{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T15:30:31Z","timestamp":1784043031392,"version":"3.55.0"},"reference-count":44,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2023,11,7]],"date-time":"2023-11-07T00:00:00Z","timestamp":1699315200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Magnetic Resonance Imaging"],"published-print":{"date-parts":[[2024,8]]},"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>Breast MRI has been recommended as supplemental screening tool to mammography and breast ultrasound of breast cancer by international guidelines, but its long examination time and use of contrast material remains concerning.<\/jats:p><\/jats:sec><jats:sec><jats:title>Purpose<\/jats:title><jats:p>To develop an unenhanced radiomics model with using non\u2010gadolinium based sequences for detecting breast cancer based on T2\u2010weighted (T2W) and diffusion\u2010weighted (DW) MRI.<\/jats:p><\/jats:sec><jats:sec><jats:title>Study Type<\/jats:title><jats:p>Retrospective analysis followed by retrospective and prospective cohorts study.<\/jats:p><\/jats:sec><jats:sec><jats:title>Population<\/jats:title><jats:p>1760 patients: Of these, 1293 for model construction (<jats:italic>n<\/jats:italic>\u2009=\u2009775 for training and 518 for validation). The remaining patients for model testing in internal retrospective (<jats:italic>n<\/jats:italic>\u2009=\u2009167), internal prospective (<jats:italic>n<\/jats:italic>\u2009=\u2009188), and external retrospective (<jats:italic>n<\/jats:italic>\u2009=\u2009112) cohorts.<\/jats:p><\/jats:sec><jats:sec><jats:title>Field Strength\/Sequence<\/jats:title><jats:p>3.0T MR scanners from two institution. T2WI, DWI, and first contrast\u2010enhanced T1\u2010weighted sequence.<\/jats:p><\/jats:sec><jats:sec><jats:title>Assessment<\/jats:title><jats:p>AUCs in distinguishing breast cancer were compared between combined model with gadolinium agent sequence and unenhanced model. Subsequently, the AUCs in testing cohorts of unenhanced model was compared with two radiologists' diagnosis for this research. Finally, patient subgroup analysis in testing cohorts was performed based on clinical subgroups and different types of malignancies.<\/jats:p><\/jats:sec><jats:sec><jats:title>Statistical Tests<\/jats:title><jats:p>Mann\u2013Whitney U test, Kruskal\u2010Wallis H test, chi\u2010square test, weighted kappa test, and DeLong's test.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The unenhanced radiomics model performed best under Gaussian process (GP) classifiers (AUC: training, 0.893; validation, 0.848) compared to support vector machine (SVM) and logistic, showing favorable prediction in testing cohorts (AUCs, 0.818\u20130.840). The AUCs for the unenhanced radiomics model were not statistically different in five cohorts from those of the combined radiomics model (<jats:italic>P<\/jats:italic>, 0.317\u20130.816), as well as the two radiologists (<jats:italic>P<\/jats:italic>, 0.181\u20130.918). The unenhanced radiomics model was least successful in identifying ductal carcinoma in situ, whereas did not show statistical significance in other subgroups.<\/jats:p><\/jats:sec><jats:sec><jats:title>Data Conclusion<\/jats:title><jats:p>An unenhanced radiomics model based on T2WI and DWI has comparable diagnostic accuracy to the combined model using the gadolinium agent.<\/jats:p><\/jats:sec><jats:sec><jats:title>Level of Evidence<\/jats:title><jats:p>4<\/jats:p><\/jats:sec><jats:sec><jats:title>Technical Efficacy<\/jats:title><jats:p>Stage 2<\/jats:p><\/jats:sec>","DOI":"10.1002\/jmri.29111","type":"journal-article","created":{"date-parts":[[2023,11,7]],"date-time":"2023-11-07T13:30:08Z","timestamp":1699363808000},"page":"600-612","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["A Machine Learning\u2010Based Unenhanced Radiomics Approach to Distinguishing Between Benign and Malignant Breast Lesions Using <scp>T2<\/scp>\u2010Weighted and Diffusion\u2010Weighted <scp>MRI<\/scp>"],"prefix":"10.1002","volume":"60","author":[{"given":"Yulu","family":"Liu","sequence":"first","affiliation":[{"name":"Department of Radiology Peking University People's Hospital  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6771-3353","authenticated-orcid":false,"given":"Xiaoxuan","family":"Jia","sequence":"additional","affiliation":[{"name":"Department of Radiology Peking University People's Hospital  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiaqi","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Radiology Jiangmen Central Hospital  Jiangmen China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuan","family":"Peng","sequence":"additional","affiliation":[{"name":"Department of Breast Surgery Peking University People's Hospital  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xun","family":"Yao","sequence":"additional","affiliation":[{"name":"Department of Radiology Peking University People's Hospital  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuege","family":"Hu","sequence":"additional","affiliation":[{"name":"Department of Breast Surgery Peking University People's Hospital  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingjing","family":"Cui","sequence":"additional","affiliation":[{"name":"Department of Research and Development United Imaging Intelligence (Beijing) Co., Ltd.  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