{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:59:37Z","timestamp":1785545977577,"version":"3.56.0"},"reference-count":59,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100007065","name":"Universit\u00e0 degli Studi di Salerno","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100007065","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Multimodal artificial intelligence (AI) is increasingly applied in head and neck oncology to integrate radiological, histopathological, clinical, and molecular data. While individual studies report encouraging performance, further synthesis is needed to characterise the data modalities, fusion strategies, model architectures, evaluation metrics, and validation pathways used in this field. This systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, and the protocol was registered in the International Prospective Register of Systematic Reviews (PROSPERO; CRD420251244308). A systematic literature search was conducted in PubMed, Web of Science, Scopus, and IEEE Xplore for studies published between 2020 and 2025. Study screening and data extraction were performed independently by two reviewers, with disagreements resolved by consensus. Eligible studies employed artificial intelligence or deep learning models that integrated at least two distinct biomedical data modalities for diagnostic or prognostic tasks in head and neck oncology. Methodological quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) framework. Twenty-three studies met the inclusion criteria. Data modalities included computed tomography (CT), positron emission tomography (PET), magnetic resonance imaging (MRI), histopathology, clinical variables, photographic or endoscopic images, and multi-omics data. Multimodal models generally outperformed unimodal approaches, achieving diagnostic area under the receiver operating characteristic curve (AUC) values up to 0.982 and prognostic concordance indices (C-index) up to 0.966, although performance varied substantially across tasks, datasets, endpoints, and validation strategies. However, evidence remains limited by retrospective designs, heterogeneous validation strategies, and limited external validation.<\/jats:p>","DOI":"10.1007\/s11042-026-21805-6","type":"journal-article","created":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T23:06:46Z","timestamp":1785539206000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multimodal artificial intelligence for diagnosis and prognosis in head and neck cancer: A systematic review"],"prefix":"10.1007","volume":"85","author":[{"given":"Luigia","family":"Rizzo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alessia","family":"Auriemma Citarella","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pier Paolo","family":"Claudio","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Antonio","family":"Cortese","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fabiola","family":"De Marco","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Monica Maria Lucia","family":"Sebillo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Genoveffa","family":"Tortora","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,8,1]]},"reference":[{"key":"21805_CR1","doi-asserted-by":"publisher","unstructured":"Marur S, Forastiere AA (2016) head and neck squamous cell carcinoma: update on epidemiology, diagnosis, and treatment. In: Mayo Clinic Proceedings 91(3):386\u2013396. https:\/\/doi.org\/10.1016\/j.mayocp.2015.12.017","DOI":"10.1016\/j.mayocp.2015.12.017"},{"key":"21805_CR2","doi-asserted-by":"publisher","unstructured":"Bray F (2024) Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 74(3):229\u2013263. https:\/\/doi.org\/10.3322\/caac.21834","DOI":"10.3322\/caac.21834"},{"key":"21805_CR3","doi-asserted-by":"publisher","unstructured":"Tan Y (2023) Oral squamous cell carcinomas: state of the field and emerging directions. Int J Oral Sci 15(1):44. https:\/\/doi.org\/10.1038\/s41368-023-00249-w","DOI":"10.1038\/s41368-023-00249-w"},{"key":"21805_CR4","doi-asserted-by":"publisher","unstructured":"Warnakulasuriya S (2009) Global epidemiology of oral and oropharyngeal cancer. In: Oral oncology 45(4-5):309\u2013316. https:\/\/doi.org\/10.1016\/j.oraloncology.2008.06.002.","DOI":"10.1016\/j.oraloncology.2008.06.002."