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A deep learning model is built to predict EGFR mutation status using only H&E-stained whole-slide images, avoiding molecular assays. Using 326 H&E slides with ResNet-50, the model achieved 76% sensitivity and 74% specificity (AUC 0.82) and, with a double-threshold strategy, produced definitive predictions for a subset while recommending further testing for uncertain cases.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/high-accuracy-epidermal-growth-factor-receptor-mutation-prediction-via-histopathological-deep-learning/379015/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/high-accuracy-epidermal-growth-factor-receptor-mutation-prediction-via-histopathological-deep-learning/379015.png","ImageObject",300,407,{"name":92,"@type":93},"Jacob","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-04","2026-09-24",true,{"@type":102,"interactionType":103,"userInteractionCount":52},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"Why is EGFR mutation detection important in non-small cell lung cancer?","Question",{"text":112,"@type":113},"EGFR mutation status guides tyrosine kinase inhibitor therapy. Molecular testing typically requires tissue samples that may be difficult to obtain in some patients.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What data and model design were used to predict EGFR mutations?",{"text":117,"@type":113},"The study used 326 H&E-stained non-small cell lung cancer slides, training on 226 slides and testing on 100 images. A ResNet-50 convolutional neural network classified EGFR mutation status at the slide level.",{"name":119,"@type":110,"acceptedAnswer":120},"How accurate was the proposed deep learning model?",{"text":121,"@type":113},"Overall sensitivity and specificity were 76% and 74%, with an AUC of 0.82. With a double-threshold approach, definite EGFR-positive/negative predictions were produced for 33% of patients with 100% sensitivity and 87.5% specificity, while the remainder required further examination.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},379015,1790632664,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":52,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":44,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":144},962084931830,"https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd","Zhao et al. BMC Pulmonary Medicine (2023) 23:244  \n[https://doi.org/10.1186/s12890-023-02537-x](https://doi.org/10.1186/s12890-023-02537-x)  \nBMC Pulmonary Medicine  \n RESEARCH ARTICLE Open Access  \nHigh accuracy epidermal growth factor receptor mutation prediction via histopathological deep learning  \nDan Zhao1, Yanli Zhao1, Sen He2, Zichen Liu1, Kun Li1, Lili Zhang1, Xiaojun Zhang3, Shuhao Wang3* , Nanying Che1* and Mulan Jin4*  \nAbstract  \nBackground The detection of epidermal growth factor receptor (EGFR) mutations in patients with non-small cell lung cancer is critical for tyrosine kinase inhibitor therapy. EGFR detection requires tissue samples, which are difficult to obtain in some patients, costing them the opportunity for further treatment. To realize EGFR mutation prediction without molecular detection, we aimed to build a high-accuracy deep learning model with only haematoxylin and eosin (H&E)-stained slides.  \nMethods We collected 326 H&E-stained non-small cell lung cancer slides from Beijing Chest Hospital, China, and used 226 slides (88 with EGFR mutations) for model training. The remaining 100 images (50 with EGFR mutations) were used for testing. We trained a convolutional neural network based on ResNet-50 to classify EGFR mutation status on the slide level.  \nResults The sensitivity and specificity of the model were 76% and 74%, respectively, with an area under the curve of 0.82. When applying the double-threshold approach, 33% of the patients could be predicted by the deep learning model as EGFR positive or negative with a sensitivity and specificity of 100. 0% and 87. 5% . The remaining 67% of the patients got an uncertain result and will be recommenced to perform further examination. By incorporating adenocarcinoma subtype information, we achieved 100% sensitivity in predicting EGFR mutations in 37. 3% of adenocarcinoma patients.  \nConclusions Our study demonstrates the potential of a deep learning-based EGFR mutation prediction model for rapid and cost-effective pre-screening. It could serve as a high-accuracy complement to current molecular detection methods and provide treatment opportunities for non-small cell lung cancer patients from whom limited samples are available.  \n*Correspondence: Shuhao Wang [to@shuhao.wang](to@shuhao.wang)[ ](to@shuhao.wang)Nanying Che[cheny0448@163.com](cheny0448@163.com)[ ](cheny0448@163.com)Mulan Jin [kinmokuran@163.com](kinmokuran@163.com)  \nFull list of author information is available at the end of the article  \n© The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit [http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ()[. The Creative Commons Public Domain Dedication waiver (](http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ()[http://creativeco](http://creativeco)[mmons.org/publicdomain/zero/1.0/](mmons.org/publicdomain/zero/1.0/)) applies to the data made available in this article, unless otherwise stated in a credit line to the data.  \nZhao et al. BMC Pulmonary Medicine (2023) 23:244  \nBackground  \nAccording to GLOBOCAN’s Global Cancer Statistics in 2018, lung cancer is t","cbCaif9NR2rdAOiO","https://ap.wps.com/l/cbCaif9NR2rdAOiO","pdf",5094231,"English","# Background\n# Methods\n# Results\n# Conclusions\n# Introduction","[{\"question\":\"Why is EGFR mutation detection important in non-small cell lung cancer?\",\"answer\":\"EGFR mutation status guides tyrosine kinase inhibitor therapy. Molecular testing typically requires tissue samples that may be difficult to obtain in some patients.\"},{\"question\":\"What data and model design were used to predict EGFR mutations?\",\"answer\":\"The study used 326 H\\u0026E-stained non-small cell lung cancer slides, training on 226 slides and testing on 100 images. A ResNet-50 convolutional neural network classified EGFR mutation status at the slide level.\"},{\"question\":\"How accurate was the proposed deep learning model?\",\"answer\":\"Overall sensitivity and specificity were 76% and 74%, with an AUC of 0.82. With a double-threshold approach, definite EGFR-positive/negative predictions were produced for 33% of patients with 100% sensitivity and 87.5% specificity, while the remainder required further examination.\"}]","High accuracy epidermal growth factor receptor mutation prediction via histopathological deep learning | PDF",1790233691,23]