{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T09:47:24Z","timestamp":1782899244183,"version":"3.54.5"},"reference-count":24,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"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>Non-mass breast carcinoma (NMBC) is a diagnostically challenging subtype with sonographic features that often overlap with mastitis, leading to misdiagnosis and delayed treatment. Unlike mastitis, which typically warrants conservative therapy, NMBC requires prompt oncological management, making accurate preoperative differentiation critically important. Using transverse, longitudinal, and fused biplanar ultrasound images, we constructed radiomics models to distinguish NMBC from mastitis and evaluated the incremental diagnostic value of combining radiomics features with clinical variables.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We retrospectively analyzed data from 139 patients (NMBC, 63; mastitis, 76) and extracted radiomics features from manually segmented transverse and longitudinal ultrasound images. After feature stability screening and least absolute shrinkage and selection operator selection, we constructed three radiomics logistic regression models for transverse, longitudinal, and fused imaging data, respectively, and additionally developed clinical variable-based models (age and BI-RADS) and combined clinical\u2013radiomics models. The models were trained and internally validated using a 70\/30 split, performance was assessed based on area under the curve (AUC), accuracy, sensitivity, specificity, and decision curve analysis (DCA) values.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>In the training cohort, the fusion model (combining features from transverse and longitudinal images) outperformed single-plane models (AUC = 94.2%, accuracy = 87.6%). However, this advantage diminished in the validation cohort, where the transverse, longitudinal, and fusion radiomics models showed comparable performance (AUC = 0.730, 0.823, and 0.800, respectively; accuracy = 69.0%, 78.6%, and 78.6%, respectively). DeLong test revealed no significant differences among the models in the validation cohort. The combined clinical\u2013radiomics model outperformed both radiomics and clinical variable-based models (AUC: 0.861\u20130.884 in the validation cohort); likelihood ratio tests confirmed that radiomics features and clinical variables contributed independently to the prediction. Calibration and DCA revealed that the fusion model showed superior clinical utility in the training cohort but demonstrated limited generalizability in the validation cohort.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Biplanar ultrasound imaging-based radiomics models show promise for distinguishing NMBC from mastitis. Although the fusion model excelled in the training cohort, it offered limited advantage internal hold-out validation. Integrating clinical variables with radiomics features offers added diagnostic value beyond either approach alone, suggesting that a combined clinical\u2013radiomics strategy may be more clinically viable.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fonc.2026.1785714","type":"journal-article","created":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T08:59:44Z","timestamp":1782896384000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Radiomics analysis of biplanar ultrasound images can discriminate non-mass breast carcinoma from mastitis"],"prefix":"10.3389","volume":"16","author":[{"given":"Qinfu","family":"Wu","sequence":"first","affiliation":[{"name":"Department of Ultrasound, Zhuhai Hospital, Guangdong Provincial Hospital of Traditional Chinese Medicine","place":["Zhuhai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guangde","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Ultrasound, Zhuhai Hospital, Guangdong Provincial Hospital of Traditional Chinese Medicine","place":["Zhuhai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengqiang","family":"Xiao","sequence":"additional","affiliation":[{"name":"Department of Radiology, Guangdong Hospital of Traditional Chinese Medicine","place":["Zhuhai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shanghuang","family":"Xie","sequence":"additional","affiliation":[{"name":"Lab of Molecular Imaging and Medical Intelligence, Department of Radiology, Longgang Central Hospital of Shenzhen\/Shenzhen Clinical Medical College, Guangzhou University of Chinese Medicine\/Longgang Clinical Institute of Shantou University Medical College","place":["Shenzhen, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenhui","family":"Teng","sequence":"additional","affiliation":[{"name":"Department of Ultrasound, Zhuhai Hospital, Guangdong Provincial Hospital of Traditional Chinese Medicine","place":["Zhuhai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Dong","sequence":"additional","affiliation":[{"name":"Department of Ultrasound, Zhuhai Hospital, Guangdong Provincial Hospital of Traditional Chinese Medicine","place":["Zhuhai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoyi","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Ultrasound, Zhuhai Hospital, Guangdong Provincial Hospital of Traditional Chinese Medicine","place":["Zhuhai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianzhu","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Radiology, Guangdong Hospital of Traditional Chinese Medicine","place":["Zhuhai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peikai","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Radiology, Guangdong Hospital of Traditional Chinese Medicine","place":["Zhuhai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,7,1]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21660","article-title":"Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"71","author":"Sung","year":"2021","journal-title":"CA: A Cancer J For Clin"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2023.1161583","article-title":"Editorial: Reviews in breast cancer","volume":"13","author":"De Miglio","year":"2023","journal-title":"Front Oncol"},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.1177\/0284185116683574","article-title":"Non-mass breast lesions on ultrasound: final outcomes and predictors of Malignancy","volume":"58","author":"Park","year":"2017","journal-title":"Acta Radiol"},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2015151169","article-title":"Radiomics: Images are more than pictures, they are