{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T09:59:08Z","timestamp":1777975148860,"version":"3.51.4"},"reference-count":34,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2023,5,3]],"date-time":"2023-05-03T00:00:00Z","timestamp":1683072000000},"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":[[2024,1]]},"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>The preoperative diagnosis of peritoneal metastasis (PM) in epithelial ovarian cancer (EOC) is challenging and can impact clinical decision\u2010making.<\/jats:p><\/jats:sec><jats:sec><jats:title>Purpose<\/jats:title><jats:p>To investigate the performance of T<jats:sub>2<\/jats:sub>\u2010weighted (T2W) MRI\u2010based deep learning (DL) and radiomics methods for PM evaluation in EOC patients.<\/jats:p><\/jats:sec><jats:sec><jats:title>Study Type<\/jats:title><jats:p>Retrospective.<\/jats:p><\/jats:sec><jats:sec><jats:title>Population<\/jats:title><jats:p>Four hundred seventy\u2010nine patients from five centers, including one training set (N\u2009=\u2009297 [mean, 54.87\u2009years]), one internal validation set (N\u2009=\u200975 [mean, 56.67\u2009years]), and two external validation sets (N\u2009=\u200953 [mean, 55.58\u2009years] and N\u2009=\u200954 [mean, 58.22\u2009years]).<\/jats:p><\/jats:sec><jats:sec><jats:title>Field Strength\/Sequence<\/jats:title><jats:p>1.5 or 3 T\/fat\u2010suppression T2W fast or turbo spin\u2010echo sequence.<\/jats:p><\/jats:sec><jats:sec><jats:title>Assessment<\/jats:title><jats:p>ResNet\u201050 was used as the architecture of DL. The largest orthogonal slices of the tumor area, radiomics features, and clinical characteristics were used to construct the DL, radiomics, and clinical models, respectively. The three models were combined using decision\u2010level fusion to create an ensemble model. Diagnostic performances of radiologists and radiology residents with and without model assistance were evaluated.<\/jats:p><\/jats:sec><jats:sec><jats:title>Statistical Tests<\/jats:title><jats:p>Receiver operating characteristic analysis was used to assess the performances of models. The McNemar test was used to compare sensitivity and specificity. A two\u2010tailed <jats:italic>P<\/jats:italic>\u2009&lt;\u20090.05 was considered significant.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The ensemble model had the best AUCs, outperforming the DL model (0.844 vs. 0.743, internal validation set; 0.859 vs. 0.737, external validation set I) and clinical model (0.872 vs. 0.730, external validation set II). After model assistance, all readers had significantly improved sensitivity, especially for those with less experience (junior radiologist1, from 0.639 to 0.820; junior radiologist2, from 0.689 to 0.803; resident1, from 0.623 to 0.803; resident2, from 0.541 to 0.738). One resident also had significantly improved specificity (from 0.633 to 0.789).<\/jats:p><\/jats:sec><jats:sec><jats:title>Data Conclusions<\/jats:title><jats:p>T2W MRI\u2010based DL and radiomics approaches have the potential to preoperatively predict PM in EOC patients and assist in clinical decision\u2010making.<\/jats:p><\/jats:sec><jats:sec><jats:title>Evidence Level<\/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.28761","type":"journal-article","created":{"date-parts":[[2023,5,3]],"date-time":"2023-05-03T16:35:26Z","timestamp":1683131726000},"page":"122-131","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Associating Peritoneal Metastasis With <scp>T2\u2010Weighted MRI<\/scp> Images in Epithelial Ovarian Cancer Using Deep Learning and Radiomics: A Multicenter Study"],"prefix":"10.1002","volume":"59","author":[{"given":"Mingxiang","family":"Wei","sequence":"first","affiliation":[{"name":"Department of Radiology The Affiliated Suzhou Hospital of Nanjing Medical University, Gusu School, Nanjing Medical University  Suzhou Jiangsu China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Radiology Dushu Lake Hospital Affiliated to Soochow University  Suzhou Jiangsu China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cong","family":"Ding","sequence":"additional","affiliation":[{"name":"Department of Radiology The Affiliated Huaian No. 1 People's Hospital of Nanjing Medical University  Huaian Jiangsu China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianye","family":"Jia","sequence":"additional","affiliation":[{"name":"Department of Radiology The Affiliated Huaian No. 1 People's Hospital of Nanjing Medical University  Huaian Jiangsu China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haimin","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Radiology Dushu Lake Hospital Affiliated to Soochow University  Suzhou Jiangsu China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yao","family":"Dai","sequence":"additional","affiliation":[{"name":"Department of Radiology The First Affiliated Hospital of Soochow University  Suzhou Jiangsu China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guannan","family":"Feng","sequence":"additional","affiliation":[{"name":"Department of Gynecology The Affiliated Suzhou Hospital of Nanjing Medical University, Gusu School, Nanjing Medical University  