.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["European Journal of Radiology"],"published-print":{"date-parts":[[2025,8]]},"DOI":"10.1016\/j.ejrad.2025.112166","type":"journal-article","created":{"date-parts":[[2025,5,9]],"date-time":"2025-05-09T13:02:21Z","timestamp":1746795741000},"page":"112166","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":19,"special_numbering":"C","title":["Digital twins in radiology: A systematic review of applications, challenges, and future perspectives"],"prefix":"10.1016","volume":"189","author":[{"given":"E.","family":"Faiella","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3516-7727","authenticated-orcid":false,"given":"M.","family":"Pileri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"R.","family":"Ragone","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"R.F.","family":"Grasso","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"B.Beomonte","family":"Zobel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"D.","family":"Santucci","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.ejrad.2025.112166_b0005","doi-asserted-by":"crossref","DOI":"10.3389\/fdgth.2023.1253050","article-title":"Digital twin for healthcare systems","volume":"5","author":"Vall\u00e9e","year":"2023","journal-title":"Front. Digit. Health"},{"key":"10.1016\/j.ejrad.2025.112166_b0010","article-title":"Digital twin in healthcare: recent updates and challenges","volume":"9","author":"Sun","year":"2023","journal-title":"Digit. Health"},{"issue":"21","key":"10.1016\/j.ejrad.2025.112166_b0015","doi-asserted-by":"crossref","first-page":"6553","DOI":"10.3390\/jcm11216553","article-title":"Digital twins in radiology","volume":"11","author":"Pesapane","year":"2022","journal-title":"J. Clin. Med.,"},{"issue":"11","key":"10.1016\/j.ejrad.2025.112166_b0020","doi-asserted-by":"crossref","first-page":"1101","DOI":"10.3390\/jpm14111101","article-title":"Digital twins\u2019 advancements and applications in healthcare, towards precision medicine","volume":"14","author":"Papachristou","year":"2024","journal-title":"J. Pers. Med.,"},{"issue":"21","key":"10.1016\/j.ejrad.2025.112166_b0025","doi-asserted-by":"crossref","first-page":"11588","DOI":"10.3390\/ijms252111588","article-title":"Recent advances in artificial intelligence to improve immunotherapy and the use of digital twins to identify prognosis of patients with solid tumors","volume":"25","author":"D\u2019Orsi","year":"2024","journal-title":"Int. J. Mol. Sci.,"},{"issue":"13","key":"10.1016\/j.ejrad.2025.112166_b0030","doi-asserted-by":"crossref","first-page":"7940","DOI":"10.3390\/app13137940","article-title":"Digital twins: the new frontier for personalized medicine?","volume":"13","author":"Cellina","year":"2023","journal-title":"Appl. Sci.,"},{"issue":"8","key":"10.1016\/j.ejrad.2025.112166_b0035","doi-asserted-by":"crossref","first-page":"1255","DOI":"10.3390\/jpm12081255","article-title":"Digital twins in healthcare: is it the beginning of a new era of evidence-based medicine? a critical review","volume":"12","author":"Armeni","year":"2022","journal-title":"J. Pers. Med.,"},{"issue":"8","key":"10.1016\/j.ejrad.2025.112166_b0040","doi-asserted-by":"crossref","first-page":"529","DOI":"10.7326\/0003-4819-155-8-201110180-00009","article-title":"QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies","volume":"155","author":"Whiting","year":"2011","journal-title":"Ann. Intern. Med.,"},{"key":"10.1016\/j.ejrad.2025.112166_b0045","doi-asserted-by":"crossref","unstructured":"Y. Feng, H. You, Z. Zhang, R. Ji, Y. Gao, \u2018Hypergraph Neural Networks\u2019, Proc. AAAI Conf. Artif. Intell., vol. 33, no. 01, Art. no. 01, Jul. 2019, doi: 10.1609\/aaai.v33i01.33013558.","DOI":"10.1609\/aaai.v33i01.33013558"},{"key":"10.1016\/j.ejrad.2025.112166_b0050","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2023.105632","article-title":"Digital twin of renal system with CT-radiography for the early diagnosis of chronic kidney diseases","volume":"88","author":"Sasikaladevi","year":"2024","journal-title":"Biomed. Signal Process. Control"},{"key":"10.1016\/j.ejrad.2025.112166_b0055","unstructured":"A. Dosovitskiy et al., An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale, Jun. 03, 2021, arXiv: arXiv:2010.11929. doi: 10.48550\/arXiv.2010.11929."