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Review
Advances in the application of CMR based radiomics in cardiac diseases
ZHOU Lei  WU Yousen  ZHANG Qian  ZHOU Boqi 

Cite this article as: ZHOU L, WU Y S, ZHANG Q, et al. Advances in the application of CMR based radiomics in cardiac diseases[J]. Chin J Magn Reson Imaging, 2025, 16(10): 164-170. DOI:10.12015/issn.1674-8034.2025.10.026.


[Abstract] Cardiovascular diseases, ranking among the leading causes of death globally, exhibit heterogeneous spectrum of clinical phenotypes. They are often insidious in onset, frequently progressing to an irreversible stage by the time of diagnosis, therapeutic effectiveness and impairing patients' quality of life. Early and precise diagnosis is therefore a critical strategy for improving prognosis. Traditional cardiac magnetic resonance (CMR) analysis methods have limitations in utilizing image information, The emergence of CMR radiomics has introduced a breakthrough in the diagnosis and treatment of cardiac diseases. This advanced technique enables the extraction of high-dimensional features from medical images, demonstrating significant advantages in the phenotyping, severity assessment, and progression evaluation of various cardiac conditions. However, current studies are mostly based on single-center, small-sample cohorts and lack external validation; imaging scan parameters and post-processing procedures have not been standardized, the reproducibility and biological interpretability of radiomics features remain insufficient, and models are still at the offline validation stage, lacking evidence for real-time decision support integrated with clinical workflows, which severely limits the speed and scope of translating CMR radiomics into clinical practice. In view of this,this review aims to review the key technical advances and clinical translations of CMR radiomics in the diagnosis and prognosis prediction of various cardiac conditions. It will also analyze current challenges and future directions, with the goal of providing an evidence-based foundation for clinical practice. Ultimately, this review seeks to foster early disease identification and improved prognosis management, thereby enhancing patient quality of life and clinical outcomes.
[Keywords] cardiac diseases;radiomics;cardiac magnetic resonance;magnetic resonance imaging;diagnosis;prognostic evaluation

ZHOU Lei   WU Yousen*   ZHANG Qian   ZHOU Boqi  

Image Center, Affiliated Hospital of Qinghai University, Xining 810001, China

Corresponding author: WU Y S, E-mail: wuyousen@163.com

Conflicts of interest   None.

Received  2025-08-04
Accepted  2025-10-08
DOI: 10.12015/issn.1674-8034.2025.10.026
Cite this article as: ZHOU L, WU Y S, ZHANG Q, et al. Advances in the application of CMR based radiomics in cardiac diseases[J]. Chin J Magn Reson Imaging, 2025, 16(10): 164-170. DOI:10.12015/issn.1674-8034.2025.10.026.

[1]
WANG M S, DENG J W, GENG W Y, et al. Temporal trend and attributable risk factors of cardiovascular disease burden for adults 55 years and older in 204 countries/territories from 1990 to 2021: an analysis for the Global Burden of Disease Study 2021[J]. Eur J Prev Cardiol, 2025, 32(7): 539-552. DOI: 10.1093/eurjpc/zwae384.
[2]
LIU M B, HE X Y, YANG X H, et al. Interpretation of report on cardiovascular health and diseases in China 2023[J]. Chin J Interv Cardiol, 2024, 32(10): 541-550. DOI: 10.3969/j.issn.1004-8812.2024.10.001.
[3]
National Center for Cardiovascular Diseases, The Writing Committee of the Report on Cardiovascular Health, CHINA D I. Report on cardiovascular health and diseases in China 2024: an updated summary[J]. Chin Circ J, 2025, 40(6): 521-559. DOI: 10.3969/j.issn.1000-3614.2025.06.001.
[4]
TIAN P, HAN B, ZHENG J M, et al. Multimodal imaging features of cardiac lipoma[J]. Chin J Med Imag, 2024, 32(11): 1129-1133. DOI: 10.3969/j.issn.1005-5185.2024.11.007.