},{"key":"21805_CR5","doi-asserted-by":"publisher","unstructured":"De Cicco D (2021) Health-related quality of life in oral cancer patients: scoping review and critical appraisal of investigated determinants. Cancers 13(17):4398. https:\/\/doi.org\/10.3390\/cancers13174398","DOI":"10.3390\/cancers13174398"},{"key":"21805_CR6","doi-asserted-by":"publisher","unstructured":"Coletta RD, Yeudall WA, Salo T (2024) Current trends on prevalence, risk factors and prevention of oral cancer. In: Frontiers in Oral Health 5(2024). Specialty Grand Challenge article, Section: Oral Cancers. ISSN: 2673-4842. https:\/\/doi.org\/10.3389\/froh.2024.1505833, https:\/\/www.frontiersin.org\/ articles\/10.3389\/froh.2024.1505833","DOI":"10.3389\/froh.2024.1505833"},{"key":"21805_CR7","doi-asserted-by":"publisher","unstructured":"Kaur G (2024) Assessing bias in the causal role of HPV in oral cancer: A systematic review and meta-analysis. In: Oral Diseases (2024). First published online: 02 July 2024. https:\/\/doi.org\/10.1111\/odi.15062","DOI":"10.1111\/odi.15062"},{"key":"21805_CR8","doi-asserted-by":"publisher","unstructured":"Iocca O et\u00a0al (2020) Potentially malignant disorders of the oral cavity and oral dysplasia: A systematic review and meta-analysis of malignant transformation rate by subtype. In: Head & neck 42(3):539\u2013555. https:\/\/doi.org\/10.1002\/hed.26006","DOI":"10.1002\/hed.26006"},{"key":"21805_CR9","doi-asserted-by":"publisher","unstructured":"Varela-Centelles P (2022) Early diagnosis and diagnostic delay in oral cancer. Cancers 14(7):1758. https:\/\/doi.org\/10.3390\/cancers14071758","DOI":"10.3390\/cancers14071758"},{"key":"21805_CR10","doi-asserted-by":"publisher","unstructured":"Petersen PE (2009) Oral cancer prevention and control\u2013the approach of the World Health Organization. In: Oral oncology 45(4-5):454\u2013460. https:\/\/doi.org\/10.1016\/j.oraloncology.2008.05.023","DOI":"10.1016\/j.oraloncology.2008.05.023"},{"key":"21805_CR11","doi-asserted-by":"publisher","unstructured":"C\u00e9sar Silva L et\u00a0al (2023) The importance of early treatment of oral squamous cell carcinoma: Case report. In: Oral Oncology 141:106442. ISSN: 1879- 0593. https:\/\/doi.org\/10.1016\/j.oraloncology.2023.106442","DOI":"10.1016\/j.oraloncology.2023.106442"},{"key":"21805_CR12","doi-asserted-by":"publisher","unstructured":"Sahoo RK et\u00a0al (2024) Diagnostic performance of artificial intelligence in detecting oral potentially malignant disorders and oral cancer using medical diagnostic imaging: a systematic review and meta-analysis. In: Frontiers in oral health 5:1494867. ISSN: 2673-4842. https:\/\/doi.org\/10.3389\/froh.2024.1494867","DOI":"10.3389\/froh.2024.1494867"},{"key":"21805_CR13","doi-asserted-by":"publisher","unstructured":"Bi WL (2019) Artificial intelligence in cancer imaging: Clinical challenges and applications. CA Cancer J Clin 69(2):127\u2013157. https:\/\/doi.org\/10.3322\/caac.21552","DOI":"10.3322\/caac.21552"},{"key":"21805_CR14","doi-asserted-by":"publisher","unstructured":"Tran KA (2021) Deep learning in cancer diagnosis, prognosis and treatment selection. Genome medicine 13:152. https:\/\/doi.org\/10.1186\/s13073-021-00968-x","DOI":"10.1186\/s13073-021-00968-x"},{"key":"21805_CR15","doi-asserted-by":"publisher","unstructured":"Esteva A (2019) A guide to deep learning in healthcare. Nat Med 25(1):24\u201329. https:\/\/doi.org\/10.1038\/s41591-018-0316-z","DOI":"10.1038\/s41591-018-0316-z"},{"key":"21805_CR16","doi-asserted-by":"publisher","unstructured":"Zhao F, Zhang C, Geng B (2024) Deep multimodal data fusion. ACM Comput Surv 