{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,7]],"date-time":"2026-11-07T04:02:09Z","timestamp":1794024129158,"version":"4.8.0"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T00:00:00Z","timestamp":1771804800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T00:00:00Z","timestamp":1771804800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Eur Radiol Exp"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n To compare the quality and clinical usefulness of large language model (LLM)-generated lumbar spine magnetic resonance imaging (MRI) reports with radiologist-written ones and assess whether medical professionals can distinguish between them.<\/jats:p>\n <\/jats:sec>\n \n Materials and methods<\/jats:title>\n \n This retrospective observational single-center study was approved by the local ethics committee. A total of 125 lumbar spine MRI reports (104 human-written, 21 LLM-generated using ChatGPT-4o) were anonymized, randomized, and blindly evaluated by five medical professionals (one board-certified radiologist, two radiology residents, one general practitioner, one orthopedic surgeon), all with basic familiarity with LLM. Each report was scored on a five-point Likert scale for clinical relevance, clarity, completeness, diagnostic accuracy, and intelligibility, whereas general practitioner and\u00a0orthopedic surgeon evaluated intelligibility only. Evaluators also classified each report as AI-generated or human-written. Accuracy was defined as the proportion of correctly classified reports in distinguishing LLM-generated from radiologist-written texts. Mann-Whitney\n U<\/jats:italic>\n or Student's\n t-<\/jats:italic>\n tests were used.\n <\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n Radiologists' reports consistently received higher median scores across all domains (\n p<\/jats:italic>\n \u2009<\u20090.001). No differences were found in the description of the imaging technique (\n p<\/jats:italic>\n \u2009>\u20090.175). No clinically false statements were identified in the LLM-generated reports. Identification accuracy varied widely among evaluators: Board-certified radiologist achieved 88.0% accuracy (sensitivity 66.7%, specificity 92.3%), Resident 1 65.6% (14.3%, 76.0%), Resident 2 94.4% (66.7%, 100%), orthopedic surgeon 78.4% (90.5%, 76.0%) and general practitioner 65.6% (81.0%, 62.5%).\n <\/jats:p>\n <\/jats:sec>\n \n Conclusion<\/jats:title>\n Radiologist-written lumbar spine MRI reports outperform LLM-generated reports in quality and structure. However, some AI-generated reports were indistinguishable from human ones, particularly for non-specialized readers. LLMs may support radiologists in structured reporting and improve workflow efficiency, while maintaining diagnostic reliability.<\/jats:p>\n <\/jats:sec>\n \n Relevance statement<\/jats:title>\n Large language models can draft lumbar spine MRI reports, but currently lack the quality and consistency of radiologist reports. With radiologist supervision, large language models may improve reporting efficiency while preserving diagnostic reliability and supporting clinical decision-making.<\/jats:p>\n <\/jats:sec>\n \n Key Points<\/jats:title>\n \n \n \n LLM-generated reports are clinically coherent and stylistically comparable to those written by expert radiologists.<\/jats:p>\n <\/jats:list-item>\n \n Radiologist-written reports scored significantly higher for clinical relevance, findings, and structure.<\/jats:p>\n <\/jats:list-item>\n \n LLM-generated reports were sometimes misclassified as human-written by clinicians.