整合纵向多模态数据，提升癌症动态监测与精准治疗能力
Advancing Precision Oncology Through Modeling of Longitudinal and Multimodal Data
- 融合时间序列与多源数据，捕捉癌症演化全过程
- 实现疾病进展与治疗反应的动态追踪，支持及时干预
- 适合肿瘤精准医疗研究者及临床决策支持系统开发者
癌症在遗传、表观遗传、微环境和表型变化的复杂相互作用下持续演化，导致细胞无序增殖、转移、免疫逃逸和耐药性，给监测与治疗带来挑战。当前数据驱动的癌症研究多基于单一时间点、单模态数据，难以全面刻画疾病的动态异质性。随着多尺度数据采集与计算方法的进步，纵向多模态生物标志物的发现成为可能。纵向数据揭示了单次采样无法体现的疾病进展与治疗响应模式，支持异常早期检测与动态调整治疗策略；多模态数据整合则从不同来源提供互补信息，提升风险评估精度与治疗靶向性。本文综述纵向与多模态建模方法，强调其协同作用在提供个性化诊疗多维度洞察方面的潜力，并总结当前挑战与未来方向。
原文摘要 · Abstract (English)
Cancer evolves continuously over time through a complex interplay of genetic, epigenetic, microenvironmental, and phenotypic changes. This dynamic behavior drives uncontrolled cell growth, metastasis, immune evasion, and therapy resistance, posing challenges for effective monitoring and treatment. However, today's data-driven research in oncology has primarily focused on cross-sectional analysis using data from a single modality, limiting the ability to fully characterize and interpret the disease's dynamic heterogeneity. Advances in multiscale data collection and computational methods now enable the discovery of longitudinal multimodal biomarkers for precision oncology. Longitudinal data reveal patterns of disease progression and treatment response that are not evident from single-timepoint data, enabling timely abnormality detection and dynamic treatment adaptation. Multimodal data integration offers complementary information from diverse sources for more precise risk assessment and targeting of cancer therapy. In this review, we survey methods of longitudinal and multimodal modeling, highlighting their synergy in providing multifaceted insights for personalized care tailored to the unique characteristics of a patient's cancer. We summarize the current challenges and future directions of longitudinal multimodal analysis in advancing precision oncology.
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