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One hundred radiomics features were computed from mono-exponential (ME), biexponential (BE), stretched exponential (SE), and diffusion-kurtosis imaging (DKI). Radiomics-based analysis was performed by comparing four classifiers, including random forest (RF), principal component analysis (PCA), L1 regularization (L1R), and support vector machine (SVM). These four classifiers were trained on a training set with 271 patients via ten-fold cross-validation and tested on an independent testing set with 271 patients. The diagnostic performance of the mean diffusion metrics of ME (mADC<jats:sub>all b<\/jats:sub>, mADC<jats:sub>0\u20131000<\/jats:sub>), BE (mD, mD<jats:sup>*<\/jats:sup>, mf), SE (mDDC, m\u03b1), and DKI (mK, mD) were also calculated for comparison. The area under the receiver operating characteristic curve (AUC) was used to compare the diagnostic performance.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>RF attained higher AUCs than L1R, PCA and SVM. The AUCs of radiomics features for the differential diagnosis of breast lesions ranged from 0.80 (BE_D*) to 0.85 (BE_D). The AUCs of the mean diffusion metrics ranged from 0.54 (BE_mf) to 0.79 (ME_mADC<jats:sub>0\u20131000<\/jats:sub>). There were significant differences in the AUCs between the mean values of all diffusion metrics and radiomics features of AUCs (all <jats:italic>P<\/jats:italic>\u2009&lt;\u20090.001) for the differentiation of benign and malignant breast lesions. Of the radiomics features computed, the most important sequence was BE_D (AUC: 0.85), and the most important feature was FO-10 percentile (Feature Importance: 0.04).<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>The radiomics-based analysis of multiparametric DWI by RF enables better differentiation of benign and malignant breast lesions than the mean diffusion metrics.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12967-021-03117-5","type":"journal-article","created":{"date-parts":[[2021,10,24]],"date-time":"2021-10-24T16:02:40Z","timestamp":1635091360000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Radiomics-based machine learning analysis and characterization of breast lesions with multiparametric diffusion-weighted MR"],"prefix":"10.1186","volume":"19","author":[{"given":"Kun","family":"Sun","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhicheng","family":"Jiao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hong","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weimin","family":"Chai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xu","family":"Yan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Caixia","family":"Fu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie-Zhi","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8969-703X","authenticated-orcid":false,"given":"Fuhua","family":"Yan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dinggang","family":"Shen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,10,24]]},"reference":[{"key":"3117_CR1","doi-asserted-by":"publisher","first-page":"666","DOI":"10.1148\/radiol.2392050509","volume":"239","author":"CK Kuhl","year":"2006","unstructured":"Kuhl CK, Jost P, Morakkabati N, Zivanovic O, Schild HH, Gieseke J. 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Informed consent was obtained from all individual participants included in the study.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"We declare that KS and ZJ contributed equally to this work. We declare that FY and DS are all the corresponding authors. We declare that two co-authors (XY, and CF) are employees of Siemens Healthcare, and two co-authors (JC and DS) are employees of Shanghai United Imaging Intelligence. Only those authors who are not employees of above two companies (KS, ZJ, HZ, WC, FY) had control of the data and information submitted for publication.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"443"}}