<\/jats:p>\n <\/jats:list-item>\n <\/jats:list>\n <\/jats:p>\n <\/jats:sec>\n \n Graphical Abstract<\/jats:title>\n <\/jats:sec>","DOI":"10.1186\/s41747-026-00682-6","type":"journal-article","created":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T10:28:39Z","timestamp":1771842519000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Can AI write reports like a radiologist? A blinded evaluation of large language model-generated lumbar spine MRI reports"],"prefix":"10.1186","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9640-8534","authenticated-orcid":false,"given":"Moreno","family":"Zanardo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7989-9861","authenticated-orcid":false,"given":"Domenico","family":"Albano","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Valentina","family":"Molinari","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Renato","family":"Fabrizio","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Martina","family":"Conca","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1410-341X","authenticated-orcid":false,"given":"Luigi","family":"Asmundo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2601-4756","authenticated-orcid":false,"given":"Francesco","family":"Pardo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francesco","family":"Traina","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michele","family":"Montechiari","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3623-7822","authenticated-orcid":false,"given":"Salvatore","family":"Gitto","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0759-8431","authenticated-orcid":false,"given":"Luca Maria","family":"Sconfienza","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,2,23]]},"reference":[{"key":"682_CR1","unstructured":"Vaswani A, Shazeer N, Parmar N et al (2017) Attention is All you Need. In: Guyon I, Luxburg U Von, Bengio S, Wallach H, Fergus R, Vishwanathan S, Garnett R (eds) Advances in Neural Information Processing Systems. Curran Associates, Inc."},{"key":"682_CR2","doi-asserted-by":"publisher","first-page":"80","DOI":"10.4274\/dir.2023.232417","volume":"30","author":"T Akinci D\u2019Antonoli","year":"2024","unstructured":"Akinci D\u2019Antonoli T, Stanzione A et al (2024) Large language models in radiology: fundamentals, applications, ethical considerations, risks, and future directions. Diagn Intervent Radiol 30:80\u201390. https:\/\/doi.org\/10.4274\/dir.2023.232417","journal-title":"Diagn Intervent Radiol"},{"key":"682_CR3","doi-asserted-by":"publisher","unstructured":"Stogiannos N, Walsh G, Ohene-Botwe B et al (2025) R-AI-diographers: a European survey on perceived impact of AI on professional identity, careers, and radiographers\u2019 roles. Insights Imaging 16:1\u201313. https:\/\/doi.org\/10.1186\/S13244-025-01918-6","DOI":"10.1186\/S13244-025-01918-6"},{"key":"682_CR4","doi-asserted-by":"publisher","unstructured":"Zanardo M, Visser JJ, Colarieti A et al (2024) Impact of AI on radiology: a EuroAIM\/EuSoMII 2024 survey among members of the European Society of Radiology. Insights Imaging 15:240. https:\/\/doi.org\/10.1186\/s13244-024-01801-w","DOI":"10.1186\/s13244-024-01801-w"},{"key":"682_CR5","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1186\/s13244-025-01905-x","volume":"16","author":"E Kotter","year":"2025","unstructured":"Kotter E, D\u2019Antonoli TA, Cuocolo R et al (2025) Guiding AI in radiology: ESR\u2019s recommendations for effective implementation of the European AI Act. Insights Imaging 16:33. https:\/\/doi.org\/10.1186\/s13244-025-01905-x","journal-title":"Insights Imaging"},{"key":"682_CR6","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1186\/s41747-025-00591-0","volume":"9","author":"J Pristoupil","year":"2025","unstructured":"Pristoupil J, Oleaga L, Junquero V, Merino C, Ozbek SS, Lambert L (2025) Five advanced chatbots solving European Diploma in Radiology (EDiR) text-based questions: differences in performance and consistency. Eur Radiol Exp 9:79. https:\/\/doi.org\/10.1186\/s41747-025-00591-0","journal-title":"Eur Radiol Exp"},{"key":"682_CR7","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1186\/s41747-024-00457-x","volume":"8","author":"S Rau","year":"2024","unstructured":"Rau S, Rau A, Nattenm\u00fcller J et al (2024) A retrieval-augmented chatbot based on GPT-4 provides appropriate differential diagnosis in gastrointestinal radiology: a proof of concept study. Eur Radiol Exp 8:60. https:\/\/doi.org\/10.1186\/s41747-024-00457-x","journal-title":"Eur Radiol Exp"},{"key":"682_CR8","doi-asserted-by":"publisher","first-page":"930","DOI":"10.3390\/diagnostics15070930","volume":"15","author":"K Ziegeler","year":"2025","unstructured":"Ziegeler K, Kreutzinger V, Tong