[5]
GRECH N, ABELA M. The role of cardiovascular magnetic resonance imaging in athletic individuals-a narrative review[J/OL]. J Clin Med, 2025, 14(10): 3576 [2025-08-03]. https://pubmed.ncbi.nlm.nih.gov/40429571/. DOI: 10.3390/jcm14103576.
[6]
SHUR J D, DORAN S J, KUMAR S, et al. Radiomics in oncology: a practical guide[J]. Radiographics, 2021, 41(6): 1717-1732. DOI: 10.1148/rg.2021210037.
[7]
INCHINGOLO R, MAINO C, CANNELLA R, et al. Radiomics in colorectal cancer patients[J]. World J Gastroenterol, 2023, 29(19): 2888-2904. DOI: 10.3748/wjg.v29.i19.2888.
[8]
AVARD E, SHIRI I, HAJIANFAR G, et al. Non-contrast Cine Cardiac Magnetic Resonance image radiomics features and machine learning algorithms for myocardial infarction detection[J/OL]. Comput Biol Med, 2022, 141: 105145 [2025-08-03]. https://pubmed.ncbi.nlm.nih.gov/34929466/. DOI: 10.1016/j.compbiomed.2021.105145.
[9]
MA Z P, WANG S W, XUE L Y, et al. A study on the application of radiomics based on cardiac MR non-enhanced cine sequence in the early diagnosis of hypertensive heart disease[J/OL]. BMC Med Imaging, 2024, 24(1): 124 [2025-08-03]. https://pubmed.ncbi.nlm.nih.gov/38802736/. DOI: 10.1186/s12880-024-01301-9.
[10]
WANG P F, REN T Y, ZHANG C Y, et al. Research progress of radiomics in cardiovascular magnetic resonance[J]. J Imag Res Med Appl, 2024, 8(5): 1-3, 7. DOI: 10.3969/j.issn.2096-3807.2024.05.001.
[11]
POLIDORI T, DE SANTIS D, RUCCI C, et al. Radiomics applications in cardiac imaging: a comprehensive review[J]. Radiol Med, 2023, 128(8): 922-933. DOI: 10.1007/s11547-023-01658-x.
[12]
SONG Y J, ZHANG W Q, ZHAO X Y, et al. Mechanism and CMR imaging evaluation of ventricular arrhythmia after myocardial infarction caused by myocardial infarction marginal zone[J]. J Clin Radiol, 2025, 44(2): 372-374. DOI: 10.13437/j.cnki.jcr.2025.02.020.
[13]
CHEN Y, ZHOU Z, GAO Y F, et al. Application value of imageology in myocardial infarction[J]. Chin Imag J Integr Tradit West Med, 2024, 22(4): 480-483, 493. DOI: 10.3969/j.issn.1672-0512.2024.04.024.
[14]
LI G, ZHENG C, CUI Y D, et al. Diagnostic efficacy of complexity metrics from cardiac MRI myocardial segmental motion curves in detecting late gadolinium enhancement in myocardial infarction patients[J/OL]. Heliyon, 2024, 10(11): e31889 [2025-08-03]. https://pubmed.ncbi.nlm.nih.gov/38912500/. DOI: 10.1016/j.heliyon.2024.e31889.
[15]
JIMÉNEZ-JARA C, SALAS R, DÍAZ-NAVARRO R, et al. AI applied to cardiac magnetic resonance for precision medicine in coronary artery disease: a systematic review[J/OL]. J Cardiovasc Dev Dis, 2025, 12(9): 345 [2025-08-03]. https://pubmed.ncbi.nlm.nih.gov/41002624/. DOI: 10.3390/jcdd12090345.
[16]
DEL BUONO M G, MORONI F, MONTONE R A, et al. Ischemic cardiomyopathy and heart failure after acute myocardial infarction[J]. Curr Cardiol Rep, 2022, 24(10): 1505-1515. DOI: 10.1007/s11886-022-01766-6.
[17]
RAUSEO E, IZQUIERDO MORCILLO C, RAISI-ESTABRAGH Z, et al. New imaging signatures of cardiac alterations in ischaemic heart disease and cerebrovascular disease using CMR radiomics[J/OL]. Front Cardiovasc Med, 2021, 8: 716577 [2025-08-03]. https://pubmed.ncbi.nlm.nih.gov/34631820/. DOI: 10.3389/fcvm.2021.716577.
