{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T15:34:14Z","timestamp":1782833654829,"version":"3.54.5"},"reference-count":28,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,2,21]],"date-time":"2024-02-21T00:00:00Z","timestamp":1708473600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Cardiovasc. Med."],"abstract":"<jats:sec><jats:title>Introduction<\/jats:title><jats:p>Accurate identification of the myocardial texture features of fat around the coronary artery on coronary computed tomography angiography (CCTA) images are crucial to improve clinical diagnostic efficiency of myocardial ischemia (MI). However, current coronary CT examination is difficult to recognize and segment the MI characteristics accurately during earlier period of inflammation.<\/jats:p><\/jats:sec><jats:sec><jats:title>Materials and methods<\/jats:title><jats:p>We proposed a random forest model to automatically segment myocardium and extract peripheral fat features. This hybrid machine learning (HML) model is integrated by CCTA images and clinical data. A total of 1,316 radiomics features were extracted from CCTA images. To further obtain the features that contribute the most to the diagnostic model, dimensionality reduction was applied to filter features to three: LNS, GFE, and WLGM. Moreover, statistical hypothesis tests were applied to improve the ability of discriminating and screening clinical features between the ischemic and non-ischemic groups.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>By comparing the accuracy, recall, specificity and AUC of the three models, it can be found that HML had the best performance, with the value of 0.848, 0.762, 0.704 and 0.729.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>In sum, this study demonstrates that ML-based radiomics model showed good predictive value in MI, and offer an enhanced tool for predicting prognosis with greater accuracy.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fcvm.2024.1327912","type":"journal-article","created":{"date-parts":[[2024,2,21]],"date-time":"2024-02-21T05:02:56Z","timestamp":1708491776000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["A novel hybrid machine learning model for auxiliary diagnosing myocardial ischemia"],"prefix":"10.3389","volume":"11","author":[{"given":"Jing","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingsong","family":"Mao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Suzhong","family":"Fu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haowei","family":"Gu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Naiming","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guoqing","family":"Su","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiping","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaiyue","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuetong","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hengyu","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingliang","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2024,2,21]]},"reference":[{"key":"B1","year":""},{"key":"B2","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1016\/j.acvd.2010.02.002","article-title":"Impact of polyvascular disease on baseline characteristics, management and mortality in acute myocardial infarction","volume":"103","author":"Meizels","year":"2010","journal-title":"The Alliance Project"},{"key":"B3","doi-asserted-by":"publisher","first-page":"926","DOI":"10.33963\/KP.14945","article-title":"The prevalence of polyvascular disease in patients with carotid artery disease and peripheral artery disease","volume":"77","author":"Vlajinac","year":"2019","journal-title":"Kardiol Pol"},{"key":"B4","doi-asserted-by":"publisher","first-page":"1068","DOI":"10.1016\/j.numecd.2019.06.015","article-title":"Polyvascular subclinical atherosclerosis in familial hypercholesterolemia: the role of cholesterol burden and gender","volume":"29","author":"Mattina","year":"2019","journal-title":"Nutr Metab Cardiovasc Dis"},{"key":"B5","doi-asserted-by":"publisher","first-page":"437","DOI":"10.1001\/jamacardio.2020.7073","article-title":"Association of lipid, inflammatory, and metabolic biomarkers with age at onset for incident coronary heart disease in women","volume":"6","author":"Dugani","year":"2021","journal-title":"JAMA Cardiol"},{"key":"B6","doi-asserted-by":"publisher","first-page":"487","DOI":"10.1007\/s40256-020-00455-5","article-title":"Angiotensin-converting enzyme inhibitors and contrast-associated acute kidney injury after coronary angiography and intervention","volume":"21","author":"Wasilewski","year":"2021","journal-title":"Am J. Cardiovasc Drugs"},{"key":"B7","doi-asserted-by":"publisher","first-page":"1338","DOI":"10.1001\/jamacardio.2020.3409","article-title":"Stress myocardialperfusion imaging vs coronary computed tomographic angiography for diagnosis of invasive vessel-specific coronary physiology: predictive modeling results from the computed tomographic evaluation of atherosclerotic determinants of myocardial ischemia (CREDENCE) trial","volume":"5","author":"Stuijfzand","year":"2020","journal-title":"JAMA Cardiol"},{"key":"B8","doi-asserted-by":"publisher","first-page":"2170","DOI":"10.1016\/S0735-1097(01)01346-8","article-title":"American College of Cardiology\/Society for cardiac angiography and intervention clinical expert consensus document on cardiac catheterization laboratory standards: a report of the American College of Cardiology Task Force on clinical expert consensus documents","volume":"37","author":"Bashore","year":"2001","journal-title":"J Am Coll Cardiol"},{"key":"B9","doi-asserted-by":"publisher","first-page":"921","DOI":"10.1136\/heartjnl-2017-311846","article-title":"Challenges in delivering computed tomography coronary angiography as the first-line test for stable chest pain","volume":"104","author":"Dreisbach","year":"2018","journal-title":"Heart"},{"key":"B10","doi-asserted-by":"publisher","first-page":"135","DOI":"10.4103\/0366-6999.173434","article-title":"Impact of clinical guideline