MW et al (2025) Information extraction from lumbar spine MRI radiology reports using GPT4: accuracy and benchmarking against research-grade comprehensive scoring. Diagnostics (Basel) 15:930. https:\/\/doi.org\/10.3390\/diagnostics15070930","journal-title":"Diagnostics (Basel)"},{"key":"682_CR9","doi-asserted-by":"publisher","first-page":"602","DOI":"10.2214\/AJR.18.20197","volume":"212","author":"PH Yi","year":"2019","unstructured":"Yi PH, Golden SK, Harringa JB, Kliewer MA (2019) Readability of lumbar spine MRI reports: will patients understand? AJR Am J Roentgenol 212:602\u2013606. https:\/\/doi.org\/10.2214\/AJR.18.20197","journal-title":"AJR Am J Roentgenol"},{"key":"682_CR10","doi-asserted-by":"publisher","unstructured":"Tanno R, Barrett DGT, Sellergren A et al (2025) Collaboration between clinicians and vision\u2013language models in radiology report generation. Nat Med 31:599\u2013608. https:\/\/doi.org\/10.1038\/s41591-024-03302-1","DOI":"10.1038\/s41591-024-03302-1"},{"key":"682_CR11","doi-asserted-by":"crossref","unstructured":"Zhu Q, Chen X, Jin Q et al (2024) Leveraging professional radiologists\u2019 expertise to enhance LLMs\u2019 evaluation for AI-generated radiology reports. In: 2024 IEEE 12th International Conference on Healthcare Informatics (ICHI). IEEE, pp. 402\u2013411","DOI":"10.1109\/ICHI61247.2024.00058"},{"key":"682_CR12","doi-asserted-by":"publisher","unstructured":"Huisman M, Rasoolzadeh N (2025) LLMs for radiology reports: from general purpose to lightweight domain adaptation. Radiology 316. https:\/\/doi.org\/10.1148\/radiol.252524","DOI":"10.1148\/radiol.252524"},{"key":"682_CR13","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1186\/s41747-024-00547-w","volume":"9","author":"BS Kelly","year":"2025","unstructured":"Kelly BS, Duignan S, Mathur P et al (2025) Can ChatGPT4-vision identify radiologic progression of multiple sclerosis on brain MRI? Eur Radiol Exp 9:9. https:\/\/doi.org\/10.1186\/s41747-024-00547-w","journal-title":"Eur Radiol Exp"},{"key":"682_CR14","doi-asserted-by":"publisher","first-page":"208","DOI":"10.23750\/abm.v89i1-S.7024","volume":"89","author":"P D\u2019Aprile","year":"2018","unstructured":"D\u2019Aprile P, Nasuto M, Tarantino A, Cornacchia S, Guglielmi G, Jinkins JR (2018) Magnetic resonance imaging in degenerative disease of the lumbar spine: fat saturation technique and contrast medium. Acta Biomed 89:208\u2013219. https:\/\/doi.org\/10.23750\/abm.v89i1-S.7024","journal-title":"Acta Biomed"},{"key":"682_CR15","doi-asserted-by":"publisher","first-page":"TC04","DOI":"10.7860\/JCDR\/2015\/11927.5761","volume":"9","author":"P Suthar","year":"2015","unstructured":"Suthar P (2015) MRI evaluation of lumbar disc degenerative disease. J Clin Diagn Res 9:TC04\u2013TC09. https:\/\/doi.org\/10.7860\/JCDR\/2015\/11927.5761","journal-title":"J Clin Diagn Res"},{"key":"682_CR16","doi-asserted-by":"publisher","DOI":"10.1186\/s12911-024-02740-8","volume":"24","author":"R Dong","year":"2024","unstructured":"Dong R, Cheng X, Kang M, Qu Y (2024) Classification of lumbar spine disorders using large language models and MRI segmentation. BMC Med Inform Decis Mak 24:343. https:\/\/doi.org\/10.1186\/s12911-024-02740-8","journal-title":"BMC Med Inform Decis Mak"},{"key":"682_CR17","doi-asserted-by":"publisher","first-page":"2837","DOI":"10.1007\/s00330-021-08327-5","volume":"32","author":"JM Nobel","year":"2022","unstructured":"Nobel JM, van Geel K, Robben SGF (2022) Structured reporting in radiology: a systematic review to explore its potential. Eur Radiol 32:2837\u20132854. https:\/\/doi.org\/10.1007\/s00330-021-08327-5","journal-title":"Eur Radiol"},{"key":"682_CR18","doi-asserted-by":"publisher","first-page":"S81","DOI":"10.1093\/pm\/pnac130","volume":"24","author":"N Sollmann","year":"2023","unstructured":"Sollmann N, Fields AJ, O\u2019Neill C et al (2023) Magnetic resonance imaging of the lumbar spine: recommendations for acquisition and image evaluation from the BACPAC Spine Imaging Working Group. Pain Medicine 24:S81\u2013S94. https:\/\/doi.org\/10.1093\/pm\/pnac130","journal-title":"Pain