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 |
| ISSN: | 2624-8212 |
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| DOI: | 10.3389/frai.2025.1738174 |