Suzhou Jiangsu China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-3472-4942","authenticated-orcid":false,"given":"Cai","family":"Qin","sequence":"additional","affiliation":[{"name":"Department of Radiology Tumor Hospital Affiliated to Nantong University  Nantong Jiangsu China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6891-9721","authenticated-orcid":false,"given":"Genji","family":"Bai","sequence":"additional","affiliation":[{"name":"Department of Radiology The Affiliated Huaian No. 1 People's Hospital of Nanjing Medical University  Huaian Jiangsu China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1504-4773","authenticated-orcid":false,"given":"Shuangqing","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Radiology The Affiliated Suzhou Hospital of Nanjing Medical University, Gusu School, Nanjing Medical University  Suzhou Jiangsu China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hong","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Radiology The Affiliated Suzhou Hospital of Nanjing Medical University, Gusu School, Nanjing Medical University  Suzhou Jiangsu China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2023,5,3]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(18)32552-2"},{"key":"e_1_2_7_3_1","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21708"},{"key":"e_1_2_7_4_1","doi-asserted-by":"publisher","DOI":"10.1002\/ijgo.13878"},{"key":"e_1_2_7_5_1","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21559"},{"key":"e_1_2_7_6_1","doi-asserted-by":"publisher","DOI":"10.6004\/jnccn.2021.0007"},{"key":"e_1_2_7_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1470-2045(18)30566-7"},{"key":"e_1_2_7_8_1","doi-asserted-by":"publisher","DOI":"10.2147\/OTT.S147700"},{"key":"e_1_2_7_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-019-06524-x"},{"key":"e_1_2_7_10_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-013-3083-8"},{"key":"e_1_2_7_11_1","doi-asserted-by":"publisher","DOI":"10.1002\/jso.23878"},{"key":"e_1_2_7_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2019.108696"},{"key":"e_1_2_7_13_1","doi-asserted-by":"publisher","DOI":"10.1038\/nrclinonc.2017.141"},{"key":"e_1_2_7_14_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41568-018-0016-5"},{"key":"e_1_2_7_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eclinm.2022.101662"},{"key":"e_1_2_7_16_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-020-06937-z"},{"key":"e_1_2_7_17_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-021-08004-7"},{"key":"e_1_2_7_18_1","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2021.765652"},{"key":"e_1_2_7_19_1","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2020.32269"},{"key":"e_1_2_7_20_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13244-022-01264-x"},{"key":"e_1_2_7_21_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13048-022-00943-z"},{"key":"e_1_2_7_22_1","doi-asserted-by":"publisher","DOI":"10.1148\/ryai.2020200029"},{"key":"e_1_2_7_23_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2006.01.015"},{"key":"e_1_2_7_24_1","doi-asserted-by":"crossref","unstructured":"HeK ZhangX RenS SunJ.Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition;2016. pp. 770\u2013778.","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_2_7_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2005.159"},{"key":"e_1_2_7_26_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41572-021-00326-6"},{"key":"e_1_2_7_27_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejso.2019.11.520"},{"key":"e_1_2_7_28_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmig.2016.03.006"},{"key":"e_1_2_7_29_1","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2022.879889"},{"key":"e_1_2_7_30_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.211367"},{"key":"e_1_2_7_31_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-020-07266-x"},{"key":"e_1_2_7_32_1","doi-asserted-by":"publisher","DOI":"10.1002\/jmri.27592"},{"key":"e_1_2_7_33_1","doi-asserted-by":"publisher","DOI":"10.1148\/rg.305105073"},{"key":"e_1_2_7_34_1","doi-asserted-by":"publisher","DOI":"10.1002\/jmri.28008"},{"key":"e_1_2_7_35_1","doi-asserted-by":"publisher","DOI":"10.3390\/cancers14040987"}],"container-title":["Journal of Magnetic Resonance Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/jmri.28761","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,24]],"date-time":"2023-12-24T20:04:26Z","timestamp":1703448266000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/jmri.28761"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,3]]},"references-count":34,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,1]]}},"alternative-id":["10.1002\/jmri.28761"],"URL":"https:\/\/doi.org\/10.1002\/jmri.28761","archive":["Portico"],"relation":{},"ISSN":["1053-1807","1522-2586"],"issn-type":[{"value":"1053-1807","type":"print"},{"value":"1522-2586","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,3]]},"assertion":[{"value":"2023-03-16","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-04-19","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-05-03","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}