Advancing Precision Oncology Through Modeling of Longitudinal and Multimodal Data.

IF 12 1区工程技术 Q1 ENGINEERING, BIOMEDICAL

IEEE Reviews in Biomedical Engineering Pub Date : 2025-07-03 DOI:10.1109/RBME.2025.3577587

Luoting Zhuang, Stephen H Park, Steven J Skates, Ashley E Prosper, Denise R Aberle, William Hsu

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引用次数: 0

Abstract

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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通过纵向和多模态数据建模推进精准肿瘤学。

随着时间的推移，癌症通过遗传、表观遗传、微环境和表型变化的复杂相互作用不断演变。这种动态行为驱动不受控制的细胞生长、转移、免疫逃避和治疗抵抗，为有效监测和治疗带来挑战。然而，今天的肿瘤学数据驱动研究主要集中在使用单一模式数据的横断面分析，限制了充分表征和解释疾病动态异质性的能力。多尺度数据收集和计算方法的进步现在使精确肿瘤学的纵向多模态生物标志物的发现成为可能。纵向数据揭示了单时间点数据不明显的疾病进展和治疗反应模式，从而能够及时发现异常并动态适应治疗。多模式数据集成提供了来自不同来源的补充信息，以便更精确地进行风险评估和靶向癌症治疗。在这篇综述中，我们调查了纵向和多模态建模的方法，强调了它们在为针对患者癌症的独特特征量身定制个性化护理提供多方面见解方面的协同作用。我们总结了目前的挑战和未来的方向纵向多模态分析在推进精准肿瘤学。

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来源期刊

IEEE Reviews in Biomedical Engineering Engineering-Biomedical Engineering

CiteScore

31.70

自引率

0.60%

发文量

期刊介绍： IEEE Reviews in Biomedical Engineering (RBME) serves as a platform to review the state-of-the-art and trends in the interdisciplinary field of biomedical engineering, which encompasses engineering, life sciences, and medicine. The journal aims to consolidate research and reviews for members of all IEEE societies interested in biomedical engineering. Recognizing the demand for comprehensive reviews among authors of various IEEE journals, RBME addresses this need by receiving, reviewing, and publishing scholarly works under one umbrella. It covers a broad spectrum, from historical to modern developments in biomedical engineering and the integration of technologies from various IEEE societies into the life sciences and medicine.