Background: Electronic patient-reported outcomes (ePROs) enable real-time symptom monitoring and early intervention in oncology. Large language models (LLMs), when combined with retrieval-augmented generation (RAG), offer scalable Artificial Intelligence (AI)-driven education tailored to individual patient needs. However, few studies have examined the feasibility and clinical impact of integrating ePRO with LLM-RAG feedback during radiotherapy in high-toxicity settings such as head and neck cancer. Methods: This prospective observational study enrolled 42 patients with head and neck cancer undergoing radiotherapy from January to December 2024. Patients completed ePRO entries twice weekly using a web-based platform. Following each entry, an LLM-RAG system (Gemini 1.5-based) generated real-time educational feedback using National Comprehensive Cancer Network (NCCN) guidelines and institutional resources. Primary outcomes included percentage weight loss and treatment interruption days. Statistical analyses includedt-tests, linear regression, and receiver operating characteristic (ROC) analysis. A threshold of ≥6 ePRO entries was used for subgroup analysis. Results: Patients had a mean age of 53.6 years and submitted an average of 8.0 ePRO entries. Frequent ePRO users (≥6 entries) had significantly less weight loss (4.45% vs. 7.57%,p= 0.021) and fewer treatment interruptions (0.67 vs. 2.50 days,p= 0.002). Chemotherapy, moderate-to-severe pain, and lower ePRO submission frequency were associated with greater weight loss. ePRO submission frequency was negatively correlated with both weight loss and treatment interruption days. The most commonly reported symptoms were appetite loss, fatigue, and nausea. Conclusions: Integrating LLM-RAG feedback with ePRO systems is feasible and may enhance symptom control, treatment continuity, and patient engagement in head and neck cancer radiotherapy. Further studies are warranted to validate the clinical benefits of AI-supported ePRO platforms in routine care.
背景：电子患者报告结局（ePRO）可实现肿瘤治疗中的实时症状监测与早期干预。大型语言模型（LLM）结合检索增强生成（RAG）技术，能够提供可规模化、基于人工智能（AI）的个体化患者教育方案。然而，在头颈癌等高毒性放疗场景中，整合ePRO与LLM-RAG反馈系统的可行性及临床影响尚缺乏研究。方法：本前瞻性观察研究于2024年1月至12月纳入42例接受放疗的头颈癌患者。患者通过网络平台每周完成两次ePRO记录。每次记录后，基于Gemini 1.5架构的LLM-RAG系统将依据美国国家综合癌症网络（NCCN）指南及机构资源生成实时教育反馈。主要结局指标包括体重下降百分比和治疗中断天数。统计分析采用t检验、线性回归及受试者工作特征（ROC）分析，并以ePRO提交次数≥6次作为亚组分析阈值。结果：患者平均年龄53.6岁，平均提交8.0次ePRO记录。高频使用ePRO（≥6次）的患者体重下降显著更少（4.45% vs. 7.57%，p=0.021），治疗中断天数更短（0.67天 vs. 2.50天，p=0.002）。化疗、中重度疼痛及较低的ePRO提交频率与更显著的体重下降相关。ePRO提交频率与体重下降及治疗中断天数均呈负相关。最常见报告症状为食欲减退、疲劳和恶心。结论：将LLM-RAG反馈系统整合至ePRO平台具有可行性，可能增强头颈癌放疗期间的症状控制、治疗连续性和患者参与度。未来需进一步研究验证AI支持的ePRO平台在常规护理中的临床效益。
肿瘤(癌症)患者之家