{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T12:49:34Z","timestamp":1781268574502,"version":"3.54.1"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2025,4,3]],"date-time":"2025-04-03T00:00:00Z","timestamp":1743638400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,4,3]],"date-time":"2025-04-03T00:00:00Z","timestamp":1743638400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"New Technology and Business of Chinese People's Liberation Army General Hospital","award":["No.20230116"],"award-info":[{"award-number":["No.20230116"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Eur Radiol"],"DOI":"10.1007\/s00330-025-11419-1","type":"journal-article","created":{"date-parts":[[2025,4,5]],"date-time":"2025-04-05T05:08:21Z","timestamp":1743829701000},"page":"6302-6312","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["An interpretable radiomics-based machine learning model for predicting reverse left ventricular remodeling in STEMI patients using late gadolinium enhancement of myocardial scar"],"prefix":"10.1007","volume":"35","author":[{"given":"Xiuzheng","family":"Yue","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianing","family":"Cui","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sicong","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenjia","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Qi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kunlun","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6689-2951","authenticated-orcid":false,"given":"Tao","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,4,3]]},"reference":[{"key":"11419_CR1","doi-asserted-by":"publisher","first-page":"782","DOI":"10.1016\/j.jchf.2019.06.004","volume":"7","author":"A Aimo","year":"2019","unstructured":"Aimo A, Gaggin HK, Barison A, Emdin M, Januzzi JL (2019) Imaging, biomarker, and clinical predictors of cardiac remodeling in heart failure with reduced ejection fraction. JACC Heart Fail 7:782\u2013794","journal-title":"JACC Heart Fail"},{"key":"11419_CR2","doi-asserted-by":"publisher","DOI":"10.1186\/s12968-016-0314-6","volume":"19","author":"O Huttin","year":"2017","unstructured":"Huttin O, Mandry D, Eschalier R et al (2017) Cardiac remodeling following reperfused acute myocardial infarction is linked to the concomitant evolution of vascular function as assessed by cardiovascular magnetic resonance. J Cardiovasc Magn Reson 19:2","journal-title":"J Cardiovasc Magn Reson"},{"key":"11419_CR3","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1007\/s00392-020-01649-2","volume":"110","author":"M Reindl","year":"2020","unstructured":"Reindl M, Tiller C, Holzknecht M et al (2020) Global longitudinal strain by feature tracking for optimized prediction of adverse remodeling after ST-elevation myocardial infarction. Clin Res Cardiol 110:61\u201371","journal-title":"Clin Res Cardiol"},{"key":"11419_CR4","doi-asserted-by":"publisher","first-page":"1196","DOI":"10.1093\/eurheartj\/ehv529","volume":"37","author":"OA Smiseth","year":"2015","unstructured":"Smiseth OA, Torp H, Opdahl A, Haugaa KH, Urheim S (2015) Myocardial strain imaging: How useful is it in clinical decision making? Eur Heart J 37:1196\u20131207","journal-title":"Eur Heart J"},{"key":"11419_CR5","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1038\/nrcardio.2011.172","volume":"9","author":"N Koitabashi","year":"2011","unstructured":"Koitabashi N, Kass DA (2011) Reverse remodeling in heart failure\u2014mechanisms and therapeutic opportunities. Nat Rev Cardiol 9:147\u2013157","journal-title":"Nat Rev Cardiol"},{"key":"11419_CR6","doi-asserted-by":"publisher","first-page":"2582","DOI":"10.1093\/eurheartj\/eht238","volume":"34","author":"OA Breithardt","year":"2013","unstructured":"Breithardt OA (2013) Reversing heart failure by CRT: How long do the effects last? Eur Heart J 34:2582\u20132584","journal-title":"Eur Heart J"},{"key":"11419_CR7","doi-asserted-by":"publisher","first-page":"2465","DOI":"10.1016\/j.jacc.2012.06.062","volume":"60","author":"DL Mann","year":"2012","unstructured":"Mann DL, Barger PM, Burkhoff D (2012) Myocardial recovery and the failing heart. J Am Coll Cardiol 60:2465\u20132472","journal-title":"J Am Coll Cardiol"},{"key":"11419_CR8","doi-asserted-by":"publisher","first-page":"612","DOI":"10.1007\/s00392-018-1387-z","volume":"108","author":"HE