Medicine"},{"key":"682_CR19","doi-asserted-by":"publisher","first-page":"728","DOI":"10.1067\/j.cpradiol.2024.07.007","volume":"53","author":"E Sacoransky","year":"2024","unstructured":"Sacoransky E, Kwan BYM, Soboleski D (2024) ChatGPT and assistive AI in structured radiology reporting: a systematic review. Curr Probl Diagn Radiol 53:728\u2013737. https:\/\/doi.org\/10.1067\/j.cpradiol.2024.07.007","journal-title":"Curr Probl Diagn Radiol"},{"key":"682_CR20","doi-asserted-by":"publisher","unstructured":"Adams LC, Truhn D, Busch F et al (2023) Leveraging GPT-4 for post hoc transformation of free-text radiology reports into structured reporting: a multilingual feasibility study. Radiology 307. https:\/\/doi.org\/10.1148\/radiol.230725","DOI":"10.1148\/radiol.230725"},{"key":"682_CR21","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-025-88925-1","volume":"15","author":"JR Tan","year":"2025","unstructured":"Tan JR, Lim DYZ, Le Q et al (2025) ChatGPT performance in assessing musculoskeletal MRI scan appropriateness based on ACR appropriateness criteria. Sci Rep 15:7140. https:\/\/doi.org\/10.1038\/s41598-025-88925-1","journal-title":"Sci Rep"},{"key":"682_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpbup.2024.100146","volume":"5","author":"M Khalifa","year":"2024","unstructured":"Khalifa M, Albadawy M (2024) AI in diagnostic imaging: revolutionising accuracy and efficiency. Comput Methods Programs Biomed Update 5:100146. https:\/\/doi.org\/10.1016\/j.cmpbup.2024.100146","journal-title":"Comput Methods Programs Biomed Update"},{"key":"682_CR23","doi-asserted-by":"publisher","first-page":"2096","DOI":"10.21037\/qims-23-1300","volume":"14","author":"CA Mallio","year":"2024","unstructured":"Mallio CA, Bernetti C, Sertorio AC, Zobel BB (2024) ChatGPT in radiology structured reporting: analysis of ChatGPT-3.5 Turbo and GPT-4 in reducing word count and recalling findings. Quant Imaging Med Surg 14:2096\u20132102. https:\/\/doi.org\/10.21037\/qims-23-1300","journal-title":"Quant Imaging Med Surg"},{"key":"682_CR24","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1016\/J.DIII.2024.04.003","volume":"105","author":"P Keshavarz","year":"2024","unstructured":"Keshavarz P, Bagherieh S, Nabipoorashrafi SA et al (2024) ChatGPT in radiology: a systematic review of performance, pitfalls, and future perspectives. Diagn Interv Imaging 105:251\u2013265. https:\/\/doi.org\/10.1016\/J.DIII.2024.04.003","journal-title":"Diagn Interv Imaging"},{"key":"682_CR25","doi-asserted-by":"publisher","first-page":"2589","DOI":"10.1007\/s00330-024-11107-6","volume":"35","author":"F Busch","year":"2024","unstructured":"Busch F, Hoffmann L, dos Santos DP et al (2024) Large language models for structured reporting in radiology: past, present, and future. Eur Radiol 35:2589\u20132602. https:\/\/doi.org\/10.1007\/s00330-024-11107-6","journal-title":"Eur Radiol"},{"key":"682_CR26","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1186\/S41747-024-00526-1\/FIGURES\/5","volume":"8","author":"R Maroncelli","year":"2024","unstructured":"Maroncelli R, Rizzo V, Pasculli M et al (2024) Probing clarity: AI-generated simplified breast imaging reports for enhanced patient comprehension powered by ChatGPT-4o. Eur Radiol Exp 8:124. https:\/\/doi.org\/10.1186\/S41747-024-00526-1\/FIGURES\/5","journal-title":"Eur Radiol Exp"},{"key":"682_CR27","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1186\/s41747-024-00485-7","volume":"8","author":"F Pesapane","year":"2024","unstructured":"Pesapane F, Cuocolo R, Sardanelli F (2024) The Picasso\u2019s skepticism on computer science and the dawn of generative AI: questions after the answers to keep \u201cmachines-in-the-loop. Eur Radiol Exp 8:81. https:\/\/doi.org\/10.1186\/s41747-024-00485-7","journal-title":"Eur Radiol Exp"},{"key":"682_CR28","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1186\/s41747-025-00569-y","volume":"9","author":"J Poto\u010dnik","year":"2025","unstructured":"Poto\u010dnik J, Thomas E, Kearney D, Killeen RP, Heffernan EJ, Foley SJ (2025) Can ChatGPT and Gemini justify brain CT referrals? A comparative study with human experts and a custom prediction model. Eur Radiol Exp 