[18]
LASODE J, CHANTAKSINOPAS W, KHONGWIROTPHAN S, et al. Radiomics for differential diagnosis of ischemic and dilated cardiomyopathy using non-contrast-enhanced cine cardiac magnetic resonance imaging[J]. Radiol Med, 2025, 130(5): 650-661. DOI: 10.1007/s11547-025-01979-z.
[19]
DENG J, ZHOU L T, LI Y Y, et al. Integration of cine-cardiac magnetic resonance radiomics and machine learning for differentiating ischemic and dilated cardiomyopathy[J]. Acad Radiol, 2024, 31(7): 2704-2714. DOI: 10.1016/j.acra.2024.03.032.
[20]
CAÑO-CARRILLO S, LOZANO-VELASCO E, CASTILLO-CASAS J M, et al. The role of ncRNAs in cardiac infarction and regeneration[J/OL]. J Cardiovasc Dev Dis, 2023, 10(3): 123 [2025-08-03]. https://pubmed.ncbi.nlm.nih.gov/36975887/. DOI: 10.3390/jcdd10030123.
[21]
PLAYFORD D, STEWART S, HARRIS S A, et al. Pattern and prognostic impact of regional wall motion abnormalities in 255 697 men and 236 641 women investigated with echocardiography[J/OL]. J Am Heart Assoc, 2023, 12(22): e031243 [2025-08-03]. https://pubmed.ncbi.nlm.nih.gov/37947119/. DOI: 10.1161/JAHA.123.031243.
[22]
DI BELLA G, AQUARO G D, BOGAERT J, et al. Non-transmural myocardial infarction associated with regional contractile function is an independent predictor of positive outcome: an integrated approach to myocardial viability[J/OL]. J Cardiovasc Magn Reson, 2021, 23(1): 121 [2025-08-03]. https://pubmed.ncbi.nlm.nih.gov/34719402/. DOI: 10.1186/s12968-021-00818-0.
[23]
LU Z, SONG B X, LIU X, et al. Factors predicting resolution of left ventricular thrombus in different time windows after myocardial infarction[J/OL]. BMC Cardiovasc Disord, 2024, 24(1): 278 [2025-08-03]. https://pubmed.ncbi.nlm.nih.gov/38811882/. DOI: 10.1186/s12872-024-03898-9.
[24]
LUO Q, LI Z B, LIU B, et al. Hydrogel formulations for orthotopic treatment of myocardial infarction[J]. Expert Opin Drug Deliv, 2024, 21(10): 1463-1478. DOI: 10.1080/17425247.2024.2409906.
[25]
PUJADAS E R, RAISI-ESTABRAGH Z, SZABO L, et al. Prediction of incident cardiovascular events using machine learning and CMR radiomics[J]. Eur Radiol, 2023, 33(5): 3488-3500. DOI: 10.1007/s00330-022-09323-z.
[26]
VANDE BERG B, DE KEYZER F, CERNICANU A, et al. Radiomics-based detection of acute myocardial infarction on noncontrast enhanced midventricular short-axis cine CMR images[J]. Int J Cardiovasc Imag, 2024, 40(6): 1211-1220. DOI: 10.1007/s10554-024-03089-9.
[27]
BAESSLER B, MANNIL M, OEBEL S, et al. Subacute and chronic left ventricular myocardial scar: accuracy of texture analysis on nonenhanced cine MR images[J]. Radiology, 2018, 286(1): 103-112. DOI: 10.1148/radiol.2017170213.
[28]
LENSELINK C, RICKEN K W L M, GROOT H E, et al. Incidence and predictors of heart failure with reduced and preserved ejection fraction after ST-elevation myocardial infarction in the contemporary era of early percutaneous coronary intervention[J]. Eur J Heart Fail, 2024, 26(5): 1142-1149. DOI: 10.1002/ejhf.3225.
[29]
YANG M X, HE Y, MA M, et al. Characterization of infarcted myocardium by T1-mapping and its association with left ventricular remodeling[J/OL]. Eur J Radiol, 2021, 137: 109590 [2025-08-03]. https://pubmed.ncbi.nlm.nih.gov/33607372/. DOI: 10.1016/j.ejrad.2021.109590.
[30]
CHEN B H, AN D A, HE J, et al. Myocardial extracellular volume fraction radiomics analysis for differentiation of reversible versus irreversible myocardial damage and prediction of left ventricular adverse remodeling after ST-elevation myocardial infarction[J]. Eur Radiol, 2021, 31(1): 504-514. DOI: 10.1007/s00330-020-07117-9.
[31]
YUE X Z, CUI J N, HUANG S C, et al. An interpretable radiomics-based machine learning model for predicting reverse left ventricular remodeling in STEMI patients using late gadolinium enhancement of myocardial scar[J]. Eur Radiol, 2025, 35(10): 6302-6312. DOI: 10.1007/s00330-025-11419-1.
[32]
XIN A, LIU M L, CHEN T, et al. Non-contrast cine cardiac magnetic resonance derived-radiomics for the prediction of left ventricular adverse remodeling in patients with ST-segment elevation myocardial infarction[J]. Korean J Radiol, 2023, 24(9): 827-837. DOI: 10.3348/kjr.2023.0061.
[33]
WANG Z T, REN J, ZHOU P, et al. Application of radiomics based on cardiac magnetic resonance to non-ischemic cardiomyopathy: a research progress[J]. Guangxi Med J, 2024, 46(2): 204-209. DOI: 10.11675/j.issn.0253-4304.2024.02.05.
[34]
OMMEN S R, HO C Y, ASIF I M, et al. 2024 AHA/ACC/AMSSM/HRS/PACES/SCMR guideline for the management of hypertrophic cardiomyopathy: a report of the American heart association/American college of cardiology joint committee on clinical practice guidelines[J/OL]. Circulation, 2024, 149(23): e1239-e1311 [2025-08-03]. https://pubmed.ncbi.nlm.nih.gov/38718139/. DOI: 10.1161/CIR.0000000000001250.
[35]
SALMANIPOUR A, GHAFFARI JOLFAYI A, SABET KHADEM N, et al. The predictive value of cardiac MRI strain parameters in hypertrophic cardiomyopathy patients with preserved left ventricular ejection fraction and a low fibrosis burden: a retrospective cohort study[J/OL]. Front Cardiovasc Med, 2023, 10: 1246759 [2025-08-03]. https://pubmed.ncbi.nlm.nih.gov/37781305/. DOI: 10.3389/fcvm.2023.1246759.
[36]
ZHANG H B, TIAN J, ZHANG C, et al. Discrimination models with radiomics features derived from cardiovascular magnetic resonance images for distinguishing hypertensive heart disease from hypertrophic cardiomyopathy[J]. Cardiovasc Diagn Ther, 2024, 14(1): 129-142. DOI: 10.21037/cdt-23-350.
[37]
LIU Q M, LU Q F, CHAI Y Z, et al. Papillary-muscle-derived radiomic features for hypertrophic cardiomyopathy versus hypertensive heart disease classification[J/OL]. Diagnostics (Basel), 2023, 13(9): 1544. [2025-06-10]. https://pubmed.ncbi.nlm.nih.gov/37174935/. DOI: 10.3390/diagnostics13091544.
[38]
LÜ J, ZHU Y Q, ZHU Y F, et al. Study of the value of radiomics based on cardiac magnetic resonance in hypertrophic cardiomyopathy[J]. Chin J Magn Reson Imag, 2024, 15(2): 30-41. DOI: 10.12015/issn.1674-8034.2024.02.005.
[39]
PU C L, HU X, LV S Y, et al. Identification of fibrosis in hypertrophic cardiomyopathy: a radiomic study on cardiac magnetic resonance cine imaging[J]. Eur Radiol, 2023, 33(4): 2301-2311. DOI: 10.1007/s00330-022-09217-0.