56(9):1\u201336. https:\/\/doi.org\/10.1145\/3649447","DOI":"10.1145\/3649447"},{"key":"21805_CR17","doi-asserted-by":"publisher","unstructured":"Aerts HJWL (2016) The potential of radiomic-based phenotyping in precision medicine: a review. In: JAMA oncology 2(12):1636\u20131642. https:\/\/doi.org\/10.1001\/jamaoncol.2016.2631. ISSN: 2374-2437","DOI":"10.1001\/jamaoncol.2016.2631"},{"key":"21805_CR18","doi-asserted-by":"publisher","unstructured":"Rajpurkar P 2022) AI in health and medicine. Nat Med 28(1):31\u201338. https:\/\/doi.org\/10.1038\/s41591-021-01614-0","DOI":"10.1038\/s41591-021-01614-0"},{"key":"21805_CR19","doi-asserted-by":"publisher","unstructured":"Pandeya YR, Lee J (2021) Deep learning-based late fusion of multimodal information for emotion classification of music video. In: Multimedia tools and applications 80(2):2887\u20132905. https:\/\/doi.org\/10.1007\/s11042-020-08836-3","DOI":"10.1007\/s11042-020-08836-3"},{"key":"21805_CR20","doi-asserted-by":"publisher","unstructured":"Waqas A (2025) Self-Normalizing Multi-Omics Neural Network for Pan-Cancer Prognostication. Int J Mol Sci 26:7358. https:\/\/doi.org\/10.3390\/ijms26157358","DOI":"10.3390\/ijms26157358"},{"key":"21805_CR21","doi-asserted-by":"publisher","unstructured":"Echle A (2021) Deep learning in cancer pathology: a new generation of clinical biomarkers. Br J Cancer 124(4):686\u2013696. https:\/\/doi.org\/10.1038\/s41416-020-01122-x","DOI":"10.1038\/s41416-020-01122-x"},{"key":"21805_CR22","doi-asserted-by":"publisher","unstructured":"Alabi RO, M\u00e4kitie AA et\u00a0al (2024) Artificial intelligence-driven radiomics in head and neck cancer: current status and future prospects. In: International journal of medical informatics 188:105464. ISSN: 1386-5056. https:\/\/doi.org\/10.1016\/j.ijmedinf.2024.105464","DOI":"10.1016\/j.ijmedinf.2024.105464"},{"key":"21805_CR23","doi-asserted-by":"publisher","unstructured":"Mahmood H (2021) Artificial Intelligence-based methods in head and neck cancer diagnosis: an overview. Br J Cancer 124(12):1934\u20131940. https:\/\/doi.org\/10.1038\/s41416-021-01386-x","DOI":"10.1038\/s41416-021-01386-x"},{"key":"21805_CR24","doi-asserted-by":"publisher","unstructured":"M\u00e4kitie AA et\u00a0al (2023) Artificial intelligence in head and neck cancer: a systematic review of systematic reviews. In: Advances in Therapy 40(8):3360\u20133380. https:\/\/doi.org\/10.1007\/s12325-023-02527-9.","DOI":"10.1007\/s12325-023-02527-9."},{"key":"21805_CR25","doi-asserted-by":"publisher","unstructured":"Loperfido A et\u00a0al (2023) Current role of artificial intelligence in head and neck cancer surgery: a systematic review of literature. In: Exploration of targeted anti-tumor therapy 4(5):933\u2013940. https:\/\/doi.org\/10.37349\/etat.2023.00174","DOI":"10.37349\/etat.2023.00174"},{"key":"21805_CR26","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1016\/j.oraloncology.2018.10.026","volume":"87","author":"V Kearney","year":"2018","unstructured":"Kearney V et al (2018) The application of artificial intelligence in the IMRT planning process for head and neck cancer. Oral Oncol 87:111\u2013116. https:\/\/doi.org\/10.1016\/j.oraloncology.2018.10.026","journal-title":"Oral Oncol"},{"key":"21805_CR27","doi-asserted-by":"publisher","unstructured":"Liu X et\u00a0al (2019) A comparison of deep learning performance against healthcare professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. In: The lancet digital health 1(6):e271\u2013e297. https:\/\/doi.org\/10.1016\/S2589-7500(19)30123-2","DOI":"10.1016\/S2589-7500(19)30123-2"},{"key":"21805_CR28","doi-asserted-by":"publisher","unstructured":"Mahmood H (2020) Use of artificial intelligence in diagnosis of head and neck precancerous and cancerous lesions: A systematic review. Oral Oncol 110:104885. https:\/\/doi.org\/10.1016\/j.oraloncology.2020.104885","DOI":"10.1016\/j.oraloncology.2020.104885"},{"key":"21805_CR29","doi-asserted-by":"publisher","unstructured":"Page MJ et\u00a0al (2021) The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. In: