{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T13:22:54Z","timestamp":1791206574497,"version":"4.1.0"},"reference-count":31,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,5,12]],"date-time":"2025-05-12T00:00:00Z","timestamp":1747008000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,5,12]],"date-time":"2025-05-12T00:00:00Z","timestamp":1747008000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cancer Imaging"],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:sec>\n            <jats:title>Purpose<\/jats:title>\n            <jats:p>According to the updated classification system, human epidermal growth factor receptor 2 (HER2) expression statuses are divided into the following three groups: HER2-over-expression, HER2-low-expression, and HER2-zero-expression. HER2-negative expression was reclassified into HER2-low-expression and HER2-zero-expression. This study aimed to identify three different HER2 expression statuses for breast cancer (BC) patients using PET\/CT radiomics and clinicopathological characteristics.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Methods and materials<\/jats:title>\n            <jats:p>A total of 315 BC patients who met the inclusion and exclusion criteria from two institutions were retrospectively included. The patients in institution 1 were divided into the training set and the independent validation set according to the ratio of 7:3, and institution 2 was used as the external validation set. According to the results of pathological examination, all BC patients were divided into HER2-over-expression, HER2-low-expression, and HER2-zero-expression. First, PET\/CT radiomic features and clinicopathological features based on each patient were extracted and collected. Second, multiple methods were used to perform feature screening and feature selection. Then, four machine learning classifiers, including logistic regression (LR), k-nearest neighbor (KNN), support vector machine (SVM), and random forest (RF), were constructed to identify HER2-over-expression vs. others, HER2-low-expression vs. others, and HER2-zero-expression vs. others. The receiver operator characteristic (ROC) curve was plotted to measure the model\u2019s predictive power.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>According to the feature screening process, 8, 10, and 2 radiomics features and 2 clinicopathological features were finally selected to construct three prediction models (HER2-over-expression vs. others, HER2-low-expression vs. others, and HER2-zero-expression vs. others). For HER2-over-expression vs. others, the RF model outperformed other models with an AUC value of 0.843 (95%CI: 0.774\u20130.897), 0.785 (95%CI: 0.665\u20130.877), and 0.788 (95%CI: 0.708\u20130.868) in the training set, independent validation set, and external validation set. Concerning HER2-low-expression vs. others, the outperformance of the LR model over other models was identified with an AUC value of 0.783 (95%CI: 0.708\u20130.846), 0.756 (95%CI: 0.634\u20130.854), and 0.779 (95%CI: 0.698\u20130.860) in the training set, independent validation set, and external validation set. Whereas, the KNN model was confirmed as the optimal model to distinguish HER2-zero-expression from others, with an AUC value of 0.929 (95%CI: 0.890\u20130.958), 0.847 (95%CI: 0.764\u20130.910), and 0.835 (95%CI: 0.762\u20130.908) in the training set, independent validation set, and external validation set.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusion<\/jats:title>\n            <jats:p>Combined PET\/CT radiomic models integrating with clinicopathological characteristics are non-invasively predictive of different HER2 statuses of BC patients.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1186\/s40644-025-00880-2","type":"journal-article","created":{"date-parts":[[2025,5,12]],"date-time":"2025-05-12T12:28:10Z","timestamp":1747052890000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Identification of HER2-over-expression, HER2-low-expression, and HER2-zero-expression statuses in breast cancer based on 18F-FDG PET\/CT radiomics"],"prefix":"10.1186","volume":"25","author":[{"given":"Xuefeng","family":"Hou","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kun","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huiwen","family":"Luo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wengui","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaofeng","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,5,12]]},"reference":[{"key":"880_CR1","doi-asserted-by":"publisher","first-page":"394","DOI":"10.3322\/caac.21492","volume":"68","author":"F