},{"issue":"1","key":"10.1016\/j.ejrad.2025.112166_b0060","doi-asserted-by":"crossref","first-page":"11440","DOI":"10.1038\/s41598-022-15634-4","article-title":"Vision transformer and explainable transfer learning models for auto detection of kidney cyst, stone and tumor from CT-radiography","volume":"12","author":"Islam","year":"2022","journal-title":"Sci. Rep.,"},{"issue":"3","key":"10.1016\/j.ejrad.2025.112166_b0065","doi-asserted-by":"crossref","first-page":"661","DOI":"10.1016\/j.jhep.2020.10.036","article-title":"Predicting the risk of post-hepatectomy portal hypertension using a digital twin: A clinical proof of concept","volume":"74","author":"Golse","year":"2021","journal-title":"J. Hepatol.,"},{"issue":"1","key":"10.1016\/j.ejrad.2025.112166_b0070","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1186\/s13018-024-05417-0","article-title":"Patient-specific spine digital twins: a computational characterization of the idiopathic scoliosis","volume":"20","author":"Landinez","year":"2025","journal-title":"J. Orthop. Surg.,"},{"issue":"6","key":"10.1016\/j.ejrad.2025.112166_b0075","doi-asserted-by":"crossref","first-page":"e3601","DOI":"10.1002\/cnm.3601","article-title":"Toward an artificial intelligence-assisted framework for reconstructing the digital twin of vertebra and predicting its fracture response","volume":"38","author":"Ahmadian","year":"2022","journal-title":"Int. J. Numer. Methods Biomed. Eng.,"},{"key":"10.1016\/j.ejrad.2025.112166_b0080","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1109\/OJEMB.2023.3345733","article-title":"Simulation of image-guided microwave ablation therapy using a digital twin computational model","volume":"5","author":"Servin","year":"2024","journal-title":"IEEE Open J. Eng. Med. Biol.,"},{"issue":"1","key":"10.1016\/j.ejrad.2025.112166_b0085","doi-asserted-by":"crossref","first-page":"10603","DOI":"10.1038\/s41598-023-37774-x","article-title":"Effectiveness of creating digital twins with different digital dentition models and cone-beam computed tomography","volume":"13","author":"Lee","year":"2023","journal-title":"Sci. Rep.,"},{"issue":"7","key":"10.1016\/j.ejrad.2025.112166_b0090","doi-asserted-by":"crossref","first-page":"1359","DOI":"10.1007\/s11548-024-03151-w","article-title":"An endoscopic chisel: intraoperative imaging carves 3D anatomical models","volume":"19","author":"Mangulabnan","year":"2024","journal-title":"Int. J. Comput. Assist. Radiol. Surg.,"},{"key":"10.1016\/j.ejrad.2025.112166_b0095","first-page":"1052","article-title":"Towards a digital twin in human brain: brain tumor detection using K-means","volume":"302","author":"Sarris","year":"2023","journal-title":"Stud. Health Technol. Inform.,"},{"issue":"1","key":"10.1016\/j.ejrad.2025.112166_b0100","doi-asserted-by":"crossref","first-page":"16279","DOI":"10.1038\/s41598-023-43618-5","article-title":"Toward precision medicine using a \u201cdigital twin\u201d approach: modeling the onset of disease-specific brain atrophy in individuals with multiple sclerosis","volume":"13","author":"Cen","year":"2023","journal-title":"Sci. Rep.,"},{"issue":"3","key":"10.1016\/j.ejrad.2025.112166_b0105","doi-asserted-by":"crossref","first-page":"e3559","DOI":"10.1002\/cnm.3559","article-title":"Automating fractional flow reserve (FFR) calculation from CT scans: A rapid workflow using unsupervised learning and computational fluid dynamics","volume":"38","author":"Chakshu","year":"2022","journal-title":"Int. J. Numer. Methods Biomed. Eng.,"},{"issue":"2212","key":"10.1016\/j.ejrad.2025.112166_b0110","article-title":"A completely automated pipeline for 3D reconstruction of human heart from 2D cine magnetic resonance