Prognosis of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Using DCE-MRI and InceptionV3

Prognosis of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Using DCE-MRI and InceptionV3 | 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 Short Report Prognosis of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Using DCE-MRI and InceptionV3 Satyabrata Pattanayak, Tripty Singh, Rishabh Kumar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4938587/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 Background: Neoadjuvant therapy is crucial for breast cancer patients seeking to retain the breast by reducing the tumor size for conserving surgery. This treatment aims to achieve a pathologic complete response (pCR), eradicating cancer cells entirely and reducing the risk of recurrence. The study’s objective is to devise a predictive approach for identifying patients achieving pCR through neoadjuvant therapy, based on radiomic features extracted from MR images by leveraging the InceptionV3 model with advanced validation techniques. Methods: In our study, we gathered data from 364 unique Patient IDs sourced from the-SPY 2 MRI database with the goal of classifying pCR (pathological complete response). Our research introduced three key areas of novelty.Firstly, we explored the extraction of advanced features such as region centroid, Entropy, Sphericity, and more. These features provided deeper insights into the characteristics of the MRI data and enhanced the discriminative power of our classification model.Secondly, we applied these extracted features to the InceptionV3 (GoogleNet) model. To optimize the model’s performance, we experimented with different combinations of loss functions, optimizer functions, and activation functions. This thorough exploration allowed us to identify the most effective configuration for the given task.Lastly, our classification results were subjected 1 to validation using advanced techniques such as Matthews Correlation Coefficient, Cohen’s Kappa, and Jaccard Index. These evaluation metrics provided a robust assessment of the model’s performance and ensured the reliability of our findings. Results: The successful combination of advanced feature extraction, utilization of the InceptionV3 model with tailored hyperparameters, and thorough validation using cutting-edge techniques significantly enhanced the accuracy and reliability of our pCR classification study. By adopting a collabo-rative approach that involved both radiologists and the computer-aided system, we achieved superior predictive performance for pCR, as evi-denced by the impressive values obtained for the area under the curve (AUC) at 0.98. Conclusion: Overall, the combination of advanced feature extraction, lever-aging the InceptionV3 model with customized hyperparameters, and rigorous validation using state-of-the-art techniques contributed to the accuracy and credibility of our pCR classification study. Breast Cancer pCR Deep Learning Inception V3 Image Analytics MCC 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. 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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-4938587","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Short Report","associatedPublications":[],"authors":[{"id":345821939,"identity":"0be7e93c-026c-4117-82a1-ad0edd102ca4","order_by":0,"name":"Satyabrata Pattanayak","email":"","orcid":"","institution":"Amrita School of Computing Bengaluru, Amrita Vishwavidyapeetham","correspondingAuthor":false,"prefix":"","firstName":"Satyabrata","middleName":"","lastName":"Pattanayak","suffix":""},{"id":345821940,"identity":"3ec09ea2-2ea8-4423-a811-ca2f95a9017d","order_by":1,"name":"Tripty Singh","email":"data:image/png;base64,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","orcid":"","institution":"Amrita School of Computing Bengaluru, Amrita Vishwavidyapeetham","correspondingAuthor":true,"prefix":"","firstName":"Tripty","middleName":"","lastName":"Singh","suffix":""},{"id":345821941,"identity":"408229c2-7419-41cd-ba1b-54e1346881f8","order_by":2,"name":"Rishabh Kumar","email":"","orcid":"","institution":"Amrita Hospital","correspondingAuthor":false,"prefix":"","firstName":"Rishabh","middleName":"","lastName":"Kumar","suffix":""}],"badges":[],"createdAt":"2024-08-19 12:17:37","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4938587/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4938587/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":74701225,"identity":"c5f02301-dc2d-426a-8085-8bb94f79e69b","added_by":"auto","created_at":"2025-01-25 00:01:24","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":856848,"visible":true,"origin":"","legend":"","description":"","filename":"PrognosisofPathologicalCompleteResponsetoNeoadjuvantChemotherapyinBreastCancerUsingDCEMRIandInceptionV3Version21.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4938587/v1_covered_10d041c5-619f-4f6d-897e-e1fd7da7b7bd.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prognosis of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Using DCE-MRI and InceptionV3","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, pCR, Deep Learning, Inception V3, Image Analytics, MCC","lastPublishedDoi":"10.21203/rs.3.rs-4938587/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4938587/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground: Neoadjuvant therapy is crucial for breast cancer patients seeking to retain the breast by reducing the tumor size for conserving surgery. This treatment aims to achieve a pathologic complete response (pCR), eradicating cancer cells entirely and reducing the risk of recurrence. The study’s objective is to devise a predictive approach for identifying patients achieving pCR through neoadjuvant therapy, based on radiomic features extracted from MR images by leveraging the InceptionV3 model with advanced validation techniques.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMethods: In our study, we gathered data from 364 unique Patient IDs sourced from the-SPY 2 MRI database with the goal of classifying pCR (pathological complete response). Our research introduced three key areas of novelty.Firstly, we explored the extraction of advanced features such as region centroid, Entropy, Sphericity, and more. These features provided deeper insights into the characteristics of the MRI data and enhanced the discriminative power of our classification model.Secondly, we applied these extracted features to the InceptionV3 (GoogleNet) model. To optimize the model’s performance, we experimented with different combinations of loss functions, optimizer functions, and activation functions. This thorough exploration allowed us to identify the most effective configuration for the given task.Lastly, our classification results were subjected 1 to validation using advanced techniques such as Matthews Correlation Coefficient, Cohen’s Kappa, and Jaccard Index. These evaluation metrics provided a robust assessment of the model’s performance and ensured the reliability of our findings.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResults: The successful combination of advanced feature extraction, utilization of the InceptionV3 model with tailored hyperparameters, and thorough validation using cutting-edge techniques significantly enhanced the accuracy and reliability of our pCR classification study. By adopting a collabo-rative approach that involved both radiologists and the computer-aided system, we achieved superior predictive performance for pCR, as evi-denced by the impressive values obtained for the area under the curve (AUC) at 0.98.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConclusion: Overall, the combination of advanced feature extraction, lever-aging the InceptionV3 model with customized hyperparameters, and rigorous validation using state-of-the-art techniques contributed to the accuracy and credibility of our pCR classification study.\u003c/p\u003e","manuscriptTitle":"Prognosis of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Using DCE-MRI and InceptionV3","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-16 18:54:25","doi":"10.21203/rs.3.rs-4938587/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":"93a8b2eb-b473-48ed-a45a-8b4587d51ec9","owner":[],"postedDate":"September 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-24T23:53:17+00:00","versionOfRecord":[],"versionCreatedAt":"2024-09-16 18:54:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4938587","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4938587","identity":"rs-4938587","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}