Groot","year":"2018","unstructured":"Groot HE, Al Ali L, van der Horst ICC et al (2018) Plasma interleukin 6 levels are associated with cardiac function after ST-elevation myocardial infarction. Clin Res Cardiol 108:612\u2013621","journal-title":"Clin Res Cardiol"},{"key":"11419_CR9","doi-asserted-by":"publisher","first-page":"681","DOI":"10.1097\/HCO.0000000000000227","volume":"30","author":"SJ Reinstadler","year":"2015","unstructured":"Reinstadler SJ, Thiele H, Eitel I (2015) Risk stratification by cardiac magnetic resonance imaging after ST-elevation myocardial infarction. Curr Opin Cardiol 30:681\u2013689","journal-title":"Curr Opin Cardiol"},{"key":"11419_CR10","doi-asserted-by":"publisher","first-page":"8501","DOI":"10.1007\/s00330-023-09907-3","volume":"33","author":"J Cui","year":"2023","unstructured":"Cui J, Zhao Y, Qian G, Yue X, Luo C, Li T (2023) Cardiac magnetic resonance for the early prediction of reverse left ventricular remodeling in patients with ST-segment elevation myocardial infarction. Eur Radiol 33:8501\u20138512","journal-title":"Eur Radiol"},{"key":"11419_CR11","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1148\/radiol.2015142674","volume":"278","author":"V Bodi","year":"2016","unstructured":"Bodi V, Monmeneu JV, Ortiz-Perez JT et al (2016) Prediction of reverse remodeling at cardiac MR imaging soon after first ST-segment\u2013elevation myocardial infarction: results of a large prospective registry. Radiology 278:54\u201363","journal-title":"Radiology"},{"key":"11419_CR12","doi-asserted-by":"publisher","first-page":"476","DOI":"10.1002\/jmri.27047","volume":"52","author":"BH Chen","year":"2020","unstructured":"Chen BH, An DA, He J, Xu JR, Wu LM, Pu J (2020) Myocardial extracellular volume fraction allows differentiation of reversible versus irreversible myocardial damage and prediction of adverse left ventricular remodeling of ST-elevation myocardial infarction. J Magn Reson Imaging 52:476\u2013487","journal-title":"J Magn Reson Imaging"},{"key":"11419_CR13","doi-asserted-by":"publisher","first-page":"535","DOI":"10.3348\/kjr.2019.0969","volume":"22","author":"Q Ma","year":"2021","unstructured":"Ma Q, Ma Y, Yu T, Sun Z, Hou Y (2021) Radiomics of non-contrast-enhanced T1 mapping: diagnostic and predictive performance for myocardial injury in acute ST-segment-elevation myocardial infarction. Korean J Radiol 22:535","journal-title":"Korean J Radiol"},{"key":"11419_CR14","doi-asserted-by":"publisher","first-page":"1274","DOI":"10.1007\/s00330-019-06427-x","volume":"30","author":"P Nie","year":"2019","unstructured":"Nie P, Yang G, Wang Z et al (2019) A CT-based radiomics nomogram for differentiation of renal angiomyolipoma without visible fat from homogeneous clear cell renal cell carcinoma. Eur Radiol 30:1274\u20131284","journal-title":"Eur Radiol"},{"key":"11419_CR15","doi-asserted-by":"publisher","first-page":"3725","DOI":"10.1007\/s00330-019-06142-7","volume":"29","author":"G-W Ji","year":"2019","unstructured":"Ji G-W, Zhu F-P, Zhang Y-D et al (2019) A radiomics approach to predict lymph node metastasis and clinical outcome of intrahepatic cholangiocarcinoma. Eur Radiol 29:3725\u20133735","journal-title":"Eur Radiol"},{"key":"11419_CR16","doi-asserted-by":"publisher","first-page":"976","DOI":"10.1007\/s00330-019-06398-z","volume":"30","author":"Y Wang","year":"2019","unstructured":"Wang Y, Liu W, Yu Y et al (2019) CT radiomics nomogram for the preoperative prediction of lymph node metastasis in gastric cancer. Eur Radiol 30:976\u2013986","journal-title":"Eur Radiol"},{"key":"11419_CR17","doi-asserted-by":"crossref","unstructured":"Lewis M, Elad G, Beladev M et al (2021) Comparison of deep learning with traditional models to predict preventable acute care use and spending among heart failure patients. Sci Rep 11:1164","DOI":"10.1038\/s41598-020-80856-3"},{"key":"11419_CR18","doi-asserted-by":"crossref","unstructured":"Ribeiro M, Singh S, Guestrin C (2016) \u201cWhy Should I Trust You?