{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T22:19:15Z","timestamp":1792793955203,"version":"4.5.0"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2025,8,8]],"date-time":"2025-08-08T00:00:00Z","timestamp":1754611200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,8,8]],"date-time":"2025-08-08T00:00:00Z","timestamp":1754611200000},"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"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>This study evaluated GPT-4\u2019s accuracy in MRI sequence selection based on radiology request forms (RRFs), comparing its performance to radiology residents.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and methods<\/jats:title>\n                    <jats:p>This retrospective study included 100 RRFs across four subspecialties (cardiac imaging, neuroradiology, musculoskeletal, and oncology). GPT-4 and two radiology residents (R1: 2 years, R2: 5 years MRI experience) selected sequences based on each patient\u2019s medical history and clinical questions. Considering imaging society guidelines, five board-certified specialized radiologists assessed protocols based on completeness, quality, and utility in consensus, using 5-point Likert scales. Clinical applicability was rated binarily by the institution\u2019s lead radiographer.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      GPT-4 achieved median scores of 3 (1\u20135) for completeness, 4 (1\u20135) for quality, and 4 (1\u20135) for utility, comparable to R1 (3 (1\u20135), 4 (1\u20135), 4 (1\u20135); each\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&gt;\u20090.05) but inferior to R2 (4 (1\u20135), 5 (1-5);\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.01, respectively, and 5 (1\u20135);\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001). Subspecialty protocol quality varied: GPT-4 matched R1 (4 (2\u20134) vs. 4 (2\u20135),\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.20) and R2 (4 (2\u20135);\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.47) in cardiac imaging; showed no differences in neuroradiology (all 5 (1\u20135),\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&gt;\u20090.05); scored lower than R1 and R2 in musculoskeletal imaging (3 (2\u20135) vs. 4 (3\u20135);\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.01, and 5 (3\u20135);\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001); and matched R1 (4 (1\u20135) vs. 2 (1\u20134),\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.12) as well as R2 (5 (2\u20135);\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.20) in oncology. GPT-4-based protocols were clinically applicable in 95% of cases, comparable to R1 (95%) and R2 (96%).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>GPT-4 generated MRI protocols with notable completeness, quality, utility, and clinical applicability, excelling in standardized subspecialties like cardiac and neuroradiology imaging while yielding lower accuracy in musculoskeletal examinations.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Key Points<\/jats:title>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Question<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>Long MRI acquisition times limit patient access, making accurate protocol selection crucial for efficient diagnostics, though it\u2019s time-consuming and error-prone, especially for inexperienced residents.<\/jats:italic>\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Findings<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>GPT-4 generated MRI protocols of remarkable yet inconsistent quality, performing on par with an experienced resident in standardized fields, but moderately in musculoskeletal examinations.<\/jats:italic>\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Clinical relevance<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>The large language model can assist less experienced radiologists in determining detailed MRI protocols and counteract increasing workloads. The model could function as a semi-automatic tool, generating MRI protocols for radiologists\u2019 confirmation, optimizing resource allocation, and improving diagnostics and cost-effectiveness.<\/jats:italic>\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Graphical Abstract<\/jats:title>\n                  <\/jats:sec>","DOI":"10.1007\/s00330-025-11888-4","type":"journal-article","created":{"date-parts":[[2025,8,8]],"date-time":"2025-08-08T07:44:57Z","timestamp":1754639097000},"page":"1541-1552","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["GPT-4 for automated sequence-level determination of MRI protocols based on radiology request forms from clinical routine"],"prefix":"10.1007","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-1068-8477","authenticated-orcid":false,"given":"Robert","family":"Terzis","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kenan","family":"Kaya","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas","family":"Sch\u00f6mig","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jan