slices","volume":"379","author":"Banerjee","year":"2021","journal-title":"Philos. Trans. r. Soc. Math. Phys. Eng. Sci.,"},{"issue":"7","key":"10.1016\/j.ejrad.2025.112166_b0115","doi-asserted-by":"crossref","first-page":"2466","DOI":"10.1109\/TMI.2024.3367409","article-title":"Toward enabling cardiac digital twins of myocardial infarction using deep computational models for inverse inference","volume":"43","author":"Li","year":"2024","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"10","key":"10.1016\/j.ejrad.2025.112166_b0120","doi-asserted-by":"crossref","first-page":"1374","DOI":"10.1093\/ehjci\/jeae123","article-title":"Evaluation of deep learning estimation of whole heart anatomy from automated cardiovascular magnetic resonance short- and long-axis analyses in UK Biobank","volume":"25","author":"Muffoletto","year":"2024","journal-title":"Eur. Heart J. Cardiovasc. Imaging"},{"key":"10.1016\/j.ejrad.2025.112166_b0125","doi-asserted-by":"crossref","unstructured":"L. Azzolin et al., AugmentA: Patient-specific augmented atrial model generation tool, Comput. Med. Imaging Graph. Off. J. Comput. Med. Imaging Soc., vol. 108, p. 102265, 2023, doi: 10.1016\/j.compmedimag.2023.102265.","DOI":"10.1016\/j.compmedimag.2023.102265"},{"issue":"9","key":"10.1016\/j.ejrad.2025.112166_b0130","doi-asserted-by":"crossref","first-page":"2035","DOI":"10.1016\/j.jacep.2024.04.032","article-title":"Cardiac MRI oversampling in heart digital twins improves preprocedure ventricular tachycardia identification in postinfarction patients","volume":"10","author":"O\u2019Hara","year":"2024","journal-title":"JACC Clin. Electrophysiol.,"},{"issue":"1","key":"10.1016\/j.ejrad.2025.112166_b0135","doi-asserted-by":"crossref","DOI":"10.1088\/2057-1976\/ad8c47","article-title":"Multi-level digital-twin models of pulmonary mechanics: correlation analysis of 3D CT lung volume and 2D Chest motion","volume":"11","author":"Zhou","year":"2024","journal-title":"Biomed. Phys. Eng. Express"},{"issue":"10","key":"10.1016\/j.ejrad.2025.112166_b0140","doi-asserted-by":"crossref","first-page":"899","DOI":"10.1097\/RLU.0000000000005400","article-title":"Organomics: a concept reflecting the importance of PET\/CT healthy organ radiomics in non-small cell lung cancer prognosis prediction using machine learning","volume":"49","author":"Salimi","year":"2024","journal-title":"Clin. Nucl. Med.,"},{"issue":"18","key":"10.1016\/j.ejrad.2025.112166_b0145","doi-asserted-by":"crossref","first-page":"3394","DOI":"10.1158\/0008-5472.CAN-22-1329","article-title":"MRI-based digital models forecast patient-specific treatment responses to neoadjuvant chemotherapy in triple-negative breast cancer","volume":"82","author":"Wu","year":"2022","journal-title":"Cancer Res.,"},{"issue":"4","key":"10.1016\/j.ejrad.2025.112166_b0150","doi-asserted-by":"crossref","first-page":"437","DOI":"10.1055\/a-1253-8558","article-title":"Personalized computed tomography - automated estimation ofheight and weight of a simulated digital twin using a 3D camera and artificial intelligence","volume":"193","author":"Geissler","year":"2021","journal-title":"ROFO. Fortschr. Geb. Rontgenstr. Nuklearmed.,"},{"key":"10.1016\/j.ejrad.2025.112166_b0155","doi-asserted-by":"crossref","DOI":"10.3389\/fdgth.2022.1007784","article-title":"Exploring approaches for predictive cancer patient digital twins: Opportunities for collaboration and innovation","volume":"4","author":"Stahlberg","year":"2022","journal-title":"Front. Digit. Health"},{"key":"10.1016\/j.ejrad.2025.112166_b0160","doi-asserted-by":"crossref","DOI":"10.3389\/fonc.2022.1062592","article-title":"Theranostic digital twins for personalized radiopharmaceutical therapies: Reimagining theranostics via computational nuclear oncology","volume":"12","author":"Rahmim","year":"2022","journal-title":"Front. Oncol.,"},{"issue":"8","key":"10.1016\/j.ejrad.2025.112166_b0165","doi-asserted-by":"crossref","first-page":"3061","DOI":"10.1109\/JBHI.2021.3063080","article-title":"iPhantom: a framework for automated creation of individualized computational phantoms and its application to CT organ dosimetry","volume":"25","author":"Fu","year":"2021","journal-title":"IEEE J. Biomed. Health Inform.,"},{"issue":"4","key":"10.1016\/j.ejrad.2025.112166_b0170","doi-asserted-by":"crossref","first-page":"Dec","DOI":"10.1016\/j.jmir.2024.05.004","article-title":"Exploring the potential of digital twin technology as a training tool for new radiographers","volume":"55","author":"Lock","year":"2024","journal-title":"J. Med. Imaging Radiat. Sci.,"},{"key":"10.1016\/j.ejrad.2025.112166_b0175","doi-asserted-by":"crossref","unstructured":"H. Hassani, X. Huang, S. MacFeely, Impactful digital twin in the healthcare revolution, Big Data Cogn. Comput., 6 (3), 3, Sep. 2022, doi: 10.3390\/bdcc6030083.","DOI":"10.3390\/bdcc6030083"},{"issue":"11","key":"10.1016\/j.ejrad.2025.112166_b0180","doi-asserted-by":"crossref","first-page":"1061","DOI":"10.3348\/kjr.2023.0393","article-title":"Overcoming the challenges in the development and implementation of artificial intelligence in radiology: a comprehensive review of solutions beyond supervised learning","volume":"24","author":"Hong","year":"2023","journal-title":"Korean J. Radiol.,"},{"issue":"5","key":"10.1016\/j.ejrad.2025.112166_b0185","doi-asserted-by":"crossref","first-page":"745","DOI":"10.1007\/s13244-018-0645-y","article-title":"Artificial intelligence as a medical device in radiology: ethical and regulatory issues in Europe and the United States","volume":"9","author":"Pesapane","year":"2018","journal-title":"Insights Imaging"},{"key":"10.1016\/j.ejrad.2025.112166_b0190","doi-asserted-by":"crossref","first-page":"670","DOI":"10.1109\/OJEMB.2024.3426916","article-title":"Digital Twins Of Biological Systems: A Narrative Review","volume":"5","author":"Alsalloum","year":"2024","journal-title":"IEEE Open J. Eng Med. Biol.,"},{"key":"10.1016\/j.ejrad.2025.112166_b0195","unstructured":"D. Avinash, M. Gudipati, R. R. Nadendla, Regulatory approach for the approval of Artificial intelligence\/ Machine language (AI\/ML) Based Software as medical devices (SaMD), Int. J. Res. Pharm. Sci. Technol., 2 (3), 3, 2020, 10.33974\/ijrpst.v2i3.264."},{"issue":"1","key":"10.1016\/j.ejrad.2025.112166_b0200","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1017\/ilm.2020.5","article-title":"Recommendation of the Council on Artificial Intelligence (OECD)","volume":"59","author":"Yeung","year":"2020","journal-title":"Int. Leg. Mater.,"},{"issue":"11","key":"10.1016\/j.ejrad.2025.112166_b0205","doi-asserted-by":"crossref","first-page":"e840","DOI":"10.1016\/S2589-7500(23)00156-5","article-title":"Federated electronic health records for the European Health Data Space","volume":"5","author":"Raab","year":"2023","journal-title":"Lancet Digit. Health"},{"issue":"10","key":"10.1016\/j.ejrad.2025.112166_b0210","doi-asserted-by":"crossref","first-page":"1522","DOI":"10.3390\/jpm13101522","article-title":"Digital twins in healthcare: methodological challenges and opportunities","volume":"13","author":"Meijer","year":"2023","journal-title":"J. Pers. Med.,"}],"container-title":["European Journal of Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0720048X25002529?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0720048X25002529?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,6,7]],"date-time":"2025-06-07T14:56:53Z","timestamp":1749308213000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0720048X25002529"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8]]},"references-count":42,"alternative-id":["S0720048X25002529"],"URL":"https:\/\/doi.org\/10.1016\/j.ejrad.2025.112166","relation":{},"ISSN":["0720-048X"],"issn-type":[{"value":"0720-048X","type":"print"}],"subject":[],"published":{"date-parts":[[2025,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Digital twins in radiology: A systematic review of applications, challenges, and future perspectives","name":"articletitle","label":"Article Title"},{"value":"European Journal of Radiology","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ejrad.2025.112166","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"112166"}}