\u201d: explaining the predictions of any classifier. In: Proceedings of the 2016 conference of the North American chapter of the Association for Computational Linguistics: Demonstrations. Association for Computational Linguistics, pp 1135\u20131144","DOI":"10.18653\/v1\/N16-3020"},{"key":"11419_CR19","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1038\/s41551-018-0304-0","volume":"2","author":"SM Lundberg","year":"2018","unstructured":"Lundberg SM, Nair B, Vavilala MS et al (2018) Explainable machine-learning predictions for the prevention of hypoxaemia during surgery. Nat Biomed Eng 2:749\u2013760","journal-title":"Nat Biomed Eng"},{"key":"11419_CR20","doi-asserted-by":"publisher","first-page":"424","DOI":"10.1136\/heartjnl-2012-302976","volume":"100","author":"HD White","year":"2013","unstructured":"White HD, Thygesen K, Alpert JS, Jaffe AS (2013) Clinical implications of the third universal definition of myocardial infarction. Heart 100:424\u2013432","journal-title":"Heart"},{"key":"11419_CR21","doi-asserted-by":"publisher","first-page":"328","DOI":"10.1148\/radiol.2020191145","volume":"295","author":"A Zwanenburg","year":"2020","unstructured":"Zwanenburg A, Valli\u00e8res M, Abdalah MA et al (2020) The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology 295:328\u2013338","journal-title":"Radiology"},{"key":"11419_CR22","doi-asserted-by":"publisher","first-page":"e180075","DOI":"10.1148\/ryai.2019180075","volume":"1","author":"R Du","year":"2019","unstructured":"Du R, Lee VH, Yuan H et al (2019) Radiomics model to predict early progression of nonmetastatic nasopharyngeal carcinoma after intensity modulation radiation therapy: a multicenter study. Radiol Artif Intell 1:e180075","journal-title":"Radiol Artif Intell"},{"key":"11419_CR23","doi-asserted-by":"crossref","unstructured":"Patil I (2021) Visualizations with statistical details: the \u2018ggstatsplot\u2019 approach. J Open Source Softw 6:3167","DOI":"10.21105\/joss.03167"},{"key":"11419_CR24","unstructured":"Huang H, Zhou L, Chen J, Wei T (2020) ggcor: extended tools for correlation analysis and visualization. https:\/\/github.com\/houyunhuang\/ggcor"},{"key":"11419_CR25","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1016\/j.jcmg.2023.07.005","volume":"17","author":"A Kiaos","year":"2024","unstructured":"Kiaos A, Daskalopoulos GN, Kamperidis V, Ziakas A, Efthimiadis G, Karamitsos TD (2024) Quantitative late gadolinium enhancement cardiac magnetic resonance and sudden death in hypertrophic cardiomyopathy: a meta-analysis. JACC Cardiovasc Imaging 17:489\u2013497","journal-title":"JACC Cardiovasc Imaging"},{"key":"11419_CR26","doi-asserted-by":"publisher","DOI":"10.1186\/s12968-020-00682-4","volume":"22","author":"T Leiner","year":"2020","unstructured":"Leiner T, Bogaert J, Friedrich MG et al (2020) SCMR position paper (2020) on clinical indications for cardiovascular magnetic resonance. J Cardiovas Magn Reson 22:76","journal-title":"J Cardiovas Magn Reson"},{"key":"11419_CR27","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1148\/radiol.2018180411","volume":"289","author":"B Baessler","year":"2018","unstructured":"Baessler B, Luecke C, Lurz J et al (2018) Cardiac MRI texture analysis of T1 and T2 maps in patients with infarctlike acute myocarditis. Radiology 289:357\u2013365","journal-title":"Radiology"},{"key":"11419_CR28","doi-asserted-by":"publisher","first-page":"827","DOI":"10.3348\/kjr.2023.0061","volume":"24","author":"A Xin","year":"2023","unstructured":"Xin A, Liu M, Chen T et al (2023) Non-contrast cine cardiac magnetic resonance derived-radiomics for the prediction of left ventricular adverse remodeling in patients with ST-segment elevation myocardial infarction. Korean J Radiol 24:827","journal-title":"Korean J Radiol"},{"key":"11419_CR29","doi-asserted-by":"publisher","first-page":"2549","DOI":"10.1093\/eurheartj\/ehac223","volume":"43","author":"S Frantz","year":"2022","unstructured":"Frantz S, Hundertmark MJ, Schulz-Menger J, Bengel FM, Bauersachs J (2022) Left ventricular remodelling post-myocardial infarction: pathophysiology, imaging, and novel therapies. Eur Heart J 43:2549\u20132561","journal-title":"Eur Heart J"},{"key":"11419_CR30","doi-asserted-by":"publisher","first-page":"922","DOI":"10.1007\/s11547-023-01658-x","volume":"128","author":"T Polidori","year":"2023","unstructured":"Polidori T, De Santis D, Rucci C et al (2023) Radiomics applications in cardiac imaging: a comprehensive review. Radiol Med 128:922\u2013933","journal-title":"Radiol