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 7:782–794
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
PubMed PubMed Central Google Scholar
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 110:61–71
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 37:1196–1207
PubMed PubMed Central Google Scholar
Koitabashi N, Kass DA (2011) Reverse remodeling in heart failure—mechanisms and therapeutic opportunities. Nat Rev Cardiol 9:147–157
Breithardt OA (2013) Reversing heart failure by CRT: How long do the effects last? Eur Heart J 34:2582–2584
Mann DL, Barger PM, Burkhoff D (2012) Myocardial recovery and the failing heart. J Am Coll Cardiol 60:2465–2472
PubMed PubMed Central Google Scholar
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 108:612–621
PubMed PubMed Central Google Scholar
Reinstadler SJ, Thiele H, Eitel I (2015) Risk stratification by cardiac magnetic resonance imaging after ST-elevation myocardial infarction. Curr Opin Cardiol 30:681–689
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 33:8501–8512
Bodi V, Monmeneu JV, Ortiz-Perez JT et al (2016) Prediction of reverse remodeling at cardiac MR imaging soon after first ST-segment–elevation myocardial infarction: results of a large prospective registry. Radiology 278:54–63
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 52:476–487
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
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 30:1274–1284
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 29:3725–3735
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 30:976–986
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
Ribeiro M, Singh S, Guestrin C (2016) “Why Should I Trust You?”: 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 1135–1144
Lundberg SM, Nair B, Vavilala MS et al (2018) Explainable machine-learning predictions for the prevention of hypoxaemia during surgery. Nat Biomed Eng 2:749–760
PubMed PubMed Central Google Scholar
White HD, Thygesen K, Alpert JS, Jaffe AS (2013) Clinical implications of the third universal definition of myocardial infarction. Heart 100:424–432
Zwanenburg A, Vallières M, Abdalah MA et al (2020) The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology 295:328–338
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
PubMed PubMed Central Google Scholar
Patil I (2021) Visualizations with statistical details: the ‘ggstatsplot’ approach. J Open Source Softw 6:3167
Huang H, Zhou L, Chen J, Wei T (2020) ggcor: extended tools for correlation analysis and visualization. https://github.com/houyunhuang/ggcor
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 17:489–497
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
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 289:357–365
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
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 43:2549–2561
CAS PubMed PubMed Central Google Scholar
Polidori T, De Santis D, Rucci C et al (2023) Radiomics applications in cardiac imaging: a comprehensive review. Radiol Med 128:922–933
PubMed PubMed Central Google Scholar
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
CAS PubMed PubMed Central Google Scholar
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 31:2704–2714
Zhang X, Cui C, Zhao S, Xie L, Tian Y (2023) Cardiac magnetic resonance radiomics for disease classification. Eur Radiol 33:2312–2323
Lambin P, Leijenaar RTH, Deist TM et al (2017) Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 14:749–762
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 28:4615–4624
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
Comments (0)