[40]
MANCIO J, PASHAKHANLOO F, EL-REWAIDY H, et al. Machine learning phenotyping of scarred myocardium from cine in hypertrophic cardiomyopathy[J]. Eur Heart J Cardiovasc Imaging, 2022, 23(4): 532-542. DOI: 10.1093/ehjci/jeab056.
[41]
FAHMY A S, ROWIN E J, ARAFATI A, et al. Radiomics and deep learning for myocardial scar screening in hypertrophic cardiomyopathy[J/OL]. J Cardiovasc Magn Reson, 2022, 24(1): 40 [2025-09-25]. https://pubmed.ncbi.nlm.nih.gov/35761339/. DOI: 10.1186/s12968-022-00869-x.
[42]
FAHMY A S, ROWIN E J, JAAFAR N, et al. Radiomics of late gadolinium enhancement reveals prognostic value of myocardial scar heterogeneity in hypertrophic cardiomyopathy[J]. JACC Cardiovasc Imaging, 2024, 17(1): 16-27. DOI: 10.1016/j.jcmg.2023.05.003.
[43]
WANG J, BRAVO L, ZHANG J Q, et al. Radiomics analysis derived from LGE-MRI predict sudden cardiac death in participants with hypertrophic cardiomyopathy[J/OL]. Front Cardiovasc Med, 2021, 8: 766287 [2025-06-10]. https://pubmed.ncbi.nlm.nih.gov/34957254/. DOI: 10.3389/fcvm.2021.766287.
[44]
SCLAFANI M, FALASCONI G, TINI G, et al. Substrates of sudden cardiac death in hypertrophic cardiomyopathy[J/OL]. J Clin Med, 2025, 14(4): 1331 [2025-08-03]. https://pubmed.ncbi.nlm.nih.gov/40004861/. DOI: 10.3390/jcm14041331.
[45]
DURMAZ E S, KARABACAK M, OZKARA B B, et al. Machine learning and radiomics for ventricular tachyarrhythmia prediction in hypertrophic cardiomyopathy: insights from an MRI-based analysis[J]. Acta Radiol, 2024, 65(12): 1473-1481. DOI: 10.1177/02841851241283041.
[46]
LÜ J, ZHU Y Q, ZHU Y F, et al. Value of CMR radiomics combined with clinical factors in predicting hypertrophic cardiomyopathy complicated by ventricular arrhythmias[J]. Chin J Magn Reson Imag, 2024, 15(4): 63-71, 87. DOI: 10.12015/issn.1674-8034.2024.04.011.
[47]
SJÖLAND H, SILVERDAL J, BOLLANO E, et al. Temporal trends in outcome and patient characteristics in dilated cardiomyopathy, data from the Swedish Heart Failure Registry 2003-2015[J/OL]. BMC Cardiovasc Disord, 2021, 21(1): 307 [2025-06-10]. https://pubmed.ncbi.nlm.nih.gov/34144681/. DOI: 10.1186/s12872-021-02124-0.
[48]
IZQUIERDO C, CASAS G, MARTIN-ISLA C, et al. Radiomics-based classification of left ventricular non-compaction, hypertrophic cardiomyopathy, and dilated cardiomyopathy in cardiovascular magnetic resonance[J/OL]. Front Cardiovasc Med, 2021, 8: 764312 [2025-06-10]. https://pubmed.ncbi.nlm.nih.gov/34778415/. DOI: 10.3389/fcvm.2021.764312.
[49]
ZHANG X X, CUI C X, ZHAO S F, et al. Cardiac magnetic resonance radiomics for disease classification[J]. Eur Radiol, 2023, 33(4): 2312-2323. DOI: 10.1007/s00330-022-09236-x.
[50]
NAKAMORI S, AMYAR A, FAHMY A S, et al. Cardiovascular magnetic resonance radiomics to identify components of the extracellular matrix in dilated cardiomyopathy[J]. Circulation, 2024, 150(1): 7-18. DOI: 10.1161/CIRCULATIONAHA.123.067107.
