Ensemble Learning Approach for Prediction of Early Complications after Radiotherapy for Head and Neck Cancer using CT and MRI Radiomic Features

Ensemble Learning Approach for Prediction of Early Complications after Radiotherapy for Head and Neck Cancer using CT and MRI Radiomic Features | 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 Ensemble Learning Approach for Prediction of Early Complications after Radiotherapy for Head and Neck Cancer using CT and MRI Radiomic Features Benyamin Khajetash, Seied Rabi Mahdavi, Alireza Nikoofar, Lee Johnson, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4185554/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 Apr, 2025 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract There are different side effects in radiotherapy of head and neck cancer (HNC) including xerostomia. The present study utilizes the addition of Magnetic Resonance (MR) radiomic image features to typical Computed Tomography (CT)-based features and radiation dose-based characteristics and incorporates the evaluation and validation of individual and ensemble classifiers for prediction of early-onset xerostomia in radiotherapy of HNC. A total of 80 patients diagnosed with HNC were evaluated prospectively. MR and CT imaging, dosimetric, and demographic features of patients were used as model input data. Bilateral parotid radiomic features were extracted from CT, T 1 weighted, and T 2 weighted MR images. Pearson statistical tests were used for selection of features and Random Tree (RT), Neural Network (NN), Linear Support Vector Machine (LSVM) and Bayesian Network (BN) classifiers were evaluated. The results suggest the extracted features from T 1 weighted images have superior prediction ability compared to T 2 weighted acquisitions. The RT and BN models based on T 1 weighted images show better performance than those obtained with T 2 weighted images. T 1 weighted image-based analysis shows area under the curve (AUC) values for The RT and BN models of 0.90 and 0.84, respectively, while corresponding values obtained from T 2 weighted images are 0.79 and 0.78 for RT and BN models respectively. Combined T 1 weighted image-based models RT-BN, RT-LSVM-BN and RT-NN-LSVM-BN also show good performance having AUC values 0.97, 0.92, and 0.90, respectively. These results show that radiomic features from MR images obtained before radiotherapy can be used in addition to other metrics as personalized and unique biomarkers for prediction of early-onset xerostomia. Ensemble classifiers are more efficient than individual classifiers in prediction of early xerostomia. Physical sciences/Engineering/Biomedical engineering Biological sciences/Cancer Health sciences/Oncology Health sciences/Medical research/Translational research Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 24 Apr, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 06 Sep, 2024 Reviews received at journal 06 Sep, 2024 Reviewers agreed at journal 22 Aug, 2024 Reviews received at journal 04 Jun, 2024 Reviewers agreed at journal 29 May, 2024 Reviewers agreed at journal 22 Apr, 2024 Reviewers invited by journal 17 Apr, 2024 Editor assigned by journal 17 Apr, 2024 Editor invited by journal 17 Apr, 2024 Submission checks completed at journal 12 Apr, 2024 First submitted to journal 28 Mar, 2024 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-4185554","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":290715145,"identity":"772b979d-1409-4b9e-b973-4166a6979ce8","order_by":0,"name":"Benyamin Khajetash","email":"","orcid":"","institution":"Iran University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Benyamin","middleName":"","lastName":"Khajetash","suffix":""},{"id":290715148,"identity":"564681f4-0d9c-461f-8e2b-8becdb9c379c","order_by":1,"name":"Seied Rabi Mahdavi","email":"","orcid":"","institution":"Iran University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Seied","middleName":"Rabi","lastName":"Mahdavi","suffix":""},{"id":290715150,"identity":"4940e5b5-401f-4f3f-87cf-364ba458f954","order_by":2,"name":"Alireza Nikoofar","email":"","orcid":"","institution":"Iran University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Alireza","middleName":"","lastName":"Nikoofar","suffix":""},{"id":290715152,"identity":"934a730a-9f9a-4ce8-9551-9a9c01896bda","order_by":3,"name":"Lee Johnson","email":"","orcid":"","institution":"University of Kentucky Chandler Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Lee","middleName":"","lastName":"Johnson","suffix":""},{"id":290715154,"identity":"f8443df6-f2d2-4087-9928-03f12014a9a3","order_by":4,"name":"Meysam Tavakoli","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDElEQVRIiWNgGAWjYJADZgaGAgk5GI+xgYByCYgWAwljkrUwJMJU4tSi295j/OFnDkMd/4zkxwYfDCzS+8UOH/vMw2Aju+EAdi1mZ86YSfZuY5CQuJFmnDjDQCJ35uy05Nk8DGnGOLXcyDFj4AVqYThzwPgwD1DLhts5xsw8DIcTcWq5/8b441+gFvkzxz+DtKTbQ7T8x63lBo+BNMgWg+M9xslALQkG0mAtB3BrOZNWJi27TUJy4/GeYkOgXwxn3E5LZpxjkGw8E5eW44c3f3y7zYZf7jD7ZokPFXXy/LOTDzO8qbCT7cOhhYGBw4ABEi1IgInHAJdyEGB/gCnG+AOfjlEwCkbBKBhpAAAOcVlDarkYjQAAAABJRU5ErkJggg==","orcid":"","institution":"Emory University","correspondingAuthor":true,"prefix":"","firstName":"Meysam","middleName":"","lastName":"Tavakoli","suffix":""}],"badges":[],"createdAt":"2024-03-29 04:00:39","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4185554/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4185554/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-93676-0","type":"published","date":"2025-04-24T15:58:26+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":81569796,"identity":"681a95ae-ba02-400a-81e4-daea6de22f69","added_by":"auto","created_at":"2025-04-28 16:11:19","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2596604,"visible":true,"origin":"","legend":"","description":"","filename":"main.