{"id":42191,"date":"2023-11-20T02:17:09","date_gmt":"2023-11-20T08:17:09","guid":{"rendered":"https:\/\/stage.cancerimagingarchive.net\/tcia-citation\/duke-breast-cancer-mri-pub\/"},"modified":"2023-11-20T02:17:09","modified_gmt":"2023-11-20T08:17:09","slug":"duke-breast-cancer-mri-pub","status":"publish","type":"tcia_citation","link":"https:\/\/stage.cancerimagingarchive.net\/tcia-citation\/duke-breast-cancer-mri-pub\/","title":{"rendered":"DUKE-BREAST-CANCER-MRI-PUB"},"template":"","class_list":["post-42191","tcia_citation","type-tcia_citation","status-publish"],"tcia_citation_type":"Publication Citation","tcia_citation_text":"<p><span> Saha, A., Harowicz, M. R., Grimm, L. J., Kim, C. E., Ghate, S. V., Walsh, R., &amp; Mazurowski, M. A. (2018). <strong>A machine learning approach to radiogenomics of breast cancer: a study of 922 subjects and 529 DCE-MRI features.<\/strong> British journal of cancer, 119(4), 508-516. DOI:\u00a0<a href=\"https:\/\/doi.org\/10.1038\/s41416-018-0185-8\">https:\/\/doi.org\/10.1038\/s41416-018-0185-8<\/a> ,\u00a0 <a href=\"https:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC6134102\/\">PMC6134102<\/a><br \/><\/span><\/p>\n","tcia_citation_statement":"","tcia_citation_doi":"10.1038\/s41416-018-0185-8","_links":{"self":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/v1\/citations\/42191","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/v1\/citations"}],"about":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/wp\/v2\/types\/tcia_citation"}],"wp:attachment":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/wp\/v2\/media?parent=42191"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}