Modeling Organ Dose from Industrial Radiography Sources: Parameter Sensitivity and Predictive formulation

Modeling Organ Dose from Industrial Radiography Sources: Parameter Sensitivity and Predictive formulation | 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 Modeling Organ Dose from Industrial Radiography Sources: Parameter Sensitivity and Predictive formulation Keyhandokht Karimi-Shahri, Mohammad Darrudi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7127611/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 Industrial radiography accidents, often involving high-activity sources such as 192 Ir, 137 Cs, and 60 Co, are among the most common radiation incidents globally. The absence of comprehensive dosimetric datasets and rapid predictive models for dose estimation in emergencies represents a critical gap. This study addresses this by systematically analyzing the impact of source-to-body distance, source height, irradiation geometry, and photon energy on organ- absorbed dose. Monte Carlo simulations, performed using MCNP6 with the ICRP reference voxel phantom, modeled doses for organs across anterior-posterior, posterior-anterior, and lateral irradiation geometries, with source distances ranging from 0.5 to 300 cm and heights spanning ground to upper torso levels. Results were validated against ICRP Publication 145. Three machine learning models—Random Forest (RF), XGBoost, and Lasso regression—were developed in Python (version 3.9.6). RF and XGBoost achieved high predictive accuracy (R² = 0.63–0.77, MSE = 0.007–0.010), with source-to-body distance identified as the most influential factor and irradiation geometry the least. Lasso regression provided a simplified predictive formula (R² = 0.56) for sensitive organs in rapid dose estimation in time-critical scenarios. These models offer a robust framework for precise clinical dose assessment and efficient safety evaluations in radiation emergencies. Health sciences/Health care Health sciences/Medical research Industrial radiography source predictive model of dose Organ- absorbed dose distribution Monte Carlo simulations Machine learning algorithm 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-7127611","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":509192438,"identity":"fca95086-12de-4bb6-8835-3e4760200939","order_by":0,"name":"Keyhandokht Karimi-Shahri","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIiWNgGAWjYBACCQY2IGnAwMMvwdgA5DEASSBIIEaL5AzStIB03YDQEC34gGQDW+LnioJtMsa3m9s+WNTckW1gP/yA4eEe3FqkGdgOS54xuM1jdudg8wyJY8+MG3jSDBgSnuHWIsfA3iDZANJyI7GZQYLtcGIDQw7QLwfwamn+CdJiPAOk5R9QC/8b/FqADjsGtsVAAqhFsg2oRYKALZLNbGmWIC0SQL8wSPYdNm6TeGZwAJ8WieNtxjcb/ty255/d/phZ4tth2X7+5IcPf+DRwsCMzAZGJTie8GlABYwfiFY6CkbBKBgFIwkAAI9ITxXNnuaHAAAAAElFTkSuQmCC","orcid":"","institution":"University of Birjand","correspondingAuthor":true,"prefix":"","firstName":"Keyhandokht","middleName":"","lastName":"Karimi-Shahri","suffix":""},{"id":509192439,"identity":"affee371-9c4d-41bd-8f97-eeefbcf5f349","order_by":1,"name":"Mohammad Darrudi","email":"","orcid":"","institution":"University of Birjand","correspondingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"","lastName":"Darrudi","suffix":""}],"badges":[],"createdAt":"2025-07-15 07:38:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7127611/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7127611/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":93742171,"identity":"c4b7ed03-f68b-4d29-a0f7-679d5d258be4","added_by":"auto","created_at":"2025-10-17 05:31:52","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1389425,"visible":true,"origin":"","legend":"","description":"","filename":"Scientificreport.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7127611/v1_covered_aa932f69-0d78-4913-a8c1-b8db2d801f36.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Modeling Organ Dose from Industrial Radiography Sources: Parameter Sensitivity and Predictive formulation","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":"Industrial radiography source, predictive model of dose, Organ- absorbed dose distribution, Monte Carlo simulations, Machine learning algorithm","lastPublishedDoi":"10.21203/rs.3.rs-7127611/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7127611/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIndustrial radiography accidents, often involving high-activity sources such as \u003csup\u003e192\u003c/sup\u003eIr, \u003csup\u003e137\u003c/sup\u003eCs, and \u003csup\u003e60\u003c/sup\u003eCo, are among the most common radiation incidents globally. The absence of comprehensive dosimetric datasets and rapid predictive models for dose estimation in emergencies represents a critical gap. This study addresses this by systematically analyzing the impact of source-to-body distance, source height, irradiation geometry, and photon energy on organ- absorbed dose. Monte Carlo simulations, performed using MCNP6 with the ICRP reference voxel phantom, modeled doses for organs across anterior-posterior, posterior-anterior, and lateral irradiation geometries, with source distances ranging from 0.5 to 300 cm and heights spanning ground to upper torso levels. Results were validated against ICRP Publication 145. Three machine learning models\u0026mdash;Random Forest (RF), XGBoost, and Lasso regression\u0026mdash;were developed in Python (version 3.9.6). RF and XGBoost achieved high predictive accuracy (R\u0026sup2; = 0.63\u0026ndash;0.77, MSE\u0026thinsp;=\u0026thinsp;0.007\u0026ndash;0.010), with source-to-body distance identified as the most influential factor and irradiation geometry the least. Lasso regression provided a simplified predictive formula (R\u0026sup2; = 0.56) for sensitive organs in rapid dose estimation in time-critical scenarios. 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