{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,30]],"date-time":"2026-05-30T01:06:57Z","timestamp":1780103217207,"version":"3.54.0"},"reference-count":31,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001691","name":"Japan Society for the Promotion of Science","doi-asserted-by":"publisher","award":["T22K07769"],"award-info":[{"award-number":["T22K07769"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","ejradiology.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["European Journal of Radiology"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1016\/j.ejrad.2026.112824","type":"journal-article","created":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T03:51:28Z","timestamp":1774583488000},"page":"112824","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["A Radiogenomic Model using MRI and Gene Signature to Predict Complete Response in Breast Cancer"],"prefix":"10.1016","volume":"200","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7520-3310","authenticated-orcid":false,"given":"Yukiko","family":"Tokuda","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuki","family":"Suzuki","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Soya","family":"Oda","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yoshiaki","family":"Sota","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yasuto","family":"Naoi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Masahiro","family":"Yanagawa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuichi","family":"Motoyama","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kosuke","family":"Kobayashi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Akinori","family":"Hata","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kazuki","family":"Yamagata","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuhei","family":"Doi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daiki","family":"Nishigaki","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Miyuki","family":"Tomiyama","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Midori","family":"Ueno","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuka","family":"Fujita","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kenzo","family":"Shimazu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eiichi","family":"Morii","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shoji","family":"Kido","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Masatoshi","family":"Hori","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Noriyuki","family":"Tomiyama","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.ejrad.2026.112824_b0005","doi-asserted-by":"crossref","first-page":"2838","DOI":"10.1158\/1078-0432.CCR-19-3492","article-title":"Pathologic complete response after neoadjuvant chemotherapy and impact on breast cancer recurrence and survival: a comprehensive meta-analysis","volume":"26","author":"Spring","year":"2020","journal-title":"Clin. Cancer Res. off. J. Am. Assoc. Cancer Res."},{"key":"10.1016\/j.ejrad.2026.112824_b0010","doi-asserted-by":"crossref","first-page":"1796","DOI":"10.1200\/JCO.2011.38.8595","article-title":"Definition and impact of pathologic complete response on prognosis after neoadjuvant chemotherapy in various intrinsic breast cancer subtypes","volume":"30","author":"von Minckwitz","year":"2012","journal-title":"J. Clin. Oncol. Off. J. Am. Soc. Clin. Oncol."},{"key":"10.1016\/j.ejrad.2026.112824_b0015","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1016\/S0140-6736(13)62422-8","article-title":"Pathological complete response and long-term clinical benefit in breast cancer: the CTNeoBC pooled analysis","volume":"384","author":"Cortazar","year":"2014","journal-title":"Lancet Lond. Engl."},{"issue":"Suppl 2","key":"10.1016\/j.ejrad.2026.112824_b0020","doi-asserted-by":"crossref","first-page":"S73","DOI":"10.1016\/j.breast.2015.07.018","article-title":"Neoadjuvant treatment of breast cancer\u2013Clinical and research perspective","volume":"24","author":"Loibl","year":"2015","journal-title":"Breast Edinb. Scotl."},{"key":"10.1016\/j.ejrad.2026.112824_b0025","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1016\/j.soc.2014.03.006","article-title":"Neoadjuvant therapy in the treatment of breast cancer","volume":"23","author":"Teshome","year":"2014","journal-title":"Surg. Oncol. Clin. N. Am."},{"key":"10.1016\/j.ejrad.2026.112824_b0030","doi-asserted-by":"crossref","first-page":"3538","DOI":"10.1158\/1078-0432.CCR-18-3190","article-title":"Radiomics of multiparametric MRI for pretreatment prediction of pathologic complete response to neoadjuvant chemotherapy in breast cancer: a multicenter study","volume":"25","author":"Liu","year":"2019","journal-title":"Clin. Cancer Res. off. J. Am. Assoc. Cancer Res."