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Regions of interest for TA were delineated by two radiologists independently on T2-weighted imaging (T2WI). Stepwise dimension reduction and texture feature selection based on reproducibility, machine learning algorithms, and correlation analyses were performed to select features. Both the CA and HCM groups were randomly divided into a training dataset and a testing dataset (7:3). After the TA model was established in the training set, the diagnostic performance of the model was validated in the testing set and further validated in a subgroup of patients with similar hypertrophy.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>The 7 independent texture features provided, in combination, a diagnostic accuracy of 86.0% (AUC\u2009=\u20090.915; 95% CI 0.879\u20130.951) in the training dataset and 79.2% (AUC\u2009=\u20090.842; 95% CI 0.759\u20130.924) in the testing dataset. The differential diagnostic accuracy in the similar hypertrophy subgroup was 82.2% (AUC\u2009=\u20090.864, 95% CI 0.805\u20130.922). The significance of the difference between the AUCs of the TA model and late gadolinium enhancement (LGE) was verified by Delong\u2019s test (<jats:italic>p<\/jats:italic>\u2009=\u20090.898). All seven texture features showed significant differences between CA and HCM (all <jats:italic>p<\/jats:italic>\u2009&lt;\u20090.001).<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>Our study demonstrated that texture analysis based on T2-weighted images could feasibly differentiate CA from HCM, even in patients with similar hypertrophy. The selected final texture features could achieve a comparable diagnostic capacity to the quantification of LGE.<\/jats:p>\n                <jats:p><jats:italic>Trial registration <\/jats:italic>Since this study is a retrospective observational study and no intervention had been involved, trial registration is waived.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12872-022-02671-0","type":"journal-article","created":{"date-parts":[[2022,5,21]],"date-time":"2022-05-21T13:04:23Z","timestamp":1653138263000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Texture analysis of T2-weighted cardiovascular magnetic resonance imaging to discriminate between cardiac amyloidosis and hypertrophic cardiomyopathy"],"prefix":"10.1186","volume":"22","author":[{"given":"Shan","family":"Huang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ke","family":"Shi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei-Feng","family":"Yan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying-Kun","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuan","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhi-Gang","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,5,21]]},"reference":[{"issue":"13","key":"2671_CR1","doi-asserted-by":"publisher","first-page":"2047","DOI":"10.1161\/CIRCULATIONAHA.104.489187","volume":"112","author":"RH Falk","year":"2005","unstructured":"Falk RH. 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