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Olgun Elicin

Publications and source records attributed to Olgun Elicin.

2 recordsLinked to original sources
PubMed2026

De-escalation of radiotherapy in HPV-negative non-nasopharyngeal head and neck squamous cell carcinoma: a systematic review.

BACKGROUND: Definitive and postoperative radiotherapy are central components of treatment for head and neck squamous cell carcinoma (HNSCC) but are associated with significant toxicities that can impair long-term function and quality of life. De-escalation strategies, aiming to reduce treatment-related morbidity while maintaining tumor control, have attracted increasing interest. However, most research has focused on HPV-positive oropharyngeal carcinoma. Systematic evidence for HPV-negative disease remains limited. METHODS: PubMed and EMBASE were searched for prospective studies investigating radio(chemo)therapy de-escalation in HPV-negative, HPV-unspecified, or mixed non-nasopharyngeal HNSCC populations. CLINICALTRIALS: gov was searched for ongoing prospective trials. Data extraction and verification were performed independently by three investigators. RESULTS: Screening of 3156 records identified 14 published prospective studies, 10 in the definitive and four in the postoperative setting. Strategies included reduction or omission of elective nodal volumes, dose reduction, and combined approaches. Additionally, 23 ongoing prospective trials were identified. Across studies, elective nodal failure rates were consistently low (0-4.6%), with most recurrences occurring within high-dose volumes rather than de-escalated elective regions. Randomized evidence for elective nodal dose reduction is mixed: two trials maintained regional control and reduced acute toxicity, whereas another was stopped for futility. CONCLUSION: Available evidence on de-escalation in HPV-negative HNSCC is limited, derived primarily from small, heterogeneous phase II studies with mixed HPV populations. Although data are promising in selected settings, notably for elective nodal control, the randomized evidence for elective nodal dose reduction is conflicting, and further adequately designed prospective randomized trials are required.

Humans↗
PubMed2026

Toward a Better Paradigm for Head and Neck Cancer Treatment Applying AI (HNC-TACTIC): Protocol for an International Cohort Study of Electronic Health Records.

BACKGROUND: Head and neck squamous cell carcinomas (HNSCCs) cause considerable morbidity and mortality. Multimodal treatment strategies can cause significant toxicity, and therapy options are limited for recurrent disease. Immunotherapy has emerged as a promising approach. However, patient response variability underscores the need for better predictive markers. OBJECTIVE: This study aims to use artificial intelligence to develop two predictive models in patients with HNSCC to assess (1) progression or recurrence following primary curative treatment and (2) long-term survival after immunotherapy schemes in recurrent and metastatic disease. This study will also describe the characteristics of patients with early, locally advanced, and recurrent or metastatic cancers. METHODS: This is a retrospective, observational study of data captured in electronic health records (EHRs) from participating hospitals between January 1, 2014, and December 31, 2021. This study's population comprises adults diagnosed with HNSCC at any stage. Study variables, including demographics, comorbidities, clinical variables, treatments, and outcomes, will be extracted using EHRead, a technology that applies natural language processing and machine learning to extract and analyze structured and unstructured clinical information in deidentified EHRs. Predictive models based on dynamic risk stratification for treatment response and progression or recurrence will be developed using multivariable logistic regressions, decision tree classifiers, and random forest approaches. Descriptive and outcome analyses will be shown for different anatomic subsites and stratified by stage and treatment. RESULTS: This study began enrolling sites in July 2021 and is currently ongoing. By December 2025, data from 10 centers has been collected, comprising a total of 151,934,990 EHRs from 2,159,719 patients. CONCLUSIONS: Development of predictive models using artificial intelligence will advance clinical understanding of HNSCC to improve patient outcomes.

Humans↗