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Patients were divided into favorable outcome (modified Rankin scale, mRS\u2009\u2264\u20092) and unfavorable outcome (mRS\u2009&gt;\u20092) groups according to mRS scores at day 90. Two radiologists manually segmented the infarction lesions based on diffusion-weighted imaging and transferred the images to corresponding apparent diffusion coefficient (ADC) maps in order to extract texture features. The prediction models including clinical characteristics and texture features were built using multiple logistic regression. A univariate analysis was conducted to assess the performance of the mean ADC value of the infarction lesion. A Delong\u2019s test was used to compare the predictive performance of models through the receiver operating characteristic curve.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>The mean ADC performance was moderate [AUC\u2009=\u20090.60, 95% confidence interval (CI) 0.49\u20130.71]. The texture feature model of the ADC map (tADC), contained seven texture features, and presented good prediction performance (AUC\u2009=\u20090.83, 95%CI 0.75\u20130.91). The energy obtained after wavelet transform, and the kurtosis and skewness obtained after Laplacian of Gaussian transformation were identified as independent prognostic factors for the favorable stroke outcomes. In addition, the combination of the tADC model and clinical characteristics (hypertension, diabetes mellitus, smoking, and atrial fibrillation) exhibited a subtly better performance (AUC\u2009=\u20090.86, 95%CI 0.79\u20130.93; <jats:italic>P<\/jats:italic>\u2009&gt;\u20090.05, Delong\u2019s).<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>The models based on MRTA on ADC maps are useful to evaluate the clinical function outcomes in patients with unilateral anterior circulation ischemic stroke. Energy obtained after wavelet transform, kurtosis obtained after Laplacian of Gaussian transform, and skewness obtained after Laplacian of Gaussian transform were identified as independent prognostic factors for favorable stroke outcomes.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12880-022-00845-y","type":"journal-article","created":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T18:02:47Z","timestamp":1656698567000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["MRI whole-lesion texture analysis on ADC maps for the prognostic assessment of ischemic stroke"],"prefix":"10.1186","volume":"22","author":[{"given":"Yuan","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuzhong","family":"Zhuang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yaqiong","family":"Ge","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pu-Yeh","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bin","family":"Song","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,7,1]]},"reference":[{"issue":"3","key":"845_CR1","first-page":"e28","volume":"129","author":"AS Go","year":"2014","unstructured":"Go AS, Mozaffarian D, Roger VL, Benjamin EJ, Berry JD, Blaha MJ, et al. 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