Med"},{"key":"11419_CR31","doi-asserted-by":"publisher","first-page":"591368","DOI":"10.3389\/fcvm.2020.591368","volume":"7","author":"I Cetin","year":"2020","unstructured":"Cetin I, Raisi-Estabragh Z, Petersen SE et al (2020) Radiomics signatures of cardiovascular risk factors in cardiac MRI: results from the UK Biobank. Front Cardiovasc Med 7:591368","journal-title":"Front Cardiovasc Med"},{"key":"11419_CR32","doi-asserted-by":"publisher","first-page":"2704","DOI":"10.1016\/j.acra.2024.03.032","volume":"31","author":"J Deng","year":"2024","unstructured":"Deng J, Zhou L, Li Y et al (2024) Integration of cine-cardiac magnetic resonance radiomics and machine learning for differentiating ischemic and dilated cardiomyopathy. Acad Radiol 31:2704\u20132714","journal-title":"Acad Radiol"},{"key":"11419_CR33","doi-asserted-by":"publisher","first-page":"2312","DOI":"10.1007\/s00330-022-09236-x","volume":"33","author":"X Zhang","year":"2023","unstructured":"Zhang X, Cui C, Zhao S, Xie L, Tian Y (2023) Cardiac magnetic resonance radiomics for disease classification. Eur Radiol 33:2312\u20132323","journal-title":"Eur Radiol"},{"key":"11419_CR34","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1038\/nrclinonc.2017.141","volume":"14","author":"P Lambin","year":"2017","unstructured":"Lambin P, Leijenaar RTH, Deist TM et al (2017) Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 14:749\u2013762","journal-title":"Nat Rev Clin Oncol"},{"key":"11419_CR35","doi-asserted-by":"publisher","first-page":"4615","DOI":"10.1007\/s00330-018-5391-5","volume":"28","author":"S Cheng","year":"2018","unstructured":"Cheng S, Fang M, Cui C et al (2018) LGE-CMR-derived texture features reflect poor prognosis in hypertrophic cardiomyopathy patients with systolic dysfunction: preliminary results. Eur Radiol 28:4615\u20134624","journal-title":"Eur Radiol"},{"key":"11419_CR36","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2020.610691","volume":"10","author":"J Zhang","year":"2020","unstructured":"Zhang J, Jin J, Ai Y et al (2020) Computer tomography radiomics-based nomogram in the survival prediction for brain metastases from non-small cell lung cancer underwent whole brain radiotherapy. Front Oncol 10:610691","journal-title":"Front Oncol"}],"container-title":["European Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00330-025-11419-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00330-025-11419-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00330-025-11419-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,10]],"date-time":"2025-09-10T10:11:05Z","timestamp":1757499065000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00330-025-11419-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,3]]},"references-count":36,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2025,10]]}},"alternative-id":["11419"],"URL":"https:\/\/doi.org\/10.1007\/s00330-025-11419-1","relation":{},"ISSN":["1432-1084"],"issn-type":[{"value":"1432-1084","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,3]]},"assertion":[{"value":"10 August 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 November 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 December 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 April 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 Tao Li.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Guarantor"}},{"value":"S.H. provides consultancy to and owns stock of Philips Healthcare, Beijing, China. The other authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"No complex statistical methods were necessary for this paper.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Statistics and biometry"}},{"value":"Written informed consent was not required for this study because this is a retrospective study.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"This retrospective study was approved by the Institutional Review Board of Chinese PLA General Hospital (S2022-567-01) and was conducted following the principles of the Declaration of Helsinki.","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Some study subjects or cohorts have been previously reported by Cui et al [].","order":7,"name":"Ethics","group":{"name":"EthicsHeading","label":"Study subjects or cohorts overlap"}},{"order":8,"name":"Ethics","group":{"name":"EthicsHeading","label":"Methodology"}}]}}