{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T14:26:25Z","timestamp":1740147985684,"version":"3.37.3"},"reference-count":25,"publisher":"Wiley","license":[{"start":{"date-parts":[[2022,8,17]],"date-time":"2022-08-17T00:00:00Z","timestamp":1660694400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100007289","name":"Nanjing Medical University","doi-asserted-by":"publisher","award":["2021KFKT014","2021KFKT002"],"award-info":[{"award-number":["2021KFKT014","2021KFKT002"]}],"id":[{"id":"10.13039\/501100007289","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Foundation of State Key Laboratory of Ultrasound in Medicine and Engineering","award":["2021KFKT014","2021KFKT002"],"award-info":[{"award-number":["2021KFKT014","2021KFKT002"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computational and Mathematical Methods in Medicine"],"published-print":{"date-parts":[[2022,8,17]]},"abstract":"<jats:p>Background. To construct and validate a radiomic-based model for estimating axillary lymph node (ALN) metastasis in patients with breast cancer by dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Methods. In this retrospective study, a radiomic-based model was established in a training cohort of 236 patients with breast cancer. Radiomic features were extracted from breast DCE-MRI scans. A method named the least absolute shrinkage and selection operator (LASSO) was applied to select radiomic features based on highly reproducible features. A radiomic signature was built by a support vector machine (SVM). Multivariate logistic regression analysis was adopted to establish a clinical characteristic-based model. The performance of models was analysed through discrimination ability and clinical benefits. Results. The radiomic signature comprised 6 features related to ALN metastasis and showed significant differences between the patients with ALN metastasis and without ALN metastasis (<jats:inline-formula><a:math xmlns:a=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M1\"><a:mi>P<\/a:mi><a:mo>&lt;<\/a:mo><a:mn>0.001<\/a:mn><\/a:math><\/jats:inline-formula>). The area under the curve (AUC) of the radiomic model was 0.990 and 0.858, respectively, in the training and validation sets. The clinical feature-based model, including MRI-reported status and palpability, performed slightly worse, with an AUC of 0.784 in the training cohort and 0.789 in the validation cohort. The radiomic signature was confirmed to provide more clinical benefits by decision curve analysis. Conclusions. The radiomic-based model developed in this study can successfully diagnose the status of lymph nodes in patients with breast cancer, which may reduce unnecessary invasive clinical operations.<\/jats:p>","DOI":"10.1155\/2022\/1507125","type":"journal-article","created":{"date-parts":[[2022,8,17]],"date-time":"2022-08-17T23:50:16Z","timestamp":1660780216000},"page":"1-12","source":"Crossref","is-referenced-by-count":1,"title":["Radiomic Signature Based on Dynamic Contrast-Enhanced MRI for Evaluation of Axillary Lymph Node Metastasis in Breast Cancer"],"prefix":"10.1155","volume":"2022","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5862-7437","authenticated-orcid":true,"given":"Yanqiu","family":"Tang","sequence":"first","affiliation":[{"name":"Department of Breast Surgery, The First Affiliated Hospital with Nanjing Medical University, Nanjing 210029, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lin","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of General Surgery, The Affiliated Cancer Hospital of Nanjing Medical University & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, Nanjing 210009, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5918-4612","authenticated-orcid":true,"given":"Yating","family":"Qiao","sequence":"additional","affiliation":[{"name":"Department of Gastrointestinal Surgery, Affiliated Hospital of Hebei University, Baoding, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weifeng","family":"Li","sequence":"additional","affiliation":[{"name":"School of Electronic Science and Engineering, Nanjing University, Nanjing 210046, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rong","family":"Deng","sequence":"additional","affiliation":[{"name":"Department of General Surgery, The Affiliated Cancer Hospital of Nanjing Medical University & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, Nanjing 210009, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3230-8555","authenticated-orcid":true,"given":"Mengdi","family":"Liang","sequence":"additional","affiliation":[{"name":"Department of Breast Surgery, The First Affiliated Hospital with Nanjing Medical University, Nanjing 210029, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"doi-asserted-by":"publisher","key":"1","DOI":"10.3322\/caac.21442"},{"doi-asserted-by":"publisher","key":"2","DOI":"10.3322\/caac.21349"},{"doi-asserted-by":"publisher","key":"3","DOI":"10.1200\/JCO.1999.17.8.2334"},{"doi-asserted-by":"publisher","key":"4","DOI":"10.1200\/JCO.2008.19.7418"},{"doi-asserted-by":"publisher","key":"5","DOI":"10.1016\/j.ejrad.2015.10.003"},{"doi-asserted-by":"publisher","key":"6","DOI":"10.1097\/01.sla.0000245472.47748.ec"},{"doi-asserted-by":"publisher","key":"7","DOI":"10.1007\/s10549-013-2509-y"},{"doi-asserted-by":"publisher","key":"8","DOI":"10.1148\/radiol.2015151169"},{"doi-asserted-by":"publisher","key":"9","DOI":"10.1002\/jmri.26701"},{"doi-asserted-by":"publisher","key":"10","DOI":"10.1001\/jama.2017.11470"},{"doi-asserted-by":"publisher","key":"11","DOI":"10.1001\/jama.2011.90"},{"doi-asserted-by":"publisher","key":"12","DOI":"10.1007\/s10434-999-0756-2"},{"doi-asserted-by":"publisher","key":"13","DOI":"10.1016\/j.cca.2017.04.023"},{"doi-asserted-by":"publisher","key":"14","DOI":"10.1186\/s12967-018-1692-3"},{"doi-asserted-by":"publisher","key":"15","DOI":"10.1093\/annonc\/mdr304"},{"doi-asserted-by":"publisher","key":"16","DOI":"10.1016\/j.mri.2014.07.001"},{"doi-asserted-by":"publisher","key":"17","DOI":"10.1016\/j.jcm.2016.02.012"},{"issue":"1","key":"18","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1111\/j.1467-9868.2007.00577.x","article-title":"Regression coefficient and autoregressive order shrinkage and selection via the lasso","volume":"69","author":"H. 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