Therapy Response Prediction in Patients with Metastatic Soft Tissue Sarcomas Using CT based Delta Radiomics

Therapy Response Prediction in Patients with Metastatic Soft Tissue Sarcomas Using CT based Delta Radiomics | 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 Therapy Response Prediction in Patients with Metastatic Soft Tissue Sarcomas Using CT based Delta Radiomics Frederik Hartmann, Christianne Jansma, Manon Verburg, David Hanff, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8259738/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 This retrospective study investigates whether computed tomography (CT)-based delta-radiomics can improve systemic treatment response prediction in patients with metastatic STS. Data from 71 patients with initially unresectable, high-grade, metastasized STS treated at Erasmus Medical Center between 2014 and 2020 were included. Radiomics features were extracted from up to five metastases, and delta-radiomics were computed as the relative difference in features between pre-treatment and follow-up scans. Therapy response was modeled using survival analysis, utilizing the time interval from metastasis diagnosis to death or latest follow-up. To predict response, we employed automated machine learning differentiating three input configurations: 1) The imaging model, based on 107 quantitative features; 2) a diameter-only model; and 3) a volume-only model. Models were evaluated using a repeated nested 5-fold cross-validation. The imaging model achieved a mean c-index of 0.68 (95% CI: 0.60–0.76) and a one-year cumulative dynamic area under the curve (cAUC) of 0.75 (95% CI: 0.55–0.95). Diameter- and volume-only models performed worse, with c-indices of 0.61 (95% CI: 0.51–0.70) and 0.65 (95% CI: 0.53–0.76), and cAUCs of 0.60 (95% CI: 0.35–0.85) and 0.63 (95% CI: 0.38–0.88), respectively. These findings suggest that CT-based delta-radiomics is valuable for predicting therapy response in metastatic STS, warranting potential optimization and validation in larger, multi-center studies before clinical adoption. radiomics machine learning soft tissue sarcomas metastasis therapy response prediction Full Text Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterialforTherapyResponsePredictioninPatientswithMetastaticSoftTissueSarcomasUsingCTbasedDeltaRadiomics.pdf 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-8259738","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":558330211,"identity":"50ee824b-fdb0-4c9d-958a-bb0ba74864c1","order_by":0,"name":"Frederik Hartmann","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIiWNgGAWjYBAC9gYeGJP5ALKEBU4tPAdAWhJATLYEZAkJYrTwGBCphYH34OfKH9vkzaV7Pj6uqKmLNjh/OvED4w58WviSJc8k3DbcOefsZsMzxw7nbriRu1mC8QxuLfZA90g2JNxmBKrcJtnAdgCohXcbA2MbPlt4jH8CtdhvuJHz/GfDv7rcDefPEtRiBrIlEaiFjbGxjTl3w4FcAlqYecwsG9JuJ2+4kWYs2dh3OHcmyC+J+LSw9xjfbLC5bbvhRvLDjw3f6nL7zp/d+OFjmw1OLQzMWEUTcGsYBaNgFIyCUUAEAAC//VfL6e1AhwAAAABJRU5ErkJggg==","orcid":"","institution":"University Medical Center Rotterdam","correspondingAuthor":true,"prefix":"","firstName":"Frederik","middleName":"","lastName":"Hartmann","suffix":""},{"id":558330213,"identity":"41f21d08-8d2c-4e92-afa5-f9d46e7e9e10","order_by":1,"name":"Christianne Jansma","email":"","orcid":"","institution":"University Medical Center Rotterdam","correspondingAuthor":false,"prefix":"","firstName":"Christianne","middleName":"","lastName":"Jansma","suffix":""},{"id":558330214,"identity":"51d58830-be25-4602-b976-b2364b73ae33","order_by":2,"name":"Manon Verburg","email":"","orcid":"","institution":"University Medical Center Rotterdam","correspondingAuthor":false,"prefix":"","firstName":"Manon","middleName":"","lastName":"Verburg","suffix":""},{"id":558330217,"identity":"40952120-86fd-470c-86c7-6602cb5bd445","order_by":3,"name":"David Hanff","email":"","orcid":"","institution":"University Medical Center Rotterdam","correspondingAuthor":false,"prefix":"","firstName":"David","middleName":"","lastName":"Hanff","suffix":""},{"id":558330218,"identity":"ad0d5947-3aa0-46ca-885d-730e881e047c","order_by":4,"name":"Wiro J. 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