{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T22:01:22Z","timestamp":1790892082032,"version":"4.1.0"},"reference-count":25,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2018,1,17]],"date-time":"2018-01-17T00:00:00Z","timestamp":1516147200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Magnetic Resonance Imaging"],"published-print":{"date-parts":[[2018,7]]},"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>Adrenal adenomas (AA) are the most common benign adrenal lesions, often characterized based on intralesional fat content as either lipid\u2010rich (LRA) or lipid\u2010poor (LPA). The differentiation of AA, particularly LPA, from nonadenoma adrenal lesions (NAL) may be challenging. Texture analysis (TA) can extract quantitative parameters from MR images. Machine learning is a technique for recognizing patterns that can be applied to medical images by identifying the best combination of TA features to create a predictive model for the diagnosis of interest.<\/jats:p><\/jats:sec><jats:sec><jats:title>Purpose\/Hypothesis<\/jats:title><jats:p>To assess the diagnostic efficacy of TA\u2010derived parameters extracted from MR images in characterizing LRA, LPA, and NAL using a machine\u2010learning approach.<\/jats:p><\/jats:sec><jats:sec><jats:title>Study Type<\/jats:title><jats:p>Retrospective, observational study.<\/jats:p><\/jats:sec><jats:sec><jats:title>Population\/Subjects\/Phantom\/Specimen\/Animal Model<\/jats:title><jats:p>Sixty MR examinations, including 20 LRA, 20 LPA, and 20 NAL.<\/jats:p><\/jats:sec><jats:sec><jats:title>Field Strength\/Sequence<\/jats:title><jats:p>Unenhanced T<jats:sub>1<\/jats:sub>\u2010weighted in\u2010phase (IP) and out\u2010of\u2010phase (OP) as well as T<jats:sub>2<\/jats:sub>\u2010weighted (T<jats:sub>2<\/jats:sub>\u2010w) MR images acquired at 3T.<\/jats:p><\/jats:sec><jats:sec><jats:title>Assessment<\/jats:title><jats:p>Adrenal lesions were manually segmented, placing a spherical volume of interest on IP, OP, and T<jats:sub>2<\/jats:sub>\u2010w images. Different selection methods were trained and tested using the J48 machine\u2010learning classifiers.<\/jats:p><\/jats:sec><jats:sec><jats:title>Statistical Tests<\/jats:title><jats:p>The feature selection method that obtained the highest diagnostic performance using the J48 classifier was identified; the diagnostic performance was also compared with that of a senior radiologist by means of McNemar's test.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>A total of 138 TA\u2010derived features were extracted; among these, four features were selected, extracted from the IP (Short_Run_High_Gray_Level_Emphasis), OP (Mean_Intensity and Maximum_3D_Diameter), and T<jats:sub>2<\/jats:sub>\u2010w (Standard_Deviation) images; the J48 classifier obtained a diagnostic accuracy of 80%. The expert radiologist obtained a diagnostic accuracy of 73%. McNemar's test did not show significant differences in terms of diagnostic performance between the J48 classifier and the expert radiologist.<\/jats:p><\/jats:sec><jats:sec><jats:title>Data Conclusion<\/jats:title><jats:p>Machine learning conducted on MR TA\u2010derived features is a potential tool to characterize adrenal lesions.<\/jats:p><jats:p><jats:bold>Level of Evidence<\/jats:bold>: 4<\/jats:p><jats:p><jats:bold>Technical Efficacy<\/jats:bold>: Stage 2<\/jats:p><jats:p>J. Magn. Reson. Imaging 2018.<\/jats:p><\/jats:sec>","DOI":"10.1002\/jmri.25954","type":"journal-article","created":{"date-parts":[[2018,1,17]],"date-time":"2018-01-17T05:43:15Z","timestamp":1516167795000},"page":"198-204","source":"Crossref","is-referenced-by-count":68,"title":["Characterization of Adrenal Lesions on Unenhanced MRI Using Texture Analysis: A Machine\u2010Learning Approach"],"prefix":"10.1002","volume":"48","author":[{"given":"Valeria","family":"Romeo","sequence":"first","affiliation":[{"name":"Department of Advanced Biomedical Sciences University of Naples \u201cFederico II,\u201d Naples Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Simone","family":"Maurea","sequence":"additional","affiliation":[{"name":"Department of Advanced Biomedical Sciences University of Naples \u201cFederico II,\u201d Naples Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1452-1574","authenticated-orcid":false,"given":"Renato","family":"Cuocolo","sequence":"additional","affiliation":[{"name":"Department of Advanced Biomedical Sciences University of Naples \u201cFederico II,\u201d Naples Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mario","family":"Petretta","sequence":"additional","affiliation":[{"name":"Department of Translational Medical Sciences University of Naples \u201cFederico II,\u201d Naples Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pier Paolo","family":"Mainenti","sequence":"additional","affiliation":[{"name":"Institute of Biostructures and Bioimaging of the National Research Council (CNR) Naples Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francesco","family":"Verde","sequence":"additional","affiliation":[{"name":"Department of Advanced Biomedical Sciences University of Naples \u201cFederico II,\u201d Naples Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Milena","family":"Coppola","sequence":"additional","affiliation":[{"name":"Department of Advanced Biomedical Sciences University of Naples \u201cFederico II,\u201d Naples Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Serena","family":"Dell'Aversana","sequence":"additional","affiliation":[{"name":"Department of Advanced Biomedical Sciences University of Naples \u201cFederico II,\u201d Naples Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arturo","family":"Brunetti","sequence":"additional","affiliation":[{"name":"Department of Advanced Biomedical Sciences University of Naples \u201cFederico II,\u201d Naples Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2018,1,17]]},"reference":[{"key":"e_1_2_5_2_1","doi-asserted-by":"publisher","DOI":"10.2214\/AJR.07.2799"},{"key":"e_1_2_5_3_1","doi-asserted-by":"publisher","DOI":"10.4329\/wjr.v5.i3.88"},{"key":"e_1_2_5_4_1","doi-asserted-by":"publisher","DOI":"10.1259\/bjr.20140369"},{"key":"e_1_2_5_5_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2283020878"},{"key":"e_1_2_5_6_1","doi-asserted-by":"publisher","DOI":"10.2214\/AJR.07.3150"},{"key":"e_1_2_5_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2008.12.010"},{"key":"e_1_2_5_8_1","doi-asserted-by":"publisher","DOI":"10.2214\/AJR.05.0179"},{"key":"e_1_2_5_9_1","first-page":"1232","article-title":"Adrenal imaging. 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