Background The rapid growth of biomedical publishing in radiation oncology has created a need for automated methods to extract structured information from unstructured text. Large language models (LLMs) show promise in biomedical natural language processing, but their use for clinical trial data curation remains to be systematically evaluated. Methods We evaluated an LLM-based extraction system for radiation oncology trials on ClinicalTrials.gov. Forty-five classification questions spanning variables such as trial design, tumor type, stage, radiotherapy modality, systemic therapy, and outcomes were implemented using the data-element-extractor Python library with three open-source LLMs (GPT-OSS-20B, GPT-OSS-120B, and Qwen-3.6-27B). A manually curated ground-truth of 94 trials was used for performance evaluation based on accuracy, precision, recall, and F1-score. Results Qwen-3.6-27B achieved the highest overall accuracy of 92.0% across all questions. For the 39 Boolean questions, it demonstrated 93.0% accuracy, 81.6% precision, 83.7% recall, and an F1-score of 82.6%, with 23 Boolean questions achieving an F1-score ≥80.0%. Three numerical questions reached accuracy ≥80.0%. Applied to the full dataset, the system characterized the clinical landscape of radiotherapy trials, detailing the prevalence of advanced techniques (e.g., 13.7% SBRT) and the integration of systemic agents, including chemotherapy (57.3%) and immunotherapy (26.1%). Conclusion LLM-based extraction can enable accurate, scalable curation of radiotherapy trial data. While challenges remain with complex categorical or numerical variables, open-source LLMs can serve as effective components of transparent, auditable clinical research pipelines.

[This corrects the article DOI: 10.1016/j.omton.2026.201320.].

Cisplatin remains a cornerstone of head and neck cancer (HNC) treatment, yet resistance frequently undermines therapeutic efficacy. Emerging evidence implicates Fusobacterium nucleatum, enriched in the tumour microbiome, in promoting chemoresistance via molecular and microenvironmental mechanisms. This review synthesises evidence from PubMed, Scopus, and Web of Science (January 2015-September 2025) examining F. nucleatum's contribution to cisplatin resistance in HNC. Cisplatin resistance involves tumour-intrinsic pathways, such as nucleotide excision repair, homologous recombination, and ATP7B-mediated drug efflux. F. nucleatum indirectly enhances resistance via Wnt5a/Ca[2+]-NFATc3 signalling, downregulation of p53 and E-cadherin, and activation of the NLRP3 inflammasome, which recruits myeloid-derived suppressor cells. It also amplifies inflammation and chemoresistance by escalating the senescence-associated secretory phenotype, promoting a tumour-supportive microenvironment. These findings highlight microbiome host crosstalk as a therapeutic target, suggesting that microbiota-directed strategies alongside cisplatin may improve outcomes in resistant HNC.

Introduction Symptom burden is a critical determinant of treatment tolerance and survivorship in multiple myeloma (MM). We investigated how symptom severity and interference relate to patient-reported quality of life (QoL) across multiple domains in a multicenter real-world MM cohort. Methods This multicenter study included 250 previously treated patients with multiple myeloma recruited from six hospitals in the West Bank, Palestine.Symptom severity and interference were assessed at baseline using the MDASI-MM, and QoL was evaluated one month later with the FACT-MM. Multivariable linear regression adjusted for demographic and clinical covariates was applied. Results are reported as adjusted β coefficients with 95% confidence intervals, and a two-sided P<0.05 was considered statistically significant. Results Greater symptom burden was consistently associated with poorer quality of life. General, specific, and overall symptom severity showed the strongest associations with lower physical well-being, disease-specific concerns, pain-related quality of life, Trial Outcome Index, and FACT-MM total score. Specific symptoms were also associated with lower social well-being. Symptom interference domains showed similar but smaller associations, particularly with physical well-being, functional well-being, pain, and composite quality-of-life scores. Conclusion In this multicenter cohort of patients with multiple myeloma, higher symptom burden was consistently associated with poorer quality of life, especially in physical functioning, pain, and disease-specific concerns. Routine symptom monitoring may help identify patients who need early supportive care interventions.