data","volume":"278","author":"Gillies","year":"2016","journal-title":"Radiology"},{"key":"B5","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0292110","article-title":"Enhanced and unenhanced: Radiomics models for discriminating between benign and Malignant cystic renal masses on CT images: A multi-center study","volume":"18","author":"Huang","year":"2023","journal-title":"PloS One"},{"key":"B6","doi-asserted-by":"publisher","DOI":"10.4274\/dir.2023.232386","article-title":"Cystic renal mass screening: machine-learning-based radiomics on unenhanced computed tomography","volume":"30","author":"Huang","year":"2024","journal-title":"Diagn Interventional Radiol"},{"key":"B7","doi-asserted-by":"publisher","DOI":"10.1186\/s13244-022-01349-7","article-title":"Deep learning and radiomic feature-based blending ensemble classifier for Malignancy risk prediction in cystic renal lesions","volume":"14","author":"He","year":"2023","journal-title":"Insights Imaging"},{"key":"B8","doi-asserted-by":"publisher","DOI":"10.1016\/j.xcrm.2024.101719","article-title":"Radiomics in breast cancer: Current advances and future directions","volume":"5","author":"Qi","year":"2024","journal-title":"Cell Rep Med"},{"key":"B9","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2022.963612","article-title":"Ultrasound radiomics in personalized breast management: Current status and future prospects","volume":"12","author":"Gu","year":"2022","journal-title":"Front Oncol"},{"key":"B10","doi-asserted-by":"publisher","DOI":"10.1007\/s40477-025-01025-8","article-title":"Diagnostic value of ultrasound radiomic features in differentiating benign and Malignant breast lesions","volume":"28","author":"Gong","year":"2025","journal-title":"J Ultrasound"},{"key":"B11","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2023.1158736","article-title":"Conventional ultrasound and contrast-enhanced ultrasound radiomics in breast cancer and molecular subtype diagnosis","volume":"13","author":"Gong","year":"2023","journal-title":"Front Oncol"},{"key":"B12","doi-asserted-by":"publisher","DOI":"10.1001\/jamainternmed.2024.4224","article-title":"Breast cancer screening using mammography, digital breast tomosynthesis, and magnetic resonance imaging by breast density","volume":"184","author":"Stout","year":"2024","journal-title":"JAMA Intern Med"},{"key":"B13","doi-asserted-by":"publisher","DOI":"10.7759\/cureus.59054","article-title":"Comparative effectiveness of mammography, ultrasound, and MRI in the detection of breast carcinoma in dense breast tissue: A systematic review","volume":"16","author":"Abu Abeelh","year":"2024","journal-title":"Cureus"},{"key":"B14","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1186\/s13244-024-01653-4","article-title":"The paradox of MRI for breast cancer screening: high-risk and dense breasts-available evidence and current practice","volume":"15","author":"Sardanelli","year":"2024","journal-title":"Insights Imaging"},{"key":"B15","doi-asserted-by":"publisher","first-page":"1859","DOI":"10.3390\/diagnostics11101859","article-title":"Classification of breast cancer lesions in ultrasound images by using attention layer and loss ensemble in deep convolutional neural networks","volume":"11","author":"Kalafi","year":"2021","journal-title":"Diagnostics (Basel)"},{"key":"B16","doi-asserted-by":"publisher","DOI":"10.1007\/s11307-019-01383-w","article-title":"Radiomic signatures derived from diffusion-weighted imaging for the assessment of breast cancer receptor status and molecular subtypes","volume":"22","author":"Leithner","year":"2020","journal-title":"Mol Imaging Biol"},{"key":"B17","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2019.108782","article-title":"Differentiation between idiopathic granulomatous mastitis and invasive breast carcinoma, both presenting with non-mass enhancement without rim-enhanced masses: The value of whole-lesion histogram and texture analysis using apparent diffusion coefficient","volume":"123","author":"Zhao","year":"2020","journal-title":"Eur J Radiol"},{"key":"B18","doi-asserted-by":"publisher","DOI":"10.1158\/0008-5472.Can-17-0339","article-title":"Computational radiomics system to decode the radiographic phenotype","volume":"77","author":"van Griethuysen","year":"2017","journal-title":"Cancer Res"},{"key":"B19","doi-asserted-by":"publisher","DOI":"10.3348\/kjr.2019.0855","article-title":"Radiomics in breast imaging from techniques to clinical applications: A review","volume":"21","author":"Lee","year":"2020","journal-title":"Korean J Radiol"},{"key":"B20","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-024-11260-y","article-title":"Methodological issues in radiomics: impact on accuracy of MRI for predicting response to neoadjuvant chemotherapy in breast cancer","volume":"35","author":"Netti","year":"2025","journal-title":"Eur Radiol"},{"key":"B21","doi-asserted-by":"publisher","first-page":"19953","DOI":"10.1038\/s41598-025-03704-2","article-title":"The value of intratumoral and peritumoral ultrasound radiomics model constructed using multiple machine learning algorithms for non-mass breast cancer","volume":"15","author":"Liu","year":"2025","journal-title":"Sci Rep"},{"key":"B22","doi-asserted-by":"publisher","DOI":"10.1177\/15330338251377374","article-title":"Ultrasound video-based radiomics analysis for differentiating benign and Malignant breast lesions","volume":"24","author":"Wu","year":"2025","journal-title":"Technol Cancer Res Treat"},{"key":"B23","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2023.1256146","article-title":"A clinical-radiomics nomogram based on multimodal ultrasound for predicting the Malignancy risk in solid hypoechoic breast lesions","volume":"13","author":"Shiyan","year":"2023","journal-title":"Front Oncol"},{"key":"B24","doi-asserted-by":"publisher","DOI":"10.1093\/bjr\/tqae199","article-title":"Value of contrast-enhanced ultrasound in differentiating granulomatous mastitis from invasive ductal carcinoma","volume":"97","author":"Xu","year":"2024","journal-title":"Br J Radiol"}],"container-title":["Frontiers in Oncology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fonc.2026.1785714\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T08:59:51Z","timestamp":1782896391000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fonc.2026.1785714\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,1]]},"references-count":24,"alternative-id":["10.3389\/fonc.2026.1785714"],"URL":"https:\/\/doi.org\/10.3389\/fonc.2026.1785714","relation":{},"ISSN":["2234-943X"],"issn-type":[{"value":"2234-943X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,1]]},"article-number":"1785714"}}