头颈癌中的数字孪生范式：临床机遇与挑战

文献信息

中文摘要

头颈部鳞状细胞癌（HNSCC）是一项重大的公共卫生挑战。其治疗具有高度的解剖复杂性，并且迫切需要保留言语、吞咽和呼吸功能。尽管包括手术、放疗和全身治疗在内的多模式治疗已显著提高患者生存率，但仍存在较高的改变生活的毒性发生率和较低的免疫治疗应答率。当前的临床决策在很大程度上依赖于“数字快照”——即源自人群统计和时间点特异性影像的静态表征——这些表征通常无法反映治疗过程中快速的解剖和生物学变化。数字孪生通过创建整合实时生物学、解剖学和临床数据的高保真虚拟患者表征，在加速科学突破以及转变肿瘤治疗和精准医学方面具有巨大前景。在本观点文章中，我们提出3个相互关联的数字孪生，以建立HNSCC数字孪生的概念框架。这些数字孪生包括：解剖孪生，利用虚拟手术规划和增强现实实现几何精度；剂量学孪生，利用每日影像和合成计算机断层扫描生成实现自动化自适应放疗；以及生物学孪生，整合深度表型分析、放射组学和机制性组学以模拟个体化治疗轨迹。除急性期照护外，我们还探讨数字孪生在应对人乳头瘤病毒相关疾病的“降阶梯困境”以及通过数字表型分析改善生存者健康方面的临床效用。随着来自医疗级物联网传感器的纵向数据和循环肿瘤DNA动力学的出现，数字孪生可作为主动哨兵，在临床表现出现前数月检测功能衰退或亚临床事件。此外，通过虚拟人群实施合成对照臂为加速临床试验和解决罕见分子亚型中的伦理问题提供了途径。尽管具有这一潜力，实施仍存在重大障碍，包括治疗过程中显著的解剖不稳定性、深度学习算法的“黑箱”性质以及多尺度数据整合的挑战。我们为开发“变形”数字孪生——能够进行连续几何和生物学审计的动态系统——提供了技术路线图。通过弥合放射物理学的宏观世界与细胞演化的微观世界之间的差距，数字孪生可能有助于将HNSCC的管理从基于人群的统计概率转向更个体化、基于生物学信息的决策，目标是在保留患者基本功能的同时优化HNSCC管理。

英文摘要

Head and neck squamous cell carcinoma (HNSCC) represents a significant public health challenge. Its treatment is characterized by high anatomical complexity and a critical need for functional preservation of speech, swallowing, and respiration. While multimodality care, including surgery, radiotherapy, and systemic therapy, has substantially improved patient survival, there are still high rates of life-altering toxicities and low response rates to immunotherapy. Current clinical decision-making largely relies on "digital snapshots"-static representations derived from population-based statistics and time point-specific imaging-which usually fail to account for the rapid anatomical and biological change during the course of treatment. Digital twins hold great promise in accelerating scientific breakthroughs and transforming oncology treatment and precision medicine through the creation of high-fidelity virtual patient representations that integrate real-time biological, anatomical, and clinical data. In this viewpoint, we propose 3 interconnected digital twins to establish a conceptual framework for the HNSCC digital twin. These digital twins include the anatomical twin, using virtual surgical planning and augmented reality for geometric precision; the dosimetric twin, using daily imaging and synthetic computed tomography generation for automated adaptive radiotherapy; and the biological twin, integrating deep phenotyping, radiomics, and mechanistic omics to simulate individualized therapeutic trajectories. Beyond acute care, we explore the clinical utility of digital twins in navigating the "de-escalation dilemma" for human papillomavirus-associated disease and improving survivorship through digital phenotyping. With the advent of longitudinal data from medical-grade Internet-of-Things sensors and circulating tumor DNA kinetics, digital twins can serve as active sentinels, detecting functional decline or subclinical events months before clinical manifestation. Furthermore, the implementation of synthetic control arms via virtual populations offers an avenue to accelerate clinical trials and address ethical issues in rare molecular subtypes. Despite this potential, substantial barriers to implementation remain, including profound anatomical instability during treatment, the "black box" nature of deep learning algorithms, and the challenges of multiscale data integration. We provide a technical roadmap for the development of "morphing" digital twins-dynamic systems capable of continuous geometric and biological auditing. By bridging the gap between the macroscopic world of radiation physics and the microscopic world of cellular evolution, digital twins may help shift the management of HNSCC from population-based statistical probability toward more individualized, biologically informed decision-making, with the goal of optimizing HNSCC management while preserving essential patient functions.