[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125404-en":3,"doc-seo-125404-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125404,1374391974564,"Clementine","https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002",7,"Healthcare","Machine learning model for predicting epidermal growth factor receptor expression status in breast cancer using ultrasound radiomics","EGFR is a clinically important target because its expression in breast cancer patients affects both overall and disease-free survival. Current EGFR assessment relies on invasive procedures, which increase discomfort, procedural risk, and testing cost. This study develops a machine learning approach that uses ultrasound radiomics to non-invasively predict EGFR expression status, combining feature reproducibility filtering, LASSO-based selection, and model interpretability with SHAP.","TYPE Original Research PUBLISHED 17 October 2025 DOI 10.3389/fonc.2025.1683164  \nOPEN ACCESS  \nEDITED BY  \nMohamed Shehata,  \nMidway College, United States  \nREVIEWED BY  \nDeepak Nag Ayyala,  \nTakeda Oncology, United States Dimitris Filos,  \nAristotle University of Thessaloniki, Greece  \n*CORRESPONDENCE  \nGuorong Lyu  \n [lgr_feus@sina.com](lgr_feus@sina.com)[ ](lgr_feus@sina.com)Shanshan Su  \n [susan@fjmu.edu.cn](susan@fjmu.edu.cn)  \n†These authors have contributed equally to this work  \nRECEIVED 10 August 2025  \nACCEPTED 06 October 2025  \nPUBLISHED 17 October 2025  \nCITATION  \nXu Z, Ye J, Zhong H, Chen J, Wang H, Zhang X, Lyu G and Su S (2025) Machine learning model for predicting epidermal growth factor receptor expression status in breast cancer using ultrasound radiomics. Front. Oncol. 15:1683164 .  \ndoi: 10.3389/fonc.2025.1683164  \nCOPYRIGHT  \n© 2025 Xu, Ye, Zhong, Chen, Wang, Zhang, Lyu and Su. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nMachine learning model for predicting epidermal growth factor receptor expression status in breast cancer using ultrasound radiomics  \nZhirong Xu 1†, Jiayi Ye 2†, Huohu Zhong 1†, Jiemin Chen 1, Han Wang 1, Xiaoqian Zhang 1, Guorong Lyu 1* and Shanshan Su 1*  \n1 Department of Ultrasound, The Second Affiliated Hospital of Fujian Medical University,  \nQuanzhou, China, 2 Department of Nuclear Medicine, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China  \nBackground/objectives: The epidermal growth factor receptor (EGFR) is a clinically important target, as its expression in patients with breast cancer influences both overall and disease-free survival. Current methods for assessing EGFR expression status in a patient are invasive. Therefore, in this study, we developed a machine learning-based approach utilizing ultrasound radiomics to non-invasively predict EGFR expression status in patients with breast cancer.  \nMethods: Radiomic features were extracted from grayscale and wavelettransformed ultrasound images of 321 patients. The dataset was randomly split into training (n = 225) and test (n = 96) sets at a 7:3 ratio with stratified sampling topreserve the EGFR+/– ratio. Key predictors were identified using a multi-step procedure—including reproducibility filtering (ICC > 0 . 75), univariate F-test filtering (p \u003C 0 . 05), and L1-regularized selection via LASSO regression. Seven machine-learning models were trained. Model interpretability was assessed using SHAP (Shapley Additive Explanations) . In addition to the hold-out evaluation, we performed stratified 10-fold cross-validation to reduce selection bias.  \nResults: The random forest model demonstrated the optimal performance, with an area under the receiver operating characteristic curve of 0.86 in the training set and 0 .70 in the test set. It significantly outperformed the other models (P \u003C 0. 001) . The Shapley additive explanation method was used to interpret the model, revealing that original_ngtdm_Coarseness, original_ngtdm_Strength, and wavelet. LL_glcm_ClusterProminence were the top predictors. These  \nFrontiers in Oncology 01 [frontiersin.org](frontiersin.org)  \nfeatures reflect structural compactness and heterogeneity associated with EGFR overexpression.  \nConclusions: We present a reliable and interpretable tool for non-invasively assessing EGFR expression status in patients with breast cancer. The most important predictors captured tumor heterogeneity and microstructural uniformity, highlighting the biological relevance of radiomic patterns in EGFRpositive tumors. This model integrates advanced imaging analyses","cbCaieRzucCsh7MK","https://ap.wps.com/l/cbCaieRzucCsh7MK","pdf",7257345,1,12,"English","en",105,"# Background and Objectives\n## Methods\n## Results\n## Conclusions\n# Introduction\n# Key Predictors and Model Interpretation","[{\"question\":\"Why is predicting EGFR expression status in breast cancer clinically important?\",\"answer\":\"EGFR expression influences overall and disease-free survival, and EGFR overexpression is linked to accelerated metastasis and recurrence.\"},{\"question\":\"How were radiomic features and predictors selected in this study?\",\"answer\":\"Radiomic features were extracted from grayscale and wavelet-transformed ultrasound images, then filtered by reproducibility (ICC), univariate tests (F-test), and LASSO regression, followed by training seven machine-learning models.\"},{\"question\":\"Which model performed best and what were the main predictive features?\",\"answer\":\"The random forest model achieved the best performance, and SHAP interpretation highlighted original_ngtdm_Coarseness, original_ngtdm_Strength, and wavelet.LL_glcm_ClusterProminence as top predictors.\"}]","Machine learning model for predicting epidermal growth factor receptor expression status in breast cancer using ultrasound radiomics | PDF",1785898700,30,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-model-for-predicting-epidermal-growth-factor-receptor-expression-status-in-breast-cancer-using-ultrasound-radiomics","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-model-for-predicting-epidermal-growth-factor-receptor-expression-status-in-breast-cancer-using-ultrasound-radiomics/125404/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is predicting EGFR expression status in breast cancer clinically important?","Question",{"text":75,"@type":76},"EGFR expression influences overall and disease-free survival, and EGFR overexpression is linked to accelerated metastasis and recurrence.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How were radiomic features and predictors selected in this study?",{"text":80,"@type":76},"Radiomic features were extracted from grayscale and wavelet-transformed ultrasound images, then filtered by reproducibility (ICC), univariate tests (F-test), and LASSO regression, followed by training seven machine-learning models.",{"name":82,"@type":73,"acceptedAnswer":83},"Which model performed best and what were the main predictive features?",{"text":84,"@type":76},"The random forest model achieved the best performance, and SHAP interpretation highlighted original_ngtdm_Coarseness, original_ngtdm_Strength, and wavelet.LL_glcm_ClusterProminence as top predictors.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,118,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":116,"slug":117},40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":29,"slug":121},8,"Research & Report","research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]