Paul","family":"Janssen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andra-Iza","family":"Iuga","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jonathan","family":"Kottlors","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Simon","family":"Lennartz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carsten","family":"Gietzen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cansin","family":"G\u00f6zdas","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lukas","family":"M\u00fcller","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robert","family":"Hahnfeldt","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Maintz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas","family":"Dratsch","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lenhard","family":"Pennig","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,8,8]]},"reference":[{"key":"11888_CR1","doi-asserted-by":"publisher","first-page":"e031454","DOI":"10.1161\/JAHA.123.031454","volume":"12","author":"P Martens","year":"2023","unstructured":"Martens P, Cooper LT, Tang WHW (2023) Diagnostic approach for suspected acute myocarditis: considerations for standardization and broadening clinical spectrum. J Am Heart Assoc 12:e031454","journal-title":"J Am Heart Assoc"},{"key":"11888_CR2","doi-asserted-by":"publisher","first-page":"757","DOI":"10.1093\/neuonc\/noaa030","volume":"22","author":"TJ Kaufmann","year":"2020","unstructured":"Kaufmann TJ, Smits M, Boxerman J et al (2020) Consensus recommendations for a standardized brain tumor imaging protocol for clinical trials in brain metastases. Neuro Oncol 22:757\u2013772","journal-title":"Neuro Oncol"},{"key":"11888_CR3","unstructured":"OECD (2024) Magnetic resonance imaging (MRI) exams (indicator). 17 Jul OECD"},{"key":"11888_CR4","doi-asserted-by":"publisher","first-page":"111418","DOI":"10.1016\/j.ejrad.2024.111418","volume":"175","author":"R Terzis","year":"2024","unstructured":"Terzis R, Dratsch T, Hahnfeldt R et al (2024) Five-minute knee MRI: an AI-based super resolution reconstruction approach for compressed sensing. A validation study on healthy volunteers. Eur J Radiol 175:111418","journal-title":"Eur J Radiol"},{"key":"11888_CR5","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1097\/RMR.0000000000000247","volume":"29","author":"MA Oztek","year":"2020","unstructured":"Oztek MA, Brunnquell CL, Hoff MN et al (2020) Practical considerations for radiologists in implementing a patient-friendly MRI experience. Top Magn Reson Imaging 29:181\u2013186","journal-title":"Top Magn Reson Imaging"},{"key":"11888_CR6","doi-asserted-by":"publisher","first-page":"844","DOI":"10.1016\/j.jacr.2009.08.003","volume":"6","author":"M Bernardy","year":"2009","unstructured":"Bernardy M, Ullrich CG, Rawson JV et al (2009) Strategies for managing imaging utilization. J Am Coll Radiol 6:844\u2013850","journal-title":"J Am Coll Radiol"},{"key":"11888_CR7","doi-asserted-by":"publisher","first-page":"1210","DOI":"10.1016\/j.jacr.2016.04.009","volume":"13","author":"A Schemmel","year":"2016","unstructured":"Schemmel A, Lee M, Hanley T et al (2016) Radiology workflow disruptors: a detailed analysis. J Am Coll Radiol 13:1210\u20131214","journal-title":"J Am Coll Radiol"},{"key":"11888_CR8","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1038\/s41571-021-00560-7","volume":"19","author":"K Bera","year":"2022","unstructured":"Bera K, Braman N, Gupta A et al (2022) Predicting cancer outcomes with radiomics and artificial intelligence in radiology. Nat Rev Clin Oncol 19:132\u2013146","journal-title":"Nat Rev Clin Oncol"},{"key":"11888_CR9","doi-asserted-by":"publisher","first-page":"103487","DOI":"10.1016\/j.compbiomed.2019.103487","volume":"115","author":"A Cl\u00e8rigues","year":"2019","unstructured":"Cl\u00e8rigues A, Valverde S, Bernal J et al (2019) Acute ischemic stroke lesion core segmentation in CT perfusion images using fully convolutional neural networks. Comput Biol Med 115:103487","journal-title":"Comput Biol Med"},{"key":"11888_CR10","doi-asserted-by":"publisher","first-page":"231","DOI":"10.3390\/diagnostics10040231","volume":"10","author":"AP Brady","year":"2020","unstructured":"Brady AP, Neri E (2020) Artificial intelligence in radiology-ethical considerations. Diagnostics (Basel) 10:231","journal-title":"Diagnostics (Basel)"},{"key":"11888_CR11","doi-asserted-by":"crossref","unstructured":"The Lancet Digital Health (2023) ChatGPT: friend or foe? Lancet Digit Health 5:e102","DOI":"10.1016\/S2589-7500(23)00023-7"},{"key":"11888_CR12","doi-asserted-by":"publisher","first-page":"e230877","DOI":"10.1148\/radiol.230877","volume":"307","author":"RJ Gertz","year":"2023","unstructured":"Gertz RJ, Bunck AC, Lennartz S et al (2023) GPT-4 for automated determination of radiological study and protocol based on radiology request forms: a feasibility study. Radiology 307:e230877","journal-title":"Radiology"},{"key":"11888_CR13","doi-asserted-by":"publisher","first-page":"e232640","DOI":"10.1148\/radiol.232640","volume":"312","author":"KR Laukamp","year":"2024","unstructured":"Laukamp KR, Terzis RA, Werner JM et al (2024) Monitoring patients with glioblastoma by using