Interactive 3D Segmentation for Primary Gross Tumor Volume in Oropharyngeal Cancer

Interactive 3D Segmentation for Primary Gross Tumor Volume in Oropharyngeal Cancer | 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 Article Interactive 3D Segmentation for Primary Gross Tumor Volume in Oropharyngeal Cancer Mikko Saukkoriipi, Jaakko Sahlsten, Joel Jaskari, Lotta Orsmaa, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6221538/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 05 Aug, 2025 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract Radiotherapy is the main treatment modality of oropharyngeal cancer (OPC), in which an accurate segmentation of primary gross tumor volume (GTVt) is essential but also challenging due to significant interobserver variability and the time consumed in manual tumor delineation. For such a challenge an interactive deep learning (DL) based approach offers the advantage of automatic high-performance segmentation with the flexibility for user correction when necessary. In this study, we investigate an interactive DL for GTVt segmentation in OPC by introducing a novel two-stage Interactive Click Refinement (2S-ICR) framework and implementing state-of-the-art algorithms. Using the 2021 HEad and neCK TumOR dataset for development and an external dataset from The University of Texas MD Anderson Cancer Center for evaluation, the 2S-ICR framework achieves a Dice similarity coefficient of 0.722 ± 0.142 without user interaction and 0.858 ± 0.050 after ten interactions, thus outperforming existing methods in both cases. Biological sciences/Cancer Biological sciences/Computational biology and bioinformatics/Image processing Full Text Additional Declarations No competing interests reported. Supplementary Files supplementarymaterial.pdf Cite Share Download PDF Status: Published Journal Publication published 05 Aug, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 24 Jun, 2025 Reviews received at journal 15 Jun, 2025 Reviewers agreed at journal 29 May, 2025 Reviewers agreed at journal 26 May, 2025 Reviews received at journal 23 Apr, 2025 Reviewers agreed at journal 19 Apr, 2025 Reviewers agreed at journal 18 Apr, 2025 Reviewers invited by journal 17 Apr, 2025 Editor invited by journal 20 Mar, 2025 Editor assigned by journal 18 Mar, 2025 Submission checks completed at journal 14 Mar, 2025 First submitted to journal 13 Mar, 2025 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-6221538","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":447117974,"identity":"72c41d55-3714-49b6-8a44-c479f304ad82","order_by":0,"name":"Mikko Saukkoriipi","email":"","orcid":"","institution":"Aalto University","correspondingAuthor":false,"prefix":"","firstName":"Mikko","middleName":"","lastName":"Saukkoriipi","suffix":""},{"id":447117975,"identity":"7a9711fa-04b8-4622-84ab-f8f1f676af06","order_by":1,"name":"Jaakko Sahlsten","email":"","orcid":"","institution":"Aalto 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