LOCD: a novel LOWESS based computational methodology for early detection of ovarian cancer with biomarker screening

LOCD: a novel LOWESS based computational methodology for early detection of ovarian cancer with biomarker screening | 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 LOCD: a novel LOWESS based computational methodology for early detection of ovarian cancer with biomarker screening Zonglun Li, Ian Jacobs, Usha Menon, Aleksandra Gentry-Maharaj, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6339487/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 Ovarian cancer is the fifth most common cause of cancer-related deaths among women and the survival rate for ovarian cancer varies significantly based on the stage at which the cancer is diagnosed. Therefore, early detection of ovarian cancer is crucial and can substantially improve the outcome in these women. Screening using protein biomarkers for the average-risk population has been trialed in the UK, but their effectiveness remains a major challenge. Nowadays, computational methods play an increasingly important role in early detection given their low cost, efficiency and ability to complement expert judgment. However, the screening data for ovarian cancer have several characteristics that make computational detection challenging. To this end, we developed a new methodology LOCD (LOWESS-based Ovarian Cancer Detection) that enables efficient and robust prediction of the outcome based on the screening history. We demonstrate the benefit of incorporating the longitudinal trajectory as well as the superiority of our method over the state-of-the-art using the gold-standard biomarker cancer antigen 125 (CA125) from a well-studied UKCTOCS dataset. Particularly, we adopted a repeated half-half-splitting strategy to conduct the internal validation to enhance the pertinence of evaluation. The performance was comprehensively assessed with the ROC AUC and the sensitivity scores and reported in the form of the median and its confidence interval. Furthermore, the extension to the multi-marker panel (CA125, HE4 and Glycodelin) is also laid out and compared with the CA125-algorithm alone. Health sciences/Biomarkers/Diagnostic markers Biological sciences/Computational biology and bioinformatics/Machine learning ovarian cancer screening machine learning early detection LOWESS Full Text Additional Declarations Competing interest reported. UM and AGM report personal consulting fees from Mercy BioAnalytics Ltd. and research support grants paid to the institution from Intelligent Lab on Fiber, RNA Guardian and MercyBio Analytics for early detection of cancer, especially ovarian can- cer. UM declares honorarium for advisory board membership of Tina’s Wish Scientific Advisory Board (USA). IJ has a right to any royalties accruing to the Risk of Ovarian Cancer Algorithm. All other authors report no conflict. 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6339487","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":488992648,"identity":"6c60895b-76c0-4879-9119-37027a1797a5","order_by":0,"name":"Zonglun Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA90lEQVRIiWNgGAWjYLCCB0DMx8zA+CChAsiAiCXg1wKSZmNmYDb4cAbIIF4LEEnObCNCi3x77+EXiW13GNjYGRikeecdlmdjYH74gbEtDacWxp5zaRaJbc9ADmMw5t122LCNgc1YgrEtB6cWZokcM4PEtsNgLclALQlAh5kxMLZV4NTChqzlMO8ckBb2b3i18EjkGD+AamFsnNkA0sIDsgW3wyR4zpgxJJw7zMPGzNjM8OFYumEbM0+xRMI53N6Xb+8x/vCh7LAcP//h4z8Saqzl+dnbNwJFknFqAXsH5EBg4DVAQ4SBYEQyf8AvPwpGwSgYBSMeAADHM0RUiXVcaQAAAABJRU5ErkJggg==","orcid":"","institution":"University College London","correspondingAuthor":true,"prefix":"","firstName":"Zonglun","middleName":"","lastName":"Li","suffix":""},{"id":488992650,"identity":"c209300c-43c2-4cb7-857e-4324ab77dff5","order_by":1,"name":"Ian Jacobs","email":"","orcid":"","institution":"University College London","correspondingAuthor":false,"prefix":"","firstName":"Ian","middleName":"","lastName":"Jacobs","suffix":""},{"id":488992651,"identity":"c2022f03-189f-4486-bef9-4256ee421097","order_by":2,"name":"Usha Menon","email":"","orcid":"","institution":"University College London","correspondingAuthor":false,"prefix":"","firstName":"Usha","middleName":"","lastName":"Menon","suffix":""},{"id":488992652,"identity":"88aaa3a2-454c-432d-99ab-97acc85f5fa3","order_by":3,"name":"Aleksandra Gentry-Maharaj","email":"","orcid":"","institution":"University College London","correspondingAuthor":false,"prefix":"","firstName":"Aleksandra","middleName":"","lastName":"Gentry-Maharaj","suffix":""},{"id":488992653,"identity":"087a9efe-8ca1-4e07-a284-43092146adb9","order_by":4,"name":"Denis Zakharov","email":"","orcid":"","institution":"National Research University Higher School of Economics","correspondingAuthor":false,"prefix":"","firstName":"Denis","middleName":"","lastName":"Zakharov","suffix":""},{"id":488992654,"identity":"80e5dceb-f330-40c3-b2f1-6596e4c42642","order_by":5,"name":"Alexey Zaikin","email":"","orcid":"","institution":"University College London","correspondingAuthor":false,"prefix":"","firstName":"Alexey","middleName":"","lastName":"Zaikin","suffix":""},{"id":488992659,"identity":"8d22650c-ae67-48b1-96fb-1bf751cf3d01","order_by":6,"name":"Oleg Blyuss","email":"","orcid":"","institution":"Queen Mary University of London","correspondingAuthor":false,"prefix":"","firstName":"Oleg","middleName":"","lastName":"Blyuss","suffix":""}],"badges":[],"createdAt":"2025-03-30 16:23:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6339487/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6339487/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90186437,"identity":"2f882f3d-5ffa-4872-9020-7ad7b92d767e","added_by":"auto","created_at":"2025-08-29 14:47:17","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3406448,"visible":true,"origin":"","legend":"","description":"","filename":"SR.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6339487/v1_covered_d2443f3a-acee-4c37-900f-28726ccabc4b.pdf"}],"financialInterests":"Competing interest reported. 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