a large language model: accurate summarization of radiology reports with GPT-4. Radiology 312:e232640","journal-title":"Radiology"},{"key":"11888_CR14","doi-asserted-by":"publisher","first-page":"1293","DOI":"10.1007\/s10278-022-00610-1","volume":"35","author":"J Denck","year":"2022","unstructured":"Denck J, Haas O, Guehring J et al (2022) Automated protocoling for MRI exams-challenges and solutions. J Digit Imaging 35:1293\u20131302","journal-title":"J Digit Imaging"},{"key":"11888_CR15","doi-asserted-by":"publisher","first-page":"568","DOI":"10.1093\/jamia\/ocx125","volume":"25","author":"AD Brown","year":"2018","unstructured":"Brown AD, Marotta TR (2018) Using machine learning for sequence-level automated MRI protocol selection in neuroradiology. J Am Med Inform Assoc 25:568\u2013571","journal-title":"J Am Med Inform Assoc"},{"key":"11888_CR16","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1007\/s10278-016-9895-8","volume":"30","author":"PB Sachs","year":"2017","unstructured":"Sachs PB, Hunt K, Mansoubi F, Borgstede J (2017) CT and MR protocol standardization across a large health system: providing a consistent radiologist, patient, and referring provider experience. J Digit Imaging 30:11\u201316","journal-title":"J Digit Imaging"},{"key":"11888_CR17","doi-asserted-by":"publisher","first-page":"783","DOI":"10.1007\/s11547-019-01032-w","volume":"124","author":"S Cornacchia","year":"2019","unstructured":"Cornacchia S, Errico R, Balzano RF et al (2019) Medical radiological procedures: which information would be chosen for the report?. Radiol Med 124:783\u2013793","journal-title":"Radiol Med"},{"key":"11888_CR18","doi-asserted-by":"publisher","first-page":"396","DOI":"10.1007\/s10278-022-00589-9","volume":"35","author":"A Ma\u00f1as-Garc\u00eda","year":"2022","unstructured":"Ma\u00f1as-Garc\u00eda A, Gonz\u00e1lez-Valverde I, Camacho-Ramos E et al (2022) Radiological structured report integrated with quantitative imaging biomarkers and qualitative scoring systems. J Digit Imaging 35:396\u2013407","journal-title":"J Digit Imaging"},{"key":"11888_CR19","doi-asserted-by":"publisher","first-page":"1233","DOI":"10.1056\/NEJMsr2214184","volume":"388","author":"P Lee","year":"2023","unstructured":"Lee P, Bubeck S, Petro J (2023) Benefits, limits, and risks of GPT-4 as an AI Chatbot for medicine. N Engl J Med 388:1233\u20131239","journal-title":"N Engl J Med"},{"key":"11888_CR20","doi-asserted-by":"publisher","first-page":"681","DOI":"10.1007\/s11023-020-09548-1","volume":"30","author":"L Floridi","year":"2020","unstructured":"Floridi L, Chiriatti M (2020) GPT-3: its nature, scope, limits, and consequences. Mind Mach 30:681\u2013694","journal-title":"Mind Mach"},{"key":"11888_CR21","unstructured":"Brown TB, Mann B, Ryder N et al (2020) Language models are few-shot learners. Adv Neural Inf Process Syst. 1877\u20131901. https:\/\/arxiv.org\/abs\/2005.14165 (2020)."},{"key":"11888_CR22","doi-asserted-by":"publisher","first-page":"e45312","DOI":"10.2196\/45312","volume":"9","author":"A Gilson","year":"2023","unstructured":"Gilson A, Safranek CW, Huang T et al (2023) How does ChatGPT perform on the United States medical licensing examination? The implications of large language models for medical education and knowledge assessment. JMIR Med Educ 9:e45312","journal-title":"JMIR Med Educ"},{"key":"11888_CR23","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1016\/j.diii.2023.02.003","volume":"6","author":"A Lecler","year":"2023","unstructured":"Lecler A, Duron L, Soyer P (2023) Revolutionizing radiology with GPT-based models: current applications, future possibilities and limitations of ChatGPT. Diagn Intervent Imaging 6:269\u2013274-274","journal-title":"Diagn Intervent Imaging"},{"key":"11888_CR24","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1186\/s12968-020-00607-1","volume":"22","author":"CM Kramer","year":"2020","unstructured":"Kramer CM, Barkhausen J, Bucciarelli-Ducci C et al (2020) Standardized cardiovascular magnetic resonance imaging (CMR) protocols: 2020 update. J Cardiovasc Magn Reson 22:17","journal-title":"J Cardiovasc Magn Reson"},{"key":"11888_CR25","unstructured":"The American College of Radiology (2023) ACR\u2013ASNR\u2013SPR practice parameter for the performance of magnetic resonance imaging (MRI) of the head and neck. (Revised 2023 (Resolution 7)). The American College of Radiology"},{"key":"11888_CR26","unstructured":"The American College of Radiology (2023) ACR\u2013ASNR\u2013SPR practice parameter for the performance and interpretation of magnetic resonance imaging (MRI) of the brain. (Revised 2013 (Resolution 6)). The American College of Radiology"},{"key":"11888_CR27","doi-asserted-by":"publisher","first-page":"6590","DOI":"10.1007\/s00330-024-10706-7","volume":"34","author":"AP Parkar","year":"2024","unstructured":"Parkar AP, Adriaensen M (2024) ESR essentials: MRI of the knee-practice recommendations by ESSR. Eur Radiol 34:6590\u20136599","journal-title":"Eur Radiol"},{"key":"11888_CR28","unstructured":"European Society of Skeletal Radiology (2016) Guidelines for MR imaging of sports