CT Texture Analysis of Pediatric Teratomas—Associations with Identification and Grading of Immature Teratoma

CT Texture Analysis of Pediatric Teratomas—Associations with Identification and Grading of Immature Teratoma | 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 CT Texture Analysis of Pediatric Teratomas—Associations with Identification and Grading of Immature Teratoma Xinxin Qi, Xiaoyu Wang, Wen Zhao, Songyu Teng, Guanglun Zhou, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4534699/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Jul, 2025 Read the published version in BMC Medical Imaging → Version 1 posted 11 You are reading this latest preprint version Abstract Background Teratomas are categorized into mature teratomas (MT) and immature teratomas (IT) of I-III grades according to content of immature tissues. The existing diagnostic methods are not comprehensive and objective enough. This study aims to utilize computed tomography texture analysis (CTTA) to examine intratumoral components and improve preoperative identification and grading of IT. Methods We analyzed the CT features and texture features of intratumoral components in teratomas(MT = 26, IT = 26). To assess intratumoral components' efficacy, logistic regression models were formulated for both MT and IT intergroups, as well as different grades within IT intragroups. Results Texture features showed 22, 30, and 43 differential texture features for fat, calcification, and solid components between IT and MT group, respectively (p < 0.05). Within those, neighborhood gray tone difference_ busyness (NGLCM_busyness) feature for solid components in IT group was obviously higher than MT (p = 0.000), with the value being higher in grade II than grade I (p = 0.020). Logistic regression analysis indicated that IT identification efficacy of fat, calcifications, and solid components models were 0.778, 0.774, and 0.976, respectively. Conclusion CTTA is an effective method for IT identification and grading, with NGTDM features holding unique value. Among tumor components, the diagnostic value of solid components is the highest. Immature teratoma CT texture analysis Differential diagnosis Tumor grading Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 01 Jul, 2025 Read the published version in BMC Medical Imaging → Version 1 posted Editorial decision: Revision requested 10 Apr, 2025 Reviews received at journal 10 Apr, 2025 Reviewers agreed at journal 02 Apr, 2025 Reviews received at journal 08 Aug, 2024 Reviewers agreed at journal 08 Aug, 2024 Reviewers agreed at journal 29 Jul, 2024 Reviewers invited by journal 22 Jul, 2024 Editor invited by journal 10 Jun, 2024 Editor assigned by journal 10 Jun, 2024 Submission checks completed at journal 10 Jun, 2024 First submitted to journal 05 Jun, 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-4534699","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":313540859,"identity":"b1455330-e325-4aca-b241-346c23ba27f1","order_by":0,"name":"Xinxin Qi","email":"","orcid":"","institution":"China Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xinxin","middleName":"","lastName":"Qi","suffix":""},{"id":313540860,"identity":"2450e491-33ae-4303-ae36-5b056a8bc20c","order_by":1,"name":"Xiaoyu Wang","email":"","orcid":"","institution":"Shenzhen Children's 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Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Immature teratoma, CT texture analysis, Differential diagnosis, Tumor grading","lastPublishedDoi":"10.21203/rs.3.rs-4534699/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4534699/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eTeratomas are categorized into mature teratomas (MT) and immature teratomas (IT) of I-III grades according to content of immature tissues. The existing diagnostic methods are not comprehensive and objective enough. This study aims to utilize computed tomography texture analysis (CTTA) to examine intratumoral components and improve preoperative identification and grading of IT.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe analyzed the CT features and texture features of intratumoral components in teratomas(MT\u0026thinsp;=\u0026thinsp;26, IT\u0026thinsp;=\u0026thinsp;26). To assess intratumoral components' efficacy, logistic regression models were formulated for both MT and IT intergroups, as well as different grades within IT intragroups.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eTexture features showed 22, 30, and 43 differential texture features for fat, calcification, and solid components between IT and MT group, respectively (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Within those, neighborhood gray tone difference_ busyness (NGLCM_busyness) feature for solid components in IT group was obviously higher than MT (p\u0026thinsp;=\u0026thinsp;0.000), with the value being higher in grade II than grade I (p\u0026thinsp;=\u0026thinsp;0.020). Logistic regression analysis indicated that IT identification efficacy of fat, calcifications, and solid components models were 0.778, 0.774, and 0.976, respectively.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eCTTA is an effective method for IT identification and grading, with NGTDM features holding unique value. Among tumor components, the diagnostic value of solid components is the highest.\u003c/p\u003e","manuscriptTitle":"CT Texture Analysis of Pediatric Teratomas—Associations with Identification and Grading of Immature Teratoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-25 09:14:07","doi":"10.21203/rs.3.rs-4534699/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-04-10T18:12:27+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-10T17:45:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"180587170332839588641944006782516036976","date":"2025-04-02T11:13:21+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-08T06:56:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"187041833816270480231627125467668471846","date":"2024-08-08T06:36:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"19381574219528156367101198354187747314","date":"2024-07-29T07:40:33+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-22T11:50:15+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-06-10T09:12:15+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-06-10T09:10:43+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-06-10T09:09:54+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Imaging","date":"2024-06-05T14:10:06+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmim","sideBox":"Learn more about [BMC Medical Imaging](http://bmcmedimaging.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmim/default.aspx","title":"BMC Medical Imaging","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f64d0934-11bc-40b0-a6e2-c4b5709f95c0","owner":[],"postedDate":"June 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-07-07T16:00:33+00:00","versionOfRecord":{"articleIdentity":"rs-4534699","link":"https://doi.org/10.1186/s12880-025-01764-4","journal":{"identity":"bmc-medical-imaging","isVorOnly":false,"title":"BMC Medical Imaging"},"publishedOn":"2025-07-01 15:57:09","publishedOnDateReadable":"July 1st, 2025"},"versionCreatedAt":"2024-06-25 09:14:07","video":"","vorDoi":"10.1186/s12880-025-01764-4","vorDoiUrl":"https://doi.org/10.1186/s12880-025-01764-4","workflowStages":[]},"version":"v1","identity":"rs-4534699","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4534699","identity":"rs-4534699","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}