{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,26]],"date-time":"2026-01-26T20:47:22Z","timestamp":1769460442897,"version":"3.49.0"},"reference-count":52,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,1,9]],"date-time":"2024-01-09T00:00:00Z","timestamp":1704758400000},"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><jats:title>Background<\/jats:title><jats:p>Breast cancer is the key global menace to women\u2019s health, which ranks first by mortality rate. The rate reduction and early diagnostics of breast cancer are the mainstream of medical research. Immunohistochemical examination is the most important link in the process of breast cancer treatment, and its results directly affect physicians\u2019 decision-making on follow-up medical treatment.<\/jats:p><\/jats:sec><jats:sec><jats:title>Purpose<\/jats:title><jats:p>This study aims to develop a computer-aided diagnosis (CAD) method based on deep learning to classify breast ultrasound (BUS) images according to immunohistochemical results.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>A new depth learning framework guided by BUS image data analysis was proposed for the classification of breast cancer nodes in BUS images. The proposed CAD classification network mainly comprised three innovation points. First, a multilevel feature distillation network (MFD-Net) based on CNN, which could extract feature layers of different scales, was designed. Then, the image features extracted at different depths were fused to achieve multilevel feature distillation using depth separable convolution and reverse depth separable convolution to increase convolution depths. Finally, a new attention module containing two independent submodules, the channel attention module (CAM) and the spatial attention module (SAM), was introduced to improve the model classification ability in channel and space.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>A total of 500 axial BUS images were retrieved from 294 patients who underwent BUS examination, and these images were detected and cropped, resulting in breast cancer node BUS image datasets, which were classified according to immunohistochemical findings, and the datasets were randomly subdivided into a training set (70%) and a test set (30%) in the classification process, with the results of the four immune indices output simultaneously from training and testing, in the model comparison experiment. Taking ER immune indicators as an example, the proposed model achieved a precision of 0.8933, a recall of 0.7563, an F1 score of 0.8191, and an accuracy of 0.8386, significantly outperforming the other models. The results of the designed ablation experiment also showed that the proposed multistage characteristic distillation structure and attention module were key in improving the accuracy rate.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The extensive experiments verify the high efficiency of the proposed method. It is considered the first classification of breast cancer by immunohistochemical results in breast cancer image processing, and it provides an effective aid for postoperative breast cancer treatment, greatly reduces the difficulty of diagnosis for doctors, and improves work efficiency.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fonc.2023.1263685","type":"journal-article","created":{"date-parts":[[2024,1,9]],"date-time":"2024-01-09T04:40:51Z","timestamp":1704775251000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Deep learning-based immunohistochemical estimation of breast cancer via ultrasound image applications"],"prefix":"10.3389","volume":"13","author":[{"given":"Ding","family":"Yan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zijian","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiajun","family":"Duan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jia","family":"Qu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Linlin","family":"Shi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qian","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huawei","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2024,1,9]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"299","DOI":"10.1016\/j.patcog.2009.05.012","article-title":"Automated breast cancer detection and classification using ultrasound images: A survey","volume":"43","author":"Cheng","year":"2009","journal-title":"Pattern Recognit"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1097\/MD.0000000000014146","article-title":"A computer-aided diagnosis system using artificial intelligence for diagnosing and characterizing breast masses on ultrasound: Added value for the inexperienced breast radiologist","volume":"98","author":"Park","year":"2019","journal-title":"Med (Baltimore)"},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.1097\/01.MP.0000039566.20817.46","article-title":"Morphological changes in breast tissue with menstrual cycle","volume":"15","author":"Ramakrishnan","year":"2002","journal-title":"Mod Pathol"},{"key":"B4","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1186\/s40164-022-00277-y","article-title":"Heterogeneity of the tumor immune microenvironment and its clinical relevance","volume":"11","author":"Jia","year":"2023","journal-title":"Exp Hematol Oncol"},{"key":"B5","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1007\/s12032-017-0949-7","article-title":"Vascular microinvasion from hepatocellular carcinoma: CT findings and pathologic correlation for the best therapeutic strategies","volume":"34","author":"Reginelli","year":"2017","journal-title":"Med Oncol"},{"key":"B6","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1409.1556","article-title":"Very deep convolutional networks for large-scale image recognition","author":"Simonyan","year":"2014","journal-title":"CoRR abs\/1409.1556"},{"key":"B7","doi-asserted-by":"publisher","DOI":"10.1007\/s11517-020-02302-w","article-title":"Lung cancer histology classification from CT images based on radiomics and deep learning models","volume":"59","author":"Marentakis","year":"2021","journal-title":"Med Biol Eng Comput"},{"key":"B8","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-319-93000-8_83","article-title":"Deep convolutional neural networks for breast cancer histology image analysis","volume-title":"Image Analysis and Recognition. 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