Effective Estrogen Receptor Status Classification in BreastCancer Using a Single TMA Image and HGCN-CNN Ensemble

Effective Estrogen Receptor Status Classification in BreastCancer Using a Single TMA Image and HGCN-CNN Ensemble | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Effective Estrogen Receptor Status Classification in BreastCancer Using a Single TMA Image and HGCN-CNN Ensemble Atefeh Azin Kousha, Jingxin Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5869654/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Introduction: This study proposes a Computer-Aided Diagnosis (CAD) method to determine Estrogen Receptor Status (ERS) in breast cancer patients using Hematoxylin and Eosin (H&E)-stained tissue microarray (TMA) images. It is the first to explore the contributions of graph-based analysis for this task and their integration with conventional Convolutional Neural Network (CNN)-based approaches. Methods: A graph model of H&E-stained images is proposed, representing intercellular spatial dependencies as graph edges. We explore the significance of intercellular spatial relationship properties derived from cellular morphology and graph topology, which have been unexplored in previous studies. Additionally, this work combines the results of the proposed graph classifier with those of well-established Convolutional Neural Network(CNN) classifiers to optimize accuracy. Results: The proposed Hybrid Graph Convolutional Network (HGCN) achieves 14%,25%, and 39% higher specificity compared to the popular pre-trained CNN classifiers VGG16, DenseNet121, and ResNet50, respectively. Whereas, HGCN demonstrates lower sensitivity than these CNN classifiers. By combining the strengths of both approaches in a novel HGCN-CNN ensemble, a well-balanced trade-off between sensitivity and specificity is achieved, resulting in the highest Area Under the Receiver Operating CharacteristicCurve (AUC-ROC) of 87% among all the classifiers. The ensemble outperforms individual CNN classifiers in accuracy, AUC-ROC, and specificity while also exceeding the HGCN classifier in accuracy, AUC-ROC, and sensitivity. These results are obtained from a training dataset of 1,000 H&E-stained TMA images and a test dataset of 554 TMA images. Conclusions: The results demonstrate that the graph-based approach offers significantly higher specificity compared to popular pre-trained CNN classifiers, while the CNN classifiers excel in sensitivity. The proposed HGCN-CNN ensemble effectively leverages the complementary strengths of both classifier types, enhancing overall binary classification performance in terms of AUC-ROC and accuracy compared to all individual classifiers. Breast Cancer Hormonal Status Estrogen Receptor Status Convolutional Neural Networks Graph Convolutional Networks Computer Aided Diagnosis Histopathology slides classification Machine learning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5869654","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":406236839,"identity":"382b7088-9939-45e1-abf6-abc432b345f8","order_by":0,"name":"Atefeh Azin Kousha","email":"","orcid":"","institution":"Swinburne University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Atefeh","middleName":"Azin","lastName":"Kousha","suffix":""},{"id":406236840,"identity":"019fa1da-13e6-4e12-87d4-1ac1341d7d98","order_by":1,"name":"Jingxin Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBklEQVRIiWNgGAWjYBACxmYQaQBGDAwfGBh4QLQE0VoYZxCjBQ5AWph5oBy8WpjbeQ+/5im4Y2/Ofvbwa5s/h2XMGZgP3uZhsEtswOkwvjTLGQbPmC178tKsc9sO81g2sCVb8zAk49HCY2bwweAwm8GBHDPj3IbbPAYHeMykeRiY8WtJMDjMY3D+jZmxxR+QFv5vQC31+LQYPwDaImFwI8f4MQMb2BY2oJbDeG1hnGFw2MDgxhszxt62/zxARxpbzjE4boxLi2H/GePPPH8O2xuczzH+8ONPmr3B8eaHN95UVMvi1NLAwAaLBSiDGUQY4FAPBPJAJR+gbDhjFIyCUTAKRgEKAAC6n1LHTwOr1wAAAABJRU5ErkJggg==","orcid":"","institution":"Swinburne University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Jingxin","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2025-01-21 03:08:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5869654/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5869654/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84490023,"identity":"5f7354e4-2447-4b37-b70e-c4d93e5109a4","added_by":"auto","created_at":"2025-06-12 14:23:58","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4361915,"visible":true,"origin":"","legend":"","description":"","filename":"paperAzin.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5869654/v1_covered_b2340c3b-b7dd-47e1-bfe1-57d330369bcb.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Effective Estrogen Receptor Status Classification in BreastCancer Using a Single TMA Image and HGCN-CNN Ensemble","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Breast Cancer Hormonal Status, Estrogen Receptor Status, Convolutional Neural Networks, Graph Convolutional Networks, Computer Aided Diagnosis, Histopathology slides classification, Machine learning","lastPublishedDoi":"10.21203/rs.3.rs-5869654/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5869654/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIntroduction: This study proposes a Computer-Aided Diagnosis (CAD) method to determine Estrogen Receptor Status (ERS) in breast cancer patients using Hematoxylin and Eosin (H\u0026amp;E)-stained tissue microarray (TMA) images. It is the first to explore the contributions of graph-based analysis for this task and their integration with conventional Convolutional Neural Network (CNN)-based approaches.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMethods: A graph model of H\u0026amp;E-stained images is proposed, representing intercellular spatial dependencies as graph edges. We explore the significance of intercellular spatial relationship properties derived from cellular morphology and graph topology, which have been unexplored in previous studies. Additionally, this work combines the results of the proposed graph classifier with those of well-established Convolutional Neural Network(CNN) classifiers to optimize accuracy.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResults: The proposed Hybrid Graph Convolutional Network (HGCN) achieves 14%,25%, and 39% higher specificity compared to the popular pre-trained CNN classifiers VGG16, DenseNet121, and ResNet50, respectively. Whereas, HGCN demonstrates lower sensitivity than these CNN classifiers. By combining the strengths of both approaches in a novel HGCN-CNN ensemble, a well-balanced trade-off between sensitivity and specificity is achieved, resulting in the highest Area Under the Receiver Operating CharacteristicCurve (AUC-ROC) of 87% among all the classifiers. The ensemble outperforms individual CNN classifiers in accuracy, AUC-ROC, and specificity while also exceeding the HGCN classifier in accuracy, AUC-ROC, and sensitivity. These results are obtained from a training dataset of 1,000 H\u0026amp;E-stained TMA images and a test dataset of 554 TMA images.\u003c/p\u003e\n\u003cp\u003eConclusions: The results demonstrate that the graph-based approach offers significantly higher specificity compared to popular pre-trained CNN classifiers, while the CNN classifiers excel in sensitivity. The proposed HGCN-CNN ensemble effectively leverages the complementary strengths of both classifier types, enhancing overall binary classification performance in terms of AUC-ROC and accuracy compared to all individual classifiers.\u003c/p\u003e","manuscriptTitle":"Effective Estrogen Receptor Status Classification in BreastCancer Using a Single TMA Image and HGCN-CNN Ensemble","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-28 13:12:06","doi":"10.21203/rs.3.rs-5869654/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"eab37a71-dd50-413e-8674-81b7fac71dc3","owner":[],"postedDate":"January 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-12T14:23:22+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-28 13:12:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5869654","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5869654","identity":"rs-5869654","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}