recommendations on the application of coronary computed tomographic angiography in patients with suspected stable coronary artery disease","volume":"129","author":"Zhou","year":"2016","journal-title":"Chin Med J"},{"key":"B11","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1016\/j.acra.2016.09.008","article-title":"Diagnostic accuracy of noninvasive 64-row computed tomographic coronary angiography (CCTA) compared with myocardial perfusion imaging (MPI): the PICTURE study. A prospective multicenter trial","volume":"24","author":"Budoff","year":"2017","journal-title":"Acad Radiol"},{"key":"B12","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1016\/j.cmpb.2014.07.005","article-title":"A fast seed detection using local geometrical feature for automatic tracking of coronary arteries in CTA","volume":"117","author":"Han","year":"2014","journal-title":"Comput Methods Programs Biomed"},{"key":"B13","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-32251-9_65","article-title":"Coronary artery plaque characterization from CCTA scans using deep learning and radiomics","author":"Denzinger","year":"2019","journal-title":"Med Image Comput Comput Assist Interv"},{"key":"B14","doi-asserted-by":"publisher","first-page":"500","DOI":"10.1093\/eurheartj\/ehw188","article-title":"Machine learning for prediction of all-cause mortality in patients with suspected coronary artery disease: a 5-year multicentre prospective registry analysis","volume":"38","author":"Motwani","year":"2017","journal-title":"Eur Heart J"},{"key":"B15","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1016\/j.media.2017.11.008","article-title":"Deep learning analysis of the myocardium in coronary CT angiog raphy for identification of patients with functionally significant coronary artery stenosis","volume":"44","author":"Zreik","year":"2018","journal-title":"Med Image Anal"},{"key":"B16","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2023.3287200","article-title":"Robust vascular segmentation for raw complex images of laser speckle contrast based on weakly supervised learning","author":"Fu","year":"2023","journal-title":"IEEE Trans Med Imaging"},{"key":"B17","doi-asserted-by":"publisher","first-page":"440","DOI":"10.1016\/j.jcmg.2010.02.003","article-title":"The napkin-ring sign: cT signature of high-risk coronary plaques?","volume":"3","author":"Maurovich-Horvat","year":"2010","journal-title":"JACC Cardiovasc Imaging"},{"key":"B18","doi-asserted-by":"publisher","first-page":"262","DOI":"10.1007\/s12350-020-02204-2","article-title":"Predicting chronic myocardial ischemia using CCTA-based radiomics machine learning nomogram","volume":"29","author":"Shu","year":"2022","journal-title":"J Nucl Cardiol"},{"key":"B19","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1109\/cvpr.2005.38","article-title":"A non-local algorithm for image denoising","volume":"2","author":"Buades","year":"2005","journal-title":"CVPR"},{"key":"B20","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1016\/j.inffus.2019.09.003","article-title":"Image denoising review: from classical to state-of-the-art approaches","volume":"55","author":"Goyal","year":"2020","journal-title":"Info Fusion"},{"key":"B21","doi-asserted-by":"publisher","first-page":"18347","DOI":"10.1038\/s41598-021-97796-1","article-title":"Radiomics-based model for predicting early recurrence of intrahepatic mass-forming cholangiocarcinoma after curative tumor resection","volume":"11","author":"Zhu","year":"2021","journal-title":"Sci Rep"},{"key":"B22","doi-asserted-by":"publisher","first-page":"563","DOI":"10.1148\/radiol.2015151169","article-title":"Radiomics: images are more than pictures, they are data","volume":"278","author":"Gillies","year":"2016","journal-title":"Radiology"},{"key":"B23","doi-asserted-by":"publisher","first-page":"4871","DOI":"10.3390\/cancers14194871","article-title":"Oncologic imaging and radiomics: a walkthrough review of methodological challenges","volume":"14","author":"Stanzione","year":"2022","journal-title":"Cancers (Basel)"},{"key":"B24","doi-asserted-by":"publisher","first-page":"1734","DOI":"10.3390\/diagnostics13101734","article-title":"Predicting extrathyroidal extension in papillary thyroid carcinoma using a clinical-radiomics nomogram based on B-mode and contrast-enhanced ultrasound","volume":"13","author":"Jiang","year":"2023","journal-title":"Diagnostics"},{"key":"B25","doi-asserted-by":"publisher","first-page":"1226","DOI":"10.1109\/TPAMI.2005.159","article-title":"Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy","volume":"8","author":"Peng","year":"2005","journal-title":"TPAMI"},{"key":"B26","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1038\/nmeth.4642","article-title":"Statistics versus machine learning","volume":"15","author":"Bzdok","year":"2018","journal-title":"Nat Methods"},{"key":"B27","doi-asserted-by":"publisher","first-page":"2165","DOI":"10.1093\/bioinformatics\/btab074","article-title":"Random forest of perfect trees: concept, performance, applications, and perspectives","volume":"37","author":"Nguyen","year":"2021","journal-title":"Bioinformatics"},{"key":"B28","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1016\/j.media.2016.07.007","article-title":"Large scale deep learning for computer aided detection of mammographic lesions","volume":"35","author":"Kooi","year":"2017","journal-title":"Med Image Anal"}],"container-title":["Frontiers in Cardiovascular Medicine"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fcvm.2024.1327912\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,21]],"date-time":"2024-02-21T05:02:59Z","timestamp":1708491779000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fcvm.2024.1327912\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,21]]},"references-count":28,"alternative-id":["10.3389\/fcvm.2024.1327912"],"URL":"https:\/\/doi.org\/10.3389\/fcvm.2024.1327912","relation":{},"ISSN":["2297-055X"],"issn-type":[{"value":"2297-055X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,21]]},"article-number":"1327912"}}