BMJ 372:n71. https:\/\/doi.org\/10.1136\/bmj.n71","DOI":"10.1136\/bmj.n71"},{"key":"21805_CR30","doi-asserted-by":"publisher","unstructured":"Petticrew M, Roberts H (2006) Systematic reviews in the social sciences: a practical guide. Oxford, UK: Blackwell Publishing. ISBN 9781405121101. https:\/\/doi.org\/10.1002\/9780470754887","DOI":"10.1002\/9780470754887"},{"key":"21805_CR31","doi-asserted-by":"publisher","unstructured":"Whiting PF (2011) QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med 155(8):529\u2013536. https:\/\/doi.org\/10.7326\/0003-4819-155-8-201110180-00009","DOI":"10.7326\/0003-4819-155-8-201110180-00009"},{"key":"21805_CR32","volume-title":"Information Retrieval","author":"CJ van Rijsbergen","year":"1979","unstructured":"van Rijsbergen CJ (1979) Information Retrieval, 2nd edn. Butterworths, London","edition":"2"},{"key":"21805_CR33","doi-asserted-by":"publisher","unstructured":"Hanley JA, McNeil BJ (1982) The Meaning and Use of the Area under a Receiver Operating Characteristic (ROC) Curve. In: Radiology 143(1):29\u201336. https:\/\/doi.org\/10.1148\/radiology.143.1.7063747","DOI":"10.1148\/radiology.143.1.7063747"},{"key":"21805_CR34","doi-asserted-by":"publisher","unstructured":"Dice LR (1945) Measures of the amount of ecologic association between species. Ecology 26(3):297\u2013302. https:\/\/doi.org\/10.2307\/1932409","DOI":"10.2307\/1932409"},{"key":"21805_CR35","doi-asserted-by":"publisher","unstructured":"Harrell FE Jr, Lee KL, Mark DB (1996) Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. In: Statistics in medicine 15(4):361\u2013387. https:\/\/doi.org\/10.1002\/(SICI)1097-0258(19960229)15:4h361::AID-SIM168i3.0.CO;2-4","DOI":"10.1002\/(SICI)1097-0258(19960229)15:4h361::AID-SIM168i3.0.CO;2-4"},{"key":"21805_CR36","doi-asserted-by":"publisher","unstructured":"Ariji Y (2019) Automatic detection of oral cancer in panoramic radiographs using deep learning. Oral Radiol 35(3):211\u2013217. https:\/\/doi.org\/10.1007\/s11282-018-0363-5","DOI":"10.1007\/s11282-018-0363-5"},{"key":"21805_CR37","doi-asserted-by":"publisher","unstructured":"Li W (2024) Integrating lipid metabolite analysis with MRI-based transformer and radiomics for early and late stage prediction of oral squamous cell carcinoma. BMC Cancer 24(1):795. https:\/\/doi.org\/10.1186\/s12885-024-12533-x","DOI":"10.1186\/s12885-024-12533-x"},{"key":"21805_CR38","doi-asserted-by":"publisher","unstructured":"Lian W (2025) Let it shine: Autofluorescence of Papanicolaou-stain improves AIbased cytological oral cancer detection. Comput Biol Med 185:109498. https:\/\/doi.org\/10.1016\/j.compbiomed.2024.109498","DOI":"10.1016\/j.compbiomed.2024.109498"},{"key":"21805_CR39","doi-asserted-by":"publisher","unstructured":"Li J, Zhou Y, Zhang M (2025) Next-generation AI framework for comprehensive oral leukoplakia evaluation and management. NPJ digital medicine 8:513. https:\/\/doi.org\/10.1038\/s41746-025-01885-8","DOI":"10.1038\/s41746-025-01885-8"},{"key":"21805_CR40","doi-asserted-by":"publisher","unstructured":"Kumari KS, Shree SU, Calarany C (2024) Integrating multimodal ai models for enhanced oral cancer detection and diagnosis. 2024 International Conference on Integrated Intelligence and Communication Systems (ICIICS). pp 1\u20136. https:\/\/doi.org\/10.1109\/ICIICS63763.2024.10859605","DOI":"10.1109\/ICIICS63763.2024.10859605"},{"key":"21805_CR41","doi-asserted-by":"publisher","unstructured":"Shakila M (2024) Integration of clinical and imaging data for enhancing oral cancer detection using deep learning. 