injuries\u2014spine. European Society of Skeletal Radiology"},{"key":"11888_CR29","unstructured":"European Society of Skeletal Radiology (2016) Guidelines for MR imaging of sports injuries\u2014pelvis. European Society of Skeletal Radiology"},{"key":"11888_CR30","unstructured":"European Society of Skeletal Radiology (2016) Guidelines for MR imaging of sports injuries\u2014shoulder. European Society of Skeletal Radiology"},{"key":"11888_CR31","doi-asserted-by":"publisher","first-page":"439","DOI":"10.6004\/jnccn.2021.0017","volume":"19","author":"MA Tempero","year":"2021","unstructured":"Tempero MA, Malafa MP, Al-Hawary M et al (2021) Pancreatic Adenocarcinoma, Version 2.2021, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw 19:439\u2013457","journal-title":"J Natl Compr Canc Netw"},{"key":"11888_CR32","doi-asserted-by":"publisher","first-page":"329","DOI":"10.6004\/jnccn.2021.0012","volume":"19","author":"AB Benson","year":"2021","unstructured":"Benson AB, Venook AP, Al-Hawary MM et al (2021) Colon Cancer, Version 2.2021, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw 19:329\u2013359","journal-title":"J Natl Compr Canc Netw"},{"key":"11888_CR33","doi-asserted-by":"publisher","first-page":"541","DOI":"10.6004\/jnccn.2021.0022","volume":"19","author":"AB Benson","year":"2021","unstructured":"Benson AB, D'Angelica MI, Abbott DE et al (2021) Hepatobiliary Cancers, Version 2.2021, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw 19:541\u2013565","journal-title":"J Natl Compr Canc Netw"},{"key":"11888_CR34","doi-asserted-by":"publisher","first-page":"171","DOI":"10.3390\/diagnostics14020171","volume":"14","author":"KH Lee","year":"2024","unstructured":"Lee KH, Lee RW (2024) ChatGPT\u2019s accuracy on magnetic resonance imaging basics: characteristics and limitations depending on the question type. Diagnostics (Basel) 14:171","journal-title":"Diagnostics (Basel)"},{"key":"11888_CR35","doi-asserted-by":"crossref","unstructured":"Busch F, Hoffmann L, Pinto Dos Santos D et al (2025) Large language models for structured reporting in radiology: past, present, and future. Eur Radiol 35:2589\u20132602","DOI":"10.1007\/s00330-024-11107-6"},{"key":"11888_CR36","doi-asserted-by":"crossref","unstructured":"Kottlors J, Bratke G, Rauen P et al (2023) Feasibility of differential diagnosis based on imaging patterns using a large language model. Radiology 308:231167","DOI":"10.1148\/radiol.231167"},{"key":"11888_CR37","unstructured":"The American College of Radiology (2020) ACR\u2013SPR\u2013SSR practice parameter for the performance and interpretation of magnetic resonance imaging (MRI) of the knee. (2020 Revision (Resolution 31)). The American College of Radiology"}],"container-title":["European Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00330-025-11888-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00330-025-11888-4","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00330-025-11888-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T09:25:54Z","timestamp":1772443554000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00330-025-11888-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,8]]},"references-count":37,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026,2]]}},"alternative-id":["11888"],"URL":"https:\/\/doi.org\/10.1007\/s00330-025-11888-4","relation":{},"ISSN":["1432-1084"],"issn-type":[{"value":"1432-1084","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,8]]},"assertion":[{"value":"1 April 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 June 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 July 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 August 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The scientific guarantor of this publication is Lenhard Pennig.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Guarantor"}},{"value":"S.L. is a member of the Scientific Editorial Board of European Radiology (Section: computer tomography). As such, they did not participate in the selection or review processes for this article. The remaining authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article. Conflicts of interest not related to this article: David Maintz and Lenhard Pennig received speaker\u2019s honoraria from Philips Healthcare.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Two authors have significant statistical expertise: Jonathan Kottlors, Institute for Diagnostic and Interventional Radiology, University Hospital Cologne, Cologne, Germany Thomas Dratsch, Institute for Diagnostic and Interventional Radiology, University Hospital Cologne, Cologne, Germany","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Statistics and biometry"}},{"value":"Written informed consent was waived by the Institutional Review Board.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"Institutional Review Board approval was obtained.","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"None.","order":7,"name":"Ethics","group":{"name":"EthicsHeading","label":"Study subjects or cohorts overlap"}},{"order":8,"name":"Ethics","group":{"name":"EthicsHeading","label":"Methodology"}}]}}