Bray","year":"2018","unstructured":"Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68:394\u2013424.","journal-title":"CA Cancer J Clin"},{"key":"880_CR2","doi-asserted-by":"publisher","first-page":"17","DOI":"10.3322\/caac.21763","volume":"73","author":"RL Siegel","year":"2023","unstructured":"Siegel RL, Miller KD, Wagle NS, Jemal A. Cancer statistics, 2023. CA Cancer J Clin. 2023;73:17\u201348.","journal-title":"CA Cancer J Clin"},{"key":"880_CR3","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1038\/s41392-019-0069-2","volume":"4","author":"J Wang","year":"2019","unstructured":"Wang J, Xu B. Targeted therapeutic options and future perspectives for HER2-positive breast cancer. Signal Transduct Target Ther. 2019;4:34.","journal-title":"Signal Transduct Target Ther"},{"key":"880_CR4","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1007\/s00005-019-00566-1","volume":"68","author":"A Arab","year":"2020","unstructured":"Arab A, Yazdian-Robati R, Behravan J. HER2-positive breast cancer immunotherapy: a focus on vaccine development. Arch Immunol Ther Exp (Warsz). 2020;68:2.","journal-title":"Arch Immunol Ther Exp (Warsz)"},{"key":"880_CR5","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1056\/NEJMoa2203690","volume":"387","author":"S Modi","year":"2022","unstructured":"Modi S, Jacot W, Yamashita T, Sohn J, Vidal M, Tokunaga E, et al. Trastuzumab deruxtecan in previously treated HER2-low advanced breast cancer. N Engl J Med. 2022;387:9\u201320.","journal-title":"N Engl J Med"},{"key":"880_CR6","doi-asserted-by":"publisher","first-page":"1951","DOI":"10.1200\/JCO.19.02488","volume":"38","author":"P Tarantino","year":"2020","unstructured":"Tarantino P, Hamilton E, Tolaney SM, Cortes J, Morganti S, Ferraro E, et al. HER2-low breast cancer: pathological and clinical landscape. J Clin Oncol. 2020;38:1951\u201362.","journal-title":"J Clin Oncol"},{"key":"880_CR7","doi-asserted-by":"publisher","first-page":"6132","DOI":"10.1007\/s00330-024-10641-7","volume":"34","author":"S Zheng","year":"2024","unstructured":"Zheng S, Yang Z, Du G, Zhang Y, Jiang C, Xu T, et al. Discrimination between HER2-overexpressing, -low-expressing, and -zero-expressing statuses in breast cancer using multiparametric MRI-based radiomics. Eur Radiol. 2024;34:6132\u201344.","journal-title":"Eur Radiol"},{"key":"880_CR8","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1038\/s41523-021-00343-4","volume":"7","author":"F Miglietta","year":"2021","unstructured":"Miglietta F, Griguolo G, Bottosso M, Giarratano T, Lo Mele M, Fassan M, et al. Evolution of HER2-low expression from primary to recurrent breast cancer. NPJ Breast Cancer. 2021;7:137.","journal-title":"NPJ Breast Cancer"},{"key":"880_CR9","doi-asserted-by":"publisher","first-page":"188849","DOI":"10.1016\/j.bbcan.2022.188849","volume":"1878","author":"P Fan","year":"2023","unstructured":"Fan P, Xu K. Antibody-drug conjugates in breast cancer: marching from HER2-overexpression into HER2-low. Biochim Biophys Acta Rev Cancer. 2023;1878:188849.","journal-title":"Biochim Biophys Acta Rev Cancer"},{"key":"880_CR10","doi-asserted-by":"crossref","unstructured":"Prat A, Bardia A, Curigliano G, Hammond MEH, Loibl S, Tolaney SM et al. An overview of clinical development of agents for metastatic or advanced breast cancer without ERBB2 amplification (HER2-low). JAMA Oncol. 2022;8(11):1676\u20131687.","DOI":"10.1001\/jamaoncol.2022.4175"},{"key":"880_CR11","doi-asserted-by":"crossref","unstructured":"Kazerouni AS, Hormuth DA 2nd, Davis T, Bloom MJ, Mounho S, Rahman G et al. Quantifying tumor heterogeneity via MRI habitats to characterize microenvironmental alterations in HER2\u2009+\u2009breast cancer. Cancers (Basel). 2022;14.","DOI":"10.3390\/cancers14071837"},{"key":"880_CR12","doi-asserted-by":"publisher","first-page":"475","DOI":"10.52586\/4960","volume":"26","author":"J Liu","year":"2021","unstructured":"Liu J, Bian H, Zhang Y, Gao Y, Yin G, Wang Z, et al. Molecular subtype classification of breast cancer using established radiomic signature models based on (18)F-FDG PET\/CT images. Front Biosci (Landmark Ed). 