Background/Objectives: Malnutrition is a key determinant of quality of life (QoL) in patients with head and neck cancers (HNCs), influencing treatment outcomes and the occurrence of adverse events (AEs). Despite there being numerous studies on nutritional status and QoL, there is no standardized risk or prognostic model integrating clinical and demographic factors.Methods:A literature search was conducted in September 2024 in Scopus, PubMed, and Web of Science, covering studies published between 2013 and 2024. Articles were selected based on their relevance to AEs, nutritional interventions, and QoL assessments in HNC patients.Results: The key factors influencing QoL in HNC patients include age, sex, weight, BMI, educational level, and tumor features. Mucositis was identified as the most significant food intake-impairing AE, contributing to malnutrition and reduced QoL. Current QoL assessments rely on descriptive questionnaires, which lack personalization and predictive capabilities. Digital tools, including machine learning models and digital twins, offer potential solutions for risk prediction and personalized nutritional interventions.Conclusions: Despite significant research efforts, QoL assessment in HNC patients remains non-uniform, and risk models integrating nutritional status are lacking. A comprehensive, personalized approach is needed, leveraging digital tools to improve nutritional intervention strategies.
背景/目的：营养不良是头颈癌患者生活质量的关键决定因素，影响治疗结果和不良事件的发生。尽管已有大量关于营养状况与生活质量的研究，但目前尚缺乏整合临床与人口统计学因素的标准化风险或预后模型。 方法：于2024年9月在Scopus、PubMed和Web of Science数据库进行文献检索，涵盖2013年至2024年间发表的研究。根据文献与头颈癌患者不良事件、营养干预及生活质量评估的相关性进行筛选。 结果：影响头颈癌患者生活质量的关键因素包括年龄、性别、体重、身体质量指数、教育水平和肿瘤特征。黏膜炎被确定为最显著影响进食的不良事件，可导致营养不良和生活质量下降。当前生活质量评估主要依赖描述性问卷，缺乏个性化与预测能力。数字工具（包括机器学习模型和数字孪生技术）为风险预测和个性化营养干预提供了潜在解决方案。 结论：尽管已有大量研究，头颈癌患者的生活质量评估仍缺乏统一标准，且缺少整合营养状况的风险模型。未来需要采用综合性的个性化策略，借助数字工具优化营养干预方案。
肿瘤(癌症)患者之家