[51]
AMYAR A, NAKAMORI S, NGO L, et al. Cardiovascular magnetic resonance radiologic-pathologic correlation in radiomic analysis of myocardium in non-ischemic dilated cardiomyopathy[J/OL]. J Cardiovasc Magn Reson, 2025, 27(1): 101881 [2025-06-10]. https://pubmed.ncbi.nlm.nih.gov/40118277/. DOI: 10.1016/j.jocmr.2025.101881.
[52]
ZHANG J, XU Y W, LI W H, et al. The predictive value of myocardial native T1 mapping radiomics in dilated cardiomyopathy: a study in a Chinese population[J]. J Magn Reson Imaging, 2023, 58(3): 772-779. DOI: 10.1002/jmri.28527.
[53]
LI X, XU Y W, CHEN X Y, et al. Prognostic value of enhanced cine cardiac MRI-based radiomics in dilated cardiomyopathy[J/OL]. Int J Cardiol, 2025, 418: 132617 [2025-06-10]. https://pubmed.ncbi.nlm.nih.gov/39370047/. DOI: 10.1016/j.ijcard.2024.132617.
[54]
SHU S L, WANG C, HONG Z M, et al. Prognostic value of late enhanced cardiac magnetic resonance imaging derived texture features in dilated cardiomyopathy patients with severely reduced ejection fractions[J/OL]. Front Cardiovasc Med, 2021, 8: 766423 [2025-06-10]. https://pubmed.ncbi.nlm.nih.gov/34977183/. DOI: 10.3389/fcvm.2021.766423.
[55]
AMYAR A, AL-DEIRI D, SROUBEK J, et al. Radiomic cardiac MRI signatures for predicting ventricular arrhythmias in patients with nonischemic dilated cardiomyopathy[J/OL]. JACC Adv, 2025, 4(4): 101684 [2025-06-10]. https://pubmed.ncbi.nlm.nih.gov/40127609/. DOI: 10.1016/j.jacadv.2025.101684.
[56]
SINITSYN V. Radiomics enhances the prognostic role of magnetic resonance imaging in cardiac amyloidosis[J]. Eur Radiol, 2024, 34(1): 400-401. DOI: 10.1007/s00330-023-10192-3.
[57]
SHE J Q, GUO J J, SUN Y, et al. Predictive model based on texture analysis of noncontrast cardiac magnetic resonance images for the prognostic evaluation of cardiac amyloidosis[J]. J Comput Assist Tomogr, 2025, 49(2): 271-280. DOI: 10.1097/RCT.0000000000001671.
[58]
ZHOU X Y, TANG C X, GUO Y K, et al. Late gadolinium enhanced cardiac MR derived radiomics approach for predicting all-cause mortality in cardiac amyloidosis: a multicenter study[J]. Eur Radiol, 2024, 34(1): 402-410. DOI: 10.1007/s00330-023-09999-x.
[59]
WU X, TANG L, DENG Q, et al. The feasibility study of MRI texture analysis in predicting delayed enhancement status in cardiac amyloidosis[J]. Chin J Magn Reson Imag, 2021, 12(12): 6-11. DOI: 10.12015/issn.1674-8034.2021.12.002.
[60]
MA Q M, CHEN J Y, CAO L Q, et al. The incremental value of native T1 mapping-derived radiomics for the diagnosis of amyloid light-chain cardiac amyloidosis[J]. Acad Radiol, 2024, 31(12): 4801-4810. DOI: 10.1016/j.acra.2024.07.005.
[61]
LIU Y, WEI X, FENG X, et al. Repeatability of radiomics studies in colorectal cancer: a systematic review[J/OL]. BMC Gastroenterol, 2023, 23(1): 125 [2025-06-10]. https://pubmed.ncbi.nlm.nih.gov/36330005/. DOI: 10.1186/s12876-023-02743-1.
[62]
ADIRAJU R V, KALYANI K, SURYANARAYANA G, et al. Radiomics: assessing significance and correlation with ground-truth data in precision medicine in lung adenocarcinoma[J/OL]. Bioengineering (Basel), 2025, 12(6): 576 [2025-06-10]. https://pubmed.ncbi.nlm.nih.gov/40564393/. DOI: 10.3390/bioengineering12060576.

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