2021;26:475\u201384.","journal-title":"Front Biosci (Landmark Ed)"},{"key":"880_CR13","doi-asserted-by":"publisher","first-page":"508","DOI":"10.1053\/j.semnuclmed.2022.05.001","volume":"52","author":"D Groheux","year":"2022","unstructured":"Groheux D. FDG-PET\/CT for primary staging and detection of recurrence of breast cancer. Semin Nucl Med. 2022;52:508\u201319.","journal-title":"Semin Nucl Med"},{"key":"880_CR14","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1007\/s00432-023-05519-y","volume":"150","author":"F Gao","year":"2024","unstructured":"Gao F, Liu F, Wang J, Bi J, Zhai L, Li D. Molecular probes targeting HER2 PET\/CT and their application in advanced breast cancer. J Cancer Res Clin Oncol. 2024;150:118.","journal-title":"J Cancer Res Clin Oncol"},{"key":"880_CR15","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1016\/j.ejca.2011.11.036","volume":"48","author":"P Lambin","year":"2012","unstructured":"Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout RG, Granton P, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer. 2012;48:441\u20136.","journal-title":"Eur J Cancer"},{"key":"880_CR16","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1007\/s12149-021-01688-3","volume":"36","author":"Y Chen","year":"2022","unstructured":"Chen Y, Wang Z, Yin G, Sui C, Liu Z, Li X, et al. Prediction of HER2 expression in breast cancer by combining PET\/CT radiomic analysis and machine learning. Ann Nucl Med. 2022;36:172\u201382.","journal-title":"Ann Nucl Med"},{"key":"880_CR17","doi-asserted-by":"publisher","first-page":"111050","DOI":"10.1016\/j.ejrad.2023.111050","volume":"167","author":"J Liu","year":"2023","unstructured":"Liu J, Zhang Z, Bian H, Zhang Y, Ma W, Wang Z, et al. Predictive value of radiomic signature based on 2-[(18)F]FDG PET\/CT in HER2 status determination for primary breast cancer with equivocal IHC results. Eur J Radiol. 2023;167:111050.","journal-title":"Eur J Radiol"},{"key":"880_CR18","doi-asserted-by":"publisher","first-page":"1468","DOI":"10.1007\/s00259-019-04313-8","volume":"46","author":"L Antunovic","year":"2019","unstructured":"Antunovic L, De Sanctis R, Cozzi L, Kirienko M, Sagona A, Torrisi R, et al. PET\/CT radiomics in breast cancer: promising tool for prediction of pathological response to neoadjuvant chemotherapy. Eur J Nucl Med Mol Imaging. 2019;46:1468\u201377.","journal-title":"Eur J Nucl Med Mol Imaging"},{"key":"880_CR19","doi-asserted-by":"publisher","first-page":"1869","DOI":"10.1007\/s00259-023-06150-2","volume":"50","author":"K Chen","year":"2023","unstructured":"Chen K, Wang J, Li S, Zhou W, Xu W. Predictive value of (18)F-FDG PET\/CT-based radiomics model for neoadjuvant chemotherapy efficacy in breast cancer: a multi-scanner\/center study with external validation. Eur J Nucl Med Mol Imaging. 2023;50:1869\u201380.","journal-title":"Eur J Nucl Med Mol Imaging"},{"key":"880_CR20","doi-asserted-by":"publisher","first-page":"645","DOI":"10.1016\/j.annonc.2023.05.008","volume":"34","author":"P Tarantino","year":"2023","unstructured":"Tarantino P, Viale G, Press MF, Hu X, Penault-Llorca F, Bardia A, et al. ESMO expert consensus statements (ECS) on the definition, diagnosis, and management of HER2-low breast cancer. Ann Oncol. 2023;34:645\u201359.","journal-title":"Ann Oncol"},{"key":"880_CR21","doi-asserted-by":"publisher","first-page":"1151","DOI":"10.1016\/S1470-2045(21)00301-6","volume":"22","author":"C Denkert","year":"2021","unstructured":"Denkert C, Seither F, Schneeweiss A, Link T, Blohmer JU, Just M, et al. Clinical and molecular characteristics of HER2-low-positive breast cancer: pooled analysis of individual patient data from four prospective, neoadjuvant clinical trials. Lancet Oncol. 2021;22:1151\u201361.","journal-title":"Lancet Oncol"},{"issue":"Suppl 1","key":"880_CR22","doi-asserted-by":"publisher","first-page":"S12","DOI":"10.1016\/j.breast.2022.01.006","volume":"62","author":"N Harbeck","year":"2022","unstructured":"Harbeck N. Neoadjuvant and adjuvant treatment of patients with HER2-positive early breast cancer. Breast. 2022;62(Suppl 1):S12\u20136.","journal-title":"Breast"},{"key":"880_CR23","doi-asserted-by":"publisher","first-page":"1139","DOI":"10.1016\/S1470-2045(21)00288-6","volume":"22","author":"Trastuzumab for early-stage","year":"2021","unstructured":"Trastuzumab for early-stage. HER2-positive breast cancer: a meta-analysis of 13\u2008864 women in seven randomised trials. Lancet Oncol. 2021;22:1139\u201350.","journal-title":"Lancet Oncol"},{"key":"880_CR24","doi-asserted-by":"publisher","first-page":"1887","DOI":"10.1200\/JCO.19.02318","volume":"38","author":"S Modi","year":"2020","unstructured":"Modi S, Park H, Murthy RK, Iwata H, Tamura K, Tsurutani J, et al. Antitumor activity and safety of trastuzumab deruxtecan in patients with HER2-low-expressing advanced breast cancer: results from a phase Ib study. J Clin Oncol. 