9:24. https:\/\/doi.org\/10.1186\/s41747-025-00569-y","journal-title":"Eur Radiol Exp"},{"key":"682_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/J.EJRAI.2025.100021","volume":"2","author":"J-P Kao","year":"2025","unstructured":"Kao J-P, Kao H-T (2025) Large language models in radiology: a technical and clinical perspective. Eur J Radiol Artif Intell 2:100021. https:\/\/doi.org\/10.1016\/J.EJRAI.2025.100021","journal-title":"Eur J Radiol Artif Intell"},{"key":"682_CR30","doi-asserted-by":"publisher","unstructured":"Mayes CJ, Reyes C, Truman ME et al (2025) Improving radiology reporting accuracy: use of GPT-4 to reduce errors in reports. Abdom Radiol (NY). https:\/\/doi.org\/10.1007\/S00261-025-05079-4","DOI":"10.1007\/S00261-025-05079-4"},{"key":"682_CR31","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1111\/1754-9485.13621","volume":"68","author":"HC Temperley","year":"2024","unstructured":"Temperley HC, O\u2019Sullivan NJ, Mac Curtain BM et al (2024) Current applications and future potential of ChatGPT in radiology: a systematic review. J Med Imaging Radiat Oncol 68:257\u2013264. https:\/\/doi.org\/10.1111\/1754-9485.13621","journal-title":"J Med Imaging Radiat Oncol"},{"key":"682_CR32","doi-asserted-by":"publisher","first-page":"2817","DOI":"10.1007\/s00330-023-10213-1","volume":"34","author":"K Jeblick","year":"2023","unstructured":"Jeblick K, Schachtner B, Dexl J et al (2023) ChatGPT makes medicine easy to swallow: an exploratory case study on simplified radiology reports. Eur Radiol 34:2817\u20132825. https:\/\/doi.org\/10.1007\/s00330-023-10213-1","journal-title":"Eur Radiol"},{"key":"682_CR33","doi-asserted-by":"publisher","unstructured":"Volin J, van Assen M, Bala W, Safdar N, Balthazar P (2025) Artificial intelligence and its effect on radiology residency education: current challenges, opportunities, and future directions. J Am Coll Radiol. https:\/\/doi.org\/10.1016\/j.jacr.2025.07.004","DOI":"10.1016\/j.jacr.2025.07.004"},{"key":"682_CR34","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1038\/s41746-024-01157-x","volume":"7","author":"J Haltaufderheide","year":"2024","unstructured":"Haltaufderheide J, Ranisch R (2024) The ethics of ChatGPT in medicine and healthcare: a systematic review on Large Language Models (LLMs). NPJ Digit Med 7:183. https:\/\/doi.org\/10.1038\/s41746-024-01157-x","journal-title":"NPJ Digit Med"}],"container-title":["European Radiology Experimental"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s41747-026-00682-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s41747-026-00682-6","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s41747-026-00682-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T22:06:02Z","timestamp":1771884362000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1186\/s41747-026-00682-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,23]]},"references-count":34,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["682"],"URL":"https:\/\/doi.org\/10.1186\/s41747-026-00682-6","relation":{},"ISSN":["2509-9280"],"issn-type":[{"value":"2509-9280","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,23]]},"assertion":[{"value":"5 August 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 January 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 February 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The study was approved by the Institutional Review Board (Comitato Etico Territoriale Lombardia 1, RETRORAD, CE: 61\/INT\/2017, 12\n th\n March 2025).","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Informed consent was waived due to the retrospective nature of the study.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"MZ, LA, SG, and LMS are members of the Scientific Editorial Board (Section Editor: information technology, social media and environmental sustainability, Abdomen\/gastrointestinal, Musculoskeletal, and Musculoskeletal, respectively) for\n European Radiology Experimental\n and as such did not participate in the selection or review processes for this article. The remaining authors declare no conflicts of interest.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"16"}}