2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems (IACIS). IEEE, Hassan, India, pp 1\u20136. https:\/\/doi.org\/10.1109\/IACIS61494.2024.10721686","DOI":"10.1109\/IACIS61494.2024.10721686"},{"key":"21805_CR42","doi-asserted-by":"publisher","unstructured":"Jiang H et\u00a0al (2025) A multimodal deep learning approach for pre-operative tumor-node-metastasis staging in oral cancer using computed tomography imaging and pathological slide data. In: Journal of circuits, systems and computers 34(07). https:\/\/doi.org\/10.1142\/S0218126625501737","DOI":"10.1142\/S0218126625501737"},{"key":"21805_CR43","doi-asserted-by":"publisher","unstructured":"Munnangi S, Satheeskumar R (2025) AI-driven biomarker discovery for early diagnosis and prognosis in oral oncology. In: Oral oncology reports 14:100749. https:\/\/doi.org\/10.1016\/j.oor.2025.100749, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2772906025000378","DOI":"10.1016\/j.oor.2025.100749"},{"key":"21805_CR44","doi-asserted-by":"publisher","unstructured":"Confer MP (2024) Rapid and label-free histopathology of oral lesions using deep learning applied to optical and infrared spectroscopic imaging data. Journal of personalized medicine 14:304. https:\/\/doi.org\/10.3390\/jpm14030304","DOI":"10.3390\/jpm14030304"},{"key":"21805_CR45","doi-asserted-by":"publisher","unstructured":"de Lima LM et\u00a0al (2023) Importance of complementary data to histopathological image analysis of oral leukoplakia and carcinoma using deep neural networks. In: Intelligent medicine 3(4):258\u2013266. https:\/\/doi.org\/10.1016\/j.imed.2023.01.004","DOI":"10.1016\/j.imed.2023.01.004"},{"key":"21805_CR46","doi-asserted-by":"publisher","unstructured":"Shephard AJ (2025) Development and validation of an artificial intelligencebased pipeline for predicting oral epithelial dysplasia malignant transformation. Communications medicine 5(1):186. https:\/\/doi.org\/10.1038\/s43856-025-00873-z","DOI":"10.1038\/s43856-025-00873-z"},{"key":"21805_CR47","doi-asserted-by":"publisher","unstructured":"Song B et\u00a0al (2025) Deep learning informed multimodal fusion of radiology and pathology to predict outcomes in HPV-associated oropharyngeal squamous cell carcinoma. In: eBioMedicine 114:105663. https:\/\/doi.org\/10.1016\/j.ebiom.2024.105663","DOI":"10.1016\/j.ebiom.2024.105663"},{"key":"21805_CR48","doi-asserted-by":"publisher","unstructured":"Le WT (2022) Cross-institutional outcome prediction for head and neck cancer patients using self-attention neural networks. In: Scientific reports 12(1):3183. https:\/\/doi.org\/10.1038\/s41598-022-07034-5","DOI":"10.1038\/s41598-022-07034-5"},{"key":"21805_CR49","doi-asserted-by":"publisher","unstructured":"Spielvogel CP (2023) Radiogenomic markers enable risk stratification and inference of mutational pathway states in head and neck cancer. Eur J Nucl Med Mol Imaging 50(2):546\u2013558. https:\/\/doi.org\/10.1007\/s00259-022-05973-9","DOI":"10.1007\/s00259-022-05973-9"},{"key":"21805_CR50","doi-asserted-by":"publisher","unstructured":"Salahuddin Z (2023) From head and neck tumour and lymph node segmentation to survival prediction on PET\/CT: an end-to-end framework featuring uncertainty, fairness, and multi-region multi-modal radiomics. Cancers 15(7):1932. https:\/\/doi.org\/10.3390\/cancers15071932","DOI":"10.3390\/cancers15071932"},{"key":"21805_CR51","doi-asserted-by":"publisher","unstructured":"Wang Z (2024) An innovative and efficient diagnostic prediction flow for head and neck cancer: a deep learning approach for multi-modal survival analysis prediction based on text and multi-center PET\/CT images. Diagnostics 14(4):448. https:\/\/doi.org\/10.3390\/diagnostics14040448","DOI":"10.3390\/diagnostics14040448"},{"key":"21805_CR52","doi-asserted-by":"publisher","unstructured":"Meneghetti AR, Zwanenburg A, Linge A (2022) Integrated radiogenomics analyses allow for subtype classification and improved outcome prognosis of patients with locally advanced HNSCC. In: Scientific reports 12:16755. https:\/\/doi.org\/10.1038\/s41598-022-21159-7","DOI":"10.1038\/s41598-022-21159-7"},{"key":"21805_CR53","doi-asserted-by":"publisher","unstructured":"Wang B (2025) A stacking ensemble framework integrating radiomics and deep learning for prognostic prediction in head and neck cancer. Radiat Oncol 20(1):127. https:\/\/doi.org\/10.1186\/s13014-025-02695-8","DOI":"10.1186\/s13014-025-02695-8"},{"key":"21805_CR54","doi-asserted-by":"publisher","unstructured":"Zhao