2020;38:1887\u201396.","journal-title":"J Clin Oncol"},{"key":"880_CR25","doi-asserted-by":"publisher","first-page":"899","DOI":"10.1007\/s00330-023-09990-6","volume":"34","author":"Y Guo","year":"2024","unstructured":"Guo Y, Xie X, Tang W, Chen S, Wang M, Fan Y, et al. Noninvasive identification of HER2-low-positive status by MRI-based deep learning radiomics predicts the disease-free survival of patients with breast cancer. Eur Radiol. 2024;34:899\u2013913.","journal-title":"Eur Radiol"},{"key":"880_CR26","doi-asserted-by":"publisher","first-page":"e222646","DOI":"10.1148\/radiol.222646","volume":"308","author":"T Ramtohul","year":"2023","unstructured":"Ramtohul T, Djerroudi L, Lissavalid E, Nhy C, Redon L, Ikni L, et al. Multiparametric MRI and radiomics for the prediction of HER2-zero, -low, and -positive breast cancers. Radiology. 2023;308:e222646.","journal-title":"Radiology"},{"key":"880_CR27","doi-asserted-by":"publisher","first-page":"2546","DOI":"10.1007\/s00330-023-10198-x","volume":"34","author":"C Mao","year":"2024","unstructured":"Mao C, Hu L, Jiang W, Qiu Y, Yang Z, Liu Y, et al. Discrimination between human epidermal growth factor receptor 2 (HER2)-low-expressing and HER2-overexpressing breast cancers: a comparative study of four MRI diffusion models. Eur Radiol. 2024;34:2546\u201359.","journal-title":"Eur Radiol"},{"key":"880_CR28","doi-asserted-by":"publisher","first-page":"2212","DOI":"10.1002\/jmri.29670","volume":"61","author":"Y Dai","year":"2025","unstructured":"Dai Y, Lian C, Zhang Z, Gao J, Lin F, Li Z, et al. Development and validation of a deep learning system to differentiate HER2-zero, HER2-low, and HER2-positive breast cancer based on dynamic contrast-enhanced MRI. J Magn Reson Imaging. 2025;61:2212\u201320.","journal-title":"J Magn Reson Imaging"},{"key":"880_CR29","doi-asserted-by":"publisher","first-page":"s1013","DOI":"10.1158\/1078-0432.CCR-05-2128","volume":"12","author":"HJ Kim","year":"2006","unstructured":"Kim HJ, Cui X, Hilsenbeck SG, Lee AV. Progesterone receptor loss correlates with human epidermal growth factor receptor 2 overexpression in estrogen receptor-positive breast cancer. Clin Cancer Res. 2006;12:s1013\u20138.","journal-title":"Clin Cancer Res"},{"key":"880_CR30","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1093\/jnci\/95.2.142","volume":"95","author":"G Konecny","year":"2003","unstructured":"Konecny G, Pauletti G, Pegram M, Untch M, Dandekar S, Aguilar Z, et al. Quantitative association between HER-2\/neu and steroid hormone receptors in hormone receptor-positive primary breast cancer. J Natl Cancer Inst. 2003;95:142\u201353.","journal-title":"J Natl Cancer Inst"},{"key":"880_CR31","doi-asserted-by":"publisher","first-page":"922185","DOI":"10.3389\/fonc.2022.922185","volume":"12","author":"C Fang","year":"2022","unstructured":"Fang C, Zhang J, Li J, Shang H, Li K, Jiao T, et al. Clinical-radiomics nomogram for identifying HER2 status in patients with breast cancer: a multicenter study. Front Oncol. 2022;12:922185.","journal-title":"Front Oncol"}],"container-title":["Cancer Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40644-025-00880-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s40644-025-00880-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40644-025-00880-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,12]],"date-time":"2025-05-12T12:28:14Z","timestamp":1747052894000},"score":1,"resource":{"primary":{"URL":"https:\/\/cancerimagingjournal.biomedcentral.com\/articles\/10.1186\/s40644-025-00880-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,12]]},"references-count":31,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["880"],"URL":"https:\/\/doi.org\/10.1186\/s40644-025-00880-2","relation":{},"ISSN":["1470-7330"],"issn-type":[{"value":"1470-7330","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,12]]},"assertion":[{"value":"14 October 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 April 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 May 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Our retrospective study was authorized by the Institutional Ethics Committee, which consented to waive informed consent because the data used in this study has no personally identifiable information of patients.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"62"}}