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4185554/v1_covered_5cd0035e-1ef3-40fa-911a-e390ee8ec3e3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Ensemble Learning Approach for Prediction of Early Complications after Radiotherapy for Head and Neck Cancer using CT and MRI Radiomic Features","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4185554/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4185554/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"There are different side effects in radiotherapy of head and neck cancer (HNC) including xerostomia. The present study utilizes the addition of Magnetic Resonance (MR) radiomic image features to typical Computed Tomography (CT)-based features and radiation dose-based characteristics and incorporates the evaluation and validation of individual and ensemble classifiers for prediction of early-onset xerostomia in radiotherapy of HNC. A total of 80 patients diagnosed with HNC were evaluated prospectively. MR and CT imaging, dosimetric, and demographic features of patients were used as model input data. Bilateral parotid radiomic features were extracted from CT, T 1 weighted, and T 2 weighted MR images. Pearson statistical tests were used for selection of features and Random Tree (RT), Neural Network (NN), Linear Support Vector Machine (LSVM) and Bayesian Network (BN) classifiers were evaluated. The results suggest the extracted features from T 1 weighted images have superior prediction ability compared to T 2 weighted acquisitions. The RT and BN models based on T 1 weighted images show better performance than those obtained with T 2 weighted images. T 1 weighted image-based analysis shows area under the curve (AUC) values for The RT and BN models of 0.90 and 0.84, respectively, while corresponding values obtained from T 2 weighted images are 0.79 and 0.78 for RT and BN models respectively. Combined T 1 weighted image-based models RT-BN, RT-LSVM-BN and RT-NN-LSVM-BN also show good performance having AUC values 0.97, 0.92, and 0.90, respectively. These results show that radiomic features from MR images obtained before radiotherapy can be used in addition to other metrics as personalized and unique biomarkers for prediction of early-onset xerostomia. Ensemble classifiers are more efficient than individual classifiers in prediction of early xerostomia.","manuscriptTitle":"Ensemble Learning Approach for Prediction of Early Complications after Radiotherapy for Head and Neck Cancer using CT and MRI Radiomic Features","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-17 05:02:02","doi":"10.21203/rs.3.rs-4185554/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-09-06T07:26:45+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-06T07:05:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"270505631816047520352637224980206029120","date":"2024-08-22T08:13:20+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-04T21:20:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"79160537285202265240382383989689793609","date":"2024-05-29T16:29:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"aefa6d16-14fa-45da-9535-728926eedd08","date":"2024-04-22T19:49:18+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-04-17T15:28:27+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-17T15:07:16+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-04-17T09:36:47+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-04-13T03:05:30+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-03-29T03:59:28+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cbad2183-d94d-4794-85a8-bf5a2f79a0a0","owner":[],"postedDate":"April 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":30626349,"name":"Physical sciences/Engineering/Biomedical engineering"},{"id":30626350,"name":"Biological sciences/Cancer"},{"id":30626351,"name":"Health sciences/Oncology"},{"id":30626352,"name":"Health sciences/Medical research/Translational research"}],"tags":[],"updatedAt":"2025-04-28T16:04:27+00:00","versionOfRecord":{"articleIdentity":"rs-4185554","link":"https://doi.org/10.1038/s41598-025-93676-0","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-04-24 15:58:26","publishedOnDateReadable":"April 24th, 2025"},"versionCreatedAt":"2024-04-17 05:02:02","video":"","vorDoi":"10.1038/s41598-025-93676-0","vorDoiUrl":"https://doi.org/10.1038/s41598-025-93676-0","workflowStages":[]},"version":"v1","identity":"rs-4185554","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4185554","identity":"rs-4185554","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}