Abstract：
Aim: This article aims to identify risk factors for severe radiation-induced oral mucositis (RIOM) in head and neck cancer (HNC) patients. In addition, we intend to establish a predictive model in patients undergoing intensity-modulated radiotherapy. Patients & methods: In this retrospective study, several HNC patients (n = 179) treated at Zhejiang Provincial People\'s Hospital from January 2019 to June 2023 were considered. The recruited subjects were divided into modeling and validation groups. The experimental data on clinical characteristics and treatment were collected and analyzed to identify predictive factors for severe RIOM based on the logistic regression approach. Results: The results indicated that severe RIOM occurred in 55.3% of patients. Accordingly, significant predictors included smoking history, diabetes, concurrent chemotherapy, cumulative radiation dose and weight loss of ≥5% in relative to admission weight. A nomogram based on these factors was validated, showing excellent predictive accuracy. Conclusion: In summary, the predictive model could effectively identify high-risk patients for severe RIOM, enabling the design of targeted interventions and improving patient management during radiotherapy.
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[Box: see text].
摘要：
目的：本文旨在确定头颈部癌症（HNC）患者严重放射性口腔黏膜炎（RIOM）的危险因素。此外,我们打算在接受调强放疗的患者中建立预测模型.患者和方法：在这项回顾性研究中，考虑了2019年1月至2023年6月在浙江省人民医院接受治疗的几名HNC患者（n=179）。将招募的受试者分为建模组和验证组。收集和分析临床特征和治疗的实验数据，以基于logistic回归方法确定严重RIOM的预测因素。结果:55.3%的患者发生严重的RIOM。因此，重要的预测因素包括吸烟史，糖尿病,同步化疗,相对于入院体重，累积辐射剂量和体重减轻≥5%。验证了基于这些因素的列线图，表现出出色的预测准确性。结论：总之，该预测模型可以有效识别重症RIOM的高危患者，能够设计有针对性的干预措施，并改善放疗期间的患者管理。
[方框：见正文]。
[方框：见正文]。
