{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T05:05:13Z","timestamp":1784005513731,"version":"3.55.0"},"reference-count":28,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T00:00:00Z","timestamp":1783987200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Oncol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>This study aimed to develop and validate a nomogram integrating multimodal imaging and clinical features for the preoperative prediction of human epidermal growth factor receptor 2 (HER2) expression status in patients with invasive breast cancer.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A total of 204 patients with pathologically confirmed breast cancer who underwent preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and mammography (MG) were retrospectively enrolled. Patients were randomly divided into training and test sets in a 7:3 ratio. Pearson correlation coefficient (PCC) combined with recursive feature elimination (RFE) was used to select optimal radiomics features. Three binary classification tasks were constructed (Model_1: HER2-Positive vs. HER2-Zero; Model_2: HER2-Positive vs. HER2-Low; Model_3: HER2-Low vs. HER2-Zero), each containing three sub-models based on MG, DCE-MRI, and their combination. The optimal radiomics model for each task was selected and integrated with independent clinicopathological factors to establish a clinical-radiomics nomogram.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The combined radiomics model achieved the best performance for Model_1 and Model_2, with area under the curve (AUC) values of 0.742 (95% confidence interval [CI]: 0.624\u20130.860) and 0.823 (95% CI: 0.749\u20130.897) in the training set, and 0.718 (95% CI: 0.594\u20130.842) and 0.778 (95% CI: 0.696\u20130.861) in the validation set, respectively. Corresponding nomograms were subsequently constructed incorporating independent clinical predictors. For Model_3, the single DCE-MRI model demonstrated optimal performance (training AUC\u00a0=\u00a00.831, 95% CI: 0.747\u20130.916; validation AUC\u00a0=\u00a00.745, 95% CI: 0.640\u20130.850). However, no nomogram was developed due to the absence of significant clinicopathological features (p &amp;gt; 0.05).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The multimodal radiomics-based nomogram, integrating imaging features and clinical factors, provides a promising non-invasive quantitative tool for preoperative evaluation of HER2 expression status in breast cancer, offering significant potential for clinical translation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fonc.2026.1829333","type":"journal-article","created":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T04:22:15Z","timestamp":1784002935000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Development and validation of a multimodality radiomics-based nomogram for predicting HER2 expression status in invasive breast cancer"],"prefix":"10.3389","volume":"16","author":[{"given":"Xianwei","family":"Yang","sequence":"first","affiliation":[{"name":"Department of Radiology, Affiliated Hospital of Guizhou Medical University","place":["Guiyang, China"]},{"name":"The People\u2019s Hospital of Qixingguan","place":["Bijie, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoling","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Radiology, Affiliated Hospital of Guizhou Medical University","place":["Guiyang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shenghong","family":"Wan","sequence":"additional","affiliation":[{"name":"Department of Radiology, Affiliated Hospital of Guizhou Medical University","place":["Guiyang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yilin","family":"Luo","sequence":"additional","affiliation":[{"name":"Department of Radiology, Affiliated Hospital of Guizhou Medical 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