多模态影像组学在创伤性脑损伤中的应用进展

Advances in the application of multimodal imaging omics in traumatic brain injury

摘要:

创伤性脑损伤（TBI）作为全球范围内的重要公共卫生问题，具有高发病率、高致残率和高致死率的特点，其病理机制复杂且异质性强，给临床精准诊疗带来巨大挑战。近年来，多模态影像组学（MIO）作为医学影像学与高通量数据分析交叉融合的新兴领域，通过整合结构磁共振成像（sMRI）、功能磁共振成像（fMRI）、正电子发射断层扫描（PET）及计算机体层成像（CT）等多源数据，结合机器学习（ML）及深度学习（DL）算法，为解析TBI病理生理机制、实现个体化精准诊疗提供了全新视角。该文系统综述了MIO在TBI研究中的最新进展，重点阐述了不同影像模态的组学特征提取方法、多模态数据融合策略及其在TBI分型、疗效评估和预后预测中的临床应用价值。此外，该文详细总结了当前研究中存在的数据异质性高、模型泛化能力弱及临床转化难等关键问题，并探讨了联邦学习（FL）和可解释人工智能（XAI）在未来TBI研究中的应用前景。通过梳理现有成果，该文旨在为TBI的精准医学研究提供理论依据与技术参考。

Abstract:

Traumatic brain injury （TBI） is a major global public health issue characterized by high incidence， disability， and mortality rates， and its complex pathological mechanisms and significant heterogeneity have brought great challenges to precise clinical diagnosis and treatment. In recent years， as an emerging interdisciplinary field integrating medical imaging with high-throughput data analysis， multimodal imaging omics （MIO） has provided a novel perspective for deciphering the pathophysiological mechanisms of TBI and realizing individualized precise diagnosis and treatment by integrating multi-source data （including structural magnetic resonance imaging， functional magnetic resonance imaging， positron emission tomography， and computed tomography） and leveraging machine learning and deep learning algorithms. This article systematically reviews the latest advances in the application of MIO in TBI research， with a focus on radiomic feature extraction methods across different imaging modalities， multimodal data fusion strategies， and their clinical application value in TBI subtyping， efficacy assessment， and prognosis prediction. Furthermore， this article summarizes the key challenges in current research， such as high data heterogeneity， weak model generalization， and difficulties in clinical translation， and discusses the application prospect of federated learning and explainable artificial intelligence in future TBI studies. By reviewing the existing research findings， this article aims to provide theoretical foundations and technical references for precision medicine research on TBI.