人工智能、多组学与多模态整合在头颈癌精准放疗中的应用：从生物学洞见到治疗决策

文献信息

中文摘要

头颈癌放疗必须在疾病控制与正常组织损伤之间取得平衡。人工智能（AI）、多组学和多模态分析为刻画肿瘤与宿主异质性提供了互补途径，但其与治疗决策的相关性取决于测量、结局和验证之间如何建立联系。本叙述性综述考察这些方法在头颈癌中的应用，证据集中于头颈部鳞状细胞癌和鼻咽癌。文献通过Web of Science和PubMed检索，并结合针对性补充检索和参考文献列表筛选而确定，并根据与放疗生物学、临床结局以及方法学或临床验证的相关性进行选择。我们区分联合分子或多模态输入与平行分析及事后生物学注释，并依据人群、治疗设置、采样时间和分析单位评估研究结果。基因组学、蛋白质基因组学、单细胞和空间研究识别出涉及脱氧核糖核酸（DNA）修复、病毒调控、免疫和代谢的候选表型。临床研究考察缓解、复发和生存分层、分子监测以及毒性预测。然而，在观察到的治疗下与结局的关联并不能确立差异性治疗获益，判别能力的提高也不能证明模型指导的诊疗可改善患者结局。患者队列较小、检测可及性不完整、潜在数据泄漏以及独立验证有限，进一步限制了结果解读。转化需要可重复的测量、对完整分析流程的患者层面验证、校准的风险估计以及在预期治疗背景下的评估。前瞻性评估应优先采用预先定义的模型指导策略，并同时评估疾病控制、患者相关危害和实际实施情况。

英文摘要

Radiotherapy for head and neck cancer must balance disease control against normal-tissue injury. Artificial intelligence (AI), multi-omics, and multimodal analysis offer complementary ways to characterize tumor and host heterogeneity, but their relevance to treatment decisions depends on how measurements, outcomes, and validation are linked. This narrative review examines these approaches across head and neck cancers, with evidence concentrated in head and neck squamous cell carcinoma and nasopharyngeal carcinoma. Literature was identified through Web of Science and PubMed searches with targeted supplementary searches and reference-list screening, and selected for relevance to radiotherapy biology, clinical outcomes, and methodological or clinical validation. We distinguish joint molecular or multimodal inputs from parallel analyses and post hoc biological annotation, and assess findings according to population, treatment setting, sampling time, and analytical unit. Genomic, proteogenomic, single-cell, and spatial studies identify candidate phenotypes involving deoxyribonucleic acid (DNA) repair, viral regulation, immunity, and metabolism. Clinical studies examine response, recurrence and survival stratification, molecular surveillance, and toxicity prediction. However, associations with outcomes under observed treatment do not establish differential treatment benefit, and improved discrimination does not demonstrate that model-guided care improves patient outcomes. Small patient cohorts, incomplete assay availability, potential data leakage, and limited independent validation further constrain interpretation. Translation requires reproducible measurements, patient-level validation of complete analytical pipelines, calibrated risk estimates, and evaluation within the intended treatment context. Prospective evaluation should prioritize predefined model-guided strategies and assess disease control, patient-relevant harm, and practical implementation together.