},{"key":"10.1016\/j.ejrad.2026.112824_b0035","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1186\/s13058-017-0846-1","article-title":"Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI","volume":"19","author":"Braman","year":"2017","journal-title":"Breast Cancer Res. BCR"},{"key":"10.1016\/j.ejrad.2026.112824_b0040","doi-asserted-by":"crossref","DOI":"10.1148\/radiol.240575","article-title":"Use of pretreatment perfusion MRI-based intratumoral heterogeneity to predict pathologic response of triple-negative breast cancer to neoadjuvant chemoimmunotherapy","volume":"312","author":"Ramtohul","year":"2024","journal-title":"Radiology"},{"key":"10.1016\/j.ejrad.2026.112824_b0045","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1186\/s12967-022-03452-1","article-title":"Integration of radiogenomic features for early prediction of pathological complete response in patients with triple-negative breast cancer and identification of potential therapeutic targets","volume":"20","author":"Zhang","year":"2022","journal-title":"J. Transl. Med."},{"key":"10.1016\/j.ejrad.2026.112824_b0050","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1148\/radiol.14132641","article-title":"Radiogenomic analysis of breast cancer: luminal B molecular subtype is associated with enhancement dynamics at MR imaging","volume":"273","author":"Mazurowski","year":"2014","journal-title":"Radiology"},{"key":"10.1016\/j.ejrad.2026.112824_b0055","doi-asserted-by":"crossref","DOI":"10.1016\/j.displa.2024.102698","article-title":"MRI and RNA-seq fusion for prediction of pathological response to neoadjuvant chemotherapy in breast cancer","volume":"83","author":"Li","year":"2024","journal-title":"Displays"},{"key":"10.1016\/j.ejrad.2026.112824_b0060","doi-asserted-by":"crossref","DOI":"10.7759\/cureus.86287","article-title":"Integrative radiogenomics using MRI radiomics and microarray gene expression analysis to predict pathological complete response in patients with breast cancer undergoing neoadjuvant chemotherapy","author":"Oda","year":"2025","journal-title":"Cureus"},{"key":"10.1016\/j.ejrad.2026.112824_b0065","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1093\/annonc\/mdt427","article-title":"Construction of novel immune-related signature for prediction of pathological complete response to neoadjuvant chemotherapy in human breast cancer","volume":"25","author":"Sota","year":"2014","journal-title":"Ann. Oncol. off. J. Eur. Soc. Med. Oncol."},{"key":"10.1016\/j.ejrad.2026.112824_b0070","doi-asserted-by":"crossref","first-page":"566","DOI":"10.1007\/s12282-025-01687-6","article-title":"IRSN-23 gene diagnosis enhances breast cancer subtype classification and predicts response to neoadjuvant chemotherapy: new validation analyses","volume":"32","author":"Sota","year":"2025","journal-title":"Breast Cancer Tokyo Jpn."},{"key":"10.1016\/j.ejrad.2026.112824_b0075","article-title":"STARD Group, STARD 2015: an updated list of essential items for reporting diagnostic accuracy studies","volume":"351","author":"Bossuyt","year":"2015","journal-title":"BMJ"},{"key":"10.1016\/j.ejrad.2026.112824_b0080","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1007\/s10549-018-4990-9","article-title":"Multivariate machine learning models for prediction of pathologic response to neoadjuvant therapy in breast cancer using MRI features: a study using an independent validation set","volume":"173","author":"Cain","year":"2019","journal-title":"Breast Cancer Res. Treat."},{"key":"10.1016\/j.ejrad.2026.112824_b0085","doi-asserted-by":"crossref","DOI":"10.1016\/j.ejrad.2022.110220","article-title":"A model combining pretreatment MRI radiomic features and tumor-infiltrating lymphocytes to predict response to neoadjuvant systemic therapy in triple-negative breast cancer","volume":"149","author":"Jimenez","year":"2022","journal-title":"Eur. J. Radiol."},{"key":"10.1016\/j.ejrad.2026.112824_b0090","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1186\/s12880-024-01198-4","article-title":"Prediction of neoadjuvant chemotherapy pathological complete response for breast cancer based on radiomics nomogram of intratumoral and derived tissue","volume":"24","author":"Zheng","year":"2024","journal-title":"BMC Med. Imaging"},{"key":"10.1016\/j.ejrad.2026.112824_b0095","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1148\/radiol.2018172462","article-title":"Intratumoral spatial heterogeneity at perfusion MR imaging predicts recurrence-free survival in locally advanced breast cancer treated with neoadjuvant chemotherapy","volume":"288","author":"Wu","year":"2018","journal-title":"Radiology"},{"key":"10.1016\/j.ejrad.2026.112824_b0100","first-page":"216","article-title":"Tumor sphericity predicts response in neoadjuvant chemotherapy for invasive breast cancer","volume":"6","author":"Li","year":"2020","journal-title":"Tomogr. Ann Arbor Mich"},{"key":"10.1016\/j.ejrad.2026.112824_b0105","doi-asserted-by":"crossref","first-page":"7265","DOI":"10.1200\/JCO.2005.02.0818","article-title":"Gene expression profiles in paraffin-embedded core biopsy tissue predict response to chemotherapy in women with locally advanced breast cancer","volume":"23","author":"Gianni","year":"2005","journal-title":"J. Clin. Oncol. Off. J. Am. Soc. Clin. Oncol."