Y (2025) Integrative multi-omics analysis and experimental validation identify molecular subtypes, prognostic signature, and CA9 as a therapeutic target in oral squamous cell carcinoma. Frontiers in cell and developmental biology 13:1629683. https:\/\/doi.org\/10.3389\/fcell.2025.1629683","DOI":"10.3389\/fcell.2025.1629683"},{"key":"21805_CR55","doi-asserted-by":"publisher","unstructured":"Deepali, Goel N, Khandnor P (2025) DeepOmicsSurv: a deep learning-based model for survival prediction of oral cancer. Discover oncology 16:614. https:\/\/doi.org\/10.1007\/s12672-025-02346-0","DOI":"10.1007\/s12672-025-02346-0"},{"key":"21805_CR56","doi-asserted-by":"publisher","unstructured":"Wei L (2024) Interpretable survival network for progression risk analysis of multimodality imaging biomarkers in poor-prognosis head and neck cancers. In: Scientific reports 14(1):30004. https:\/\/doi.org\/10.1038\/s41598-024-80815-2","DOI":"10.1038\/s41598-024-80815-2"},{"key":"21805_CR57","doi-asserted-by":"publisher","first-page":"21353","DOI":"10.1007\/s00521-022-07615-5","volume":"34","author":"D Sharma","year":"2022","unstructured":"Sharma D, Deepali GVK et al (2022) A deep learning-based integrative model for survival time prediction of head and neck squamous cell carcinoma patients. Neural Comput Appl 34:21353\u201321365. https:\/\/doi.org\/10.1007\/s00521-022-07615-5","journal-title":"Neural Comput Appl"},{"key":"21805_CR58","doi-asserted-by":"publisher","first-page":"110905","DOI":"10.1109\/ACCESS.2024.3439439","volume":"12","author":"MA Mahdi","year":"2024","unstructured":"Mahdi MA et al (2024) Weighted Fusion Transformer for Dual PET\/CT Head and Neck Tumor Segmentation. IEEE Access 12:110905\u2013110919. https:\/\/doi.org\/10.1109\/ACCESS.2024.3439439","journal-title":"IEEE Access"},{"key":"21805_CR59","doi-asserted-by":"publisher","unstructured":"Coole JB et\u00a0al (2023) Multimodal optical imaging with real-time projection of cancer risk and biopsy guidance maps for early oral cancer diagnosis and treatment. In: Journal of biomedical optics 28(1):016002. https:\/\/doi.org\/10.1117\/1.JBO.28.1.016002","DOI":"10.1117\/1.JBO.28.1.016002"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-026-21805-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-026-21805-6","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-026-21805-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T23:06:48Z","timestamp":1785539208000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-026-21805-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,1]]},"references-count":59,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2026,8]]}},"alternative-id":["21805"],"URL":"https:\/\/doi.org\/10.1007\/s11042-026-21805-6","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,1]]},"assertion":[{"value":"31 March 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 June 2026","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 July 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 August 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Authors Luigia Rizzo, Alessia Auriemma Citarella, Fabiola De Marco, Monica Maria Lucia Sebillo, and Genoveffa Tortora are from the same institution as Guest Editor Rita Francese. The Guest Editors were not involved in the journal\u2019s review of, or decisions related to, this manuscript. Peer review was handled by Harry Agius, Deputy Editor in Chief.","order":1,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Ethical approval is not required, as the study is based exclusively on published literature.","order":2,"name":"Ethics","label":"Ethics approval","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":3,"name":"Ethics","label":"Consent for publication","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":4,"name":"Ethics","label":"Materials and code availability","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"670"}}