Deep learning and machine learning integration of radiomics and transcriptomics predicts response-adapted radiotherapy outcome and radiosensitivity in resectable locally advanced laryngeal carcinoma.
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| Title: | Deep learning and machine learning integration of radiomics and transcriptomics predicts response-adapted radiotherapy outcome and radiosensitivity in resectable locally advanced laryngeal carcinoma. |
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| Authors: | Ujjahan S; Department of Radiotherapy, Chattogram Maa O Shishu Hospital & Park View Hospital, Chittagong, Bangladesh., Noman ASM; Department of Biochemistry and Molecular Biology, University of Chittagong, Chittagong, Bangladesh., Al-Johani SS; Department of Molecular Oncology, King Faisal Specialist Hospital & Research Centre, Riyadh, Saudi Arabia., Shinwari Z; Therapeutics & Biomarker Discovery for Clinical Application, King Faisal Specialist Hospital & Research Centre, Riyadh, Saudi Arabia., Alaiya AA; Therapeutics & Biomarker Discovery for Clinical Application, King Faisal Specialist Hospital & Research Centre, Riyadh, Saudi Arabia., Islam SS; Department of Molecular Oncology, King Faisal Specialist Hospital & Research Centre, Riyadh, Saudi Arabia.; Institute of Medical Science, Al-Faisal University, Riyadh, Saudi Arabia. |
| Source: | Frontiers in artificial intelligence [Front Artif Intell] 2026 Jan 12; Vol. 8, pp. 1738174. Date of Electronic Publication: 2026 Jan 12 (Print Publication: 2025). |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Frontiers Media SA Country of Publication: Switzerland NLM ID: 101770551 Publication Model: eCollection Cited Medium: Internet ISSN: 2624-8212 (Electronic) Linking ISSN: 26248212 NLM ISO Abbreviation: Front Artif Intell Subsets: PubMed not MEDLINE |
| Database: | MEDLINE Ultimate |
| FullText | Text: Availability: 0 |
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| Header | DbId: mdl DbLabel: MEDLINE Ultimate An: 41602310 AccessLevel: 2 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Deep learning and machine learning integration of radiomics and transcriptomics predicts response-adapted radiotherapy outcome and radiosensitivity in resectable locally advanced laryngeal carcinoma. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AU" term="%22Ujjahan+S%22">Ujjahan S</searchLink>; Department of Radiotherapy, Chattogram Maa O Shishu Hospital & Park View Hospital, Chittagong, Bangladesh.<br /><searchLink fieldCode="AU" term="%22Noman+ASM%22">Noman ASM</searchLink>; Department of Biochemistry and Molecular Biology, University of Chittagong, Chittagong, Bangladesh.<br /><searchLink fieldCode="AU" term="%22Al-Johani+SS%22">Al-Johani SS</searchLink>; Department of Molecular Oncology, King Faisal Specialist Hospital & Research Centre, Riyadh, Saudi Arabia.<br /><searchLink fieldCode="AU" term="%22Shinwari+Z%22">Shinwari Z</searchLink>; Therapeutics & Biomarker Discovery for Clinical Application, King Faisal Specialist Hospital & Research Centre, Riyadh, Saudi Arabia.<br /><searchLink fieldCode="AU" term="%22Alaiya+AA%22">Alaiya AA</searchLink>; Therapeutics & Biomarker Discovery for Clinical Application, King Faisal Specialist Hospital & Research Centre, Riyadh, Saudi Arabia.<br /><searchLink fieldCode="AU" term="%22Islam+SS%22">Islam SS</searchLink>; Department of Molecular Oncology, King Faisal Specialist Hospital & Research Centre, Riyadh, Saudi Arabia.; Institute of Medical Science, Al-Faisal University, Riyadh, Saudi Arabia. – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22101770551%22">Frontiers in artificial intelligence</searchLink> [Front Artif Intell] 2026 Jan 12; Vol. 8, pp. 1738174. <i>Date of Electronic Publication: </i>2026 Jan 12 (<i>Print Publication: </i>2025). – Name: TypePub Label: Publication Type Group: TypPub Data: Journal Article – Name: TitleSource Label: Journal Info Group: Src Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22Frontiers+Media+SA%22">Frontiers Media SA </searchLink><i>Country of Publication: </i>Switzerland <i>NLM ID: </i>101770551 <i>Publication Model: </i>eCollection <i>Cited Medium: </i>Internet <i>ISSN: </i>2624-8212 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2226248212%22">26248212 </searchLink><i>NLM ISO Abbreviation: </i>Front Artif Intell <i>Subsets: </i>PubMed not MEDLINE |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=mdl&AN=41602310 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3389/frai.2025.1738174 Languages: – Code: eng Text: English PhysicalDescription: Pagination: StartPage: 1738174 Titles: – TitleFull: Deep learning and machine learning integration of radiomics and transcriptomics predicts response-adapted radiotherapy outcome and radiosensitivity in resectable locally advanced laryngeal carcinoma. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Ujjahan S – PersonEntity: Name: NameFull: Noman ASM – PersonEntity: Name: NameFull: Al-Johani SS – PersonEntity: Name: NameFull: Shinwari Z – PersonEntity: Name: NameFull: Alaiya AA – PersonEntity: Name: NameFull: Islam SS IsPartOfRelationships: – BibEntity: Dates: – D: 12 M: 01 Text: 2026 Jan 12 Type: published Y: 2026 Identifiers: – Type: issn-electronic Value: 2624-8212 Numbering: – Type: volume Value: 8 Titles: – TitleFull: Frontiers in artificial intelligence Type: main |
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