},{"key":"10.1016\/j.ejrad.2026.112824_b0110","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1186\/s40644-024-00746-z","article-title":"A preoperative radiogenomic model based on quantitative heterogeneity for predicting outcomes in triple-negative breast cancer patients who underwent neoadjuvant chemotherapy, cancer Imaging off","volume":"24","author":"Zhou","year":"2024","journal-title":"Publ. Int. Cancer Imaging Soc."},{"key":"10.1016\/j.ejrad.2026.112824_b0115","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1186\/s13058-024-01905-7","article-title":"Predicting pathologic complete response to neoadjuvant chemotherapy in breast cancer using a machine learning approach","volume":"26","author":"Zhao","year":"2024","journal-title":"Breast Cancer Res. BCR"},{"key":"10.1016\/j.ejrad.2026.112824_b0120","doi-asserted-by":"crossref","first-page":"21256","DOI":"10.1002\/cam4.6704","article-title":"Imaging\u2010proteomic analysis for prediction of neoadjuvant chemotherapy responses in patients with breast cancer","volume":"12","author":"Duan","year":"2023","journal-title":"Cancer Med."},{"key":"10.1016\/j.ejrad.2026.112824_b0125","doi-asserted-by":"crossref","DOI":"10.1002\/advs.202508190","article-title":"Interpretable multimodal fusion model enhances postoperative recurrence prediction in gastric cancer","volume":"12","author":"Ding","year":"2025","journal-title":"Adv. Sci."},{"key":"10.1016\/j.ejrad.2026.112824_b0130","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/S1470-2045(17)30904-X","article-title":"Tumour-infiltrating lymphocytes and prognosis in different subtypes of breast cancer: a pooled analysis of 3771 patients treated with neoadjuvant therapy","volume":"19","author":"Denkert","year":"2018","journal-title":"Lancet Oncol."},{"key":"10.1016\/j.ejrad.2026.112824_b0135","doi-asserted-by":"crossref","DOI":"10.3389\/fonc.2022.943326","article-title":"Radiogenomics analysis reveals the associations of dynamic contrast-enhanced-MRI features with gene expression characteristics, PAM50 subtypes, and prognosis of breast cancer","volume":"12","author":"Ming","year":"2022","journal-title":"Front. Oncol."},{"key":"10.1016\/j.ejrad.2026.112824_b0140","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1002\/jmri.25119","article-title":"Breast cancer molecular subtype classifier that incorporates MRI features","volume":"44","author":"Sutton","year":"2016","journal-title":"J. Magn. Reson. Imaging JMRI"},{"key":"10.1016\/j.ejrad.2026.112824_b0145","doi-asserted-by":"crossref","first-page":"617","DOI":"10.1056\/NEJMoa1814017","article-title":"Trastuzumab emtansine for residual invasive HER2-positive breast cancer","volume":"380","author":"von Minckwitz","year":"2019","journal-title":"N. Engl. J. Med."},{"key":"10.1016\/j.ejrad.2026.112824_b0150","doi-asserted-by":"crossref","first-page":"868","DOI":"10.1038\/s41586-023-06498-3","article-title":"Spatial predictors of immunotherapy response in triple-negative breast cancer","volume":"621","author":"Wang","year":"2023","journal-title":"Nature"},{"key":"10.1016\/j.ejrad.2026.112824_b0155","doi-asserted-by":"crossref","first-page":"e2245","DOI":"10.7717\/peerj-cs.2245","article-title":"Trade-off between training and testing ratio in machine learning for medical image processing","volume":"10","author":"Sivakumar","year":"2024","journal-title":"PeerJ Comput. Sci."}],"container-title":["European Journal of Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0720048X26001725?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0720048X26001725?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,30]],"date-time":"2026-05-30T00:39:14Z","timestamp":1780101554000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0720048X26001725"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":31,"alternative-id":["S0720048X26001725"],"URL":"https:\/\/doi.org\/10.1016\/j.ejrad.2026.112824","relation":{},"ISSN":["0720-048X"],"issn-type":[{"value":"0720-048X","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A Radiogenomic Model using MRI and Gene Signature to Predict Complete Response in Breast Cancer","name":"articletitle","label":"Article Title"},{"value":"European Journal of Radiology","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ejrad.2026.112824","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"112824"}}