Assessing the reproducibility of a subject-specific finite element modelling pipeline for the human metastatic vertebrae

Assessing the reproducibility of a subject-specific finite element modelling pipeline for the human metastatic vertebrae | 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 Assessing the reproducibility of a subject-specific finite element modelling pipeline for the human metastatic vertebrae Robert Roger, Rajdeep Ghosh, Yuanrui Cai, Fiona Gibson, Áron Lazáry, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8660042/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Apr, 2026 Read the published version in Scientific Reports → Version 1 posted 13 You are reading this latest preprint version Abstract Computed tomography (CT)-based subject-specific finite element (SS-FE) models offer a quantitative approach to assessing the stability of metastatic vertebrae. A critical step in developing SS-FE models is image segmentation, however automatic techniques fail to accurately capture the altered contours of metastatic vertebrae, making manual image segmentation the preferred approach. This introduces operator-dependent steps into the CT-to-FE modelling pipeline that may influence the model’s mechanical properties. To assess these dependencies, this study evaluated intra- and inter-operator reproducibility of a CT-to-FE pipeline applied to six lumbar vertebrae, three radiologically healthy and three lytic metastases. All models were developed using a standardised pipeline incorporating densitometric calibration, 1mm quadratic tetrahedral meshing, and heterogeneous isotropic elasto-plastic material properties, with vertebrae loaded in uniaxial compression to 1.9% apparent strain. Geometrical/volumetrical metrics from segmented images and mechanical metrics from the SS-FE models were assessed. Intra-operator reproducibility was high across geometric and mechanical metrics, whereas inter-operator reproducibility showed modest variability, particularly in local mechanical metrics of metastatic vertebrae. Consistent volumetric inconsistencies between segmentations significantly correlated with variation in predicted failure load of SS-FE models. Overall reproducibility was high, demonstrating the reliability of the CT-based SS-FE modelling pipeline for estimating vertebral strength. Physical sciences/Engineering Health sciences/Medical research Computed tomography metastatic vertebrae image segmentation subject-specific finite element modelling reproducibility Full Text Additional Declarations No competing interests reported. Supplementary Files RogerSciRepSupMaterialsV1.pdf Cite Share Download PDF Status: Published Journal Publication published 07 Apr, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 06 Mar, 2026 Reviews received at journal 05 Mar, 2026 Reviews received at journal 26 Feb, 2026 Reviewers agreed at journal 21 Feb, 2026 Reviewers agreed at journal 20 Feb, 2026 Reviewers agreed at journal 19 Feb, 2026 Reviewers agreed at journal 19 Feb, 2026 Reviewers agreed at journal 19 Feb, 2026 Reviewers invited by journal 19 Feb, 2026 Editor assigned by journal 19 Feb, 2026 Editor invited by journal 13 Feb, 2026 Submission checks completed at journal 12 Feb, 2026 First submitted to journal 12 Feb, 2026 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-8660042","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":594928402,"identity":"4dc530ed-9e33-4f80-9b03-136cf8324750","order_by":0,"name":"Robert Roger","email":"","orcid":"","institution":"University of Sheffield","correspondingAuthor":false,"prefix":"","firstName":"Robert","middleName":"","lastName":"Roger","suffix":""},{"id":594928403,"identity":"9f173d3b-0337-46d6-b489-045b0f0051d4","order_by":1,"name":"Rajdeep Ghosh","email":"","orcid":"","institution":"University of 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