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 Radiomics in Traumatic Brain Injury

摘要:

创伤性脑损伤（Traumatic Brain Injury, 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（Multimodal Imaging Omics）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（Structural Magnetic Resonance Imaging, sMRI）、功能磁共振成像（functional Magnetic Resonance Imaging, fMRI）、正电子发射断层扫描（Positron Emission Tomography, PET）及计算机断层扫描（Computed Tomography, CT）等多源数据，结合机器学习（Machine Learning, ML）及深度学习（Deep Learning, 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。本文系统综述了多模态影像组学在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，本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（Federated Learning, FL）和可解释人工智能（Explainable Artificial Intelligence, XAI）在未来TBI研究中的应用前景。通过梳理现有成果，本文旨在为TBI的精准医学研究提供理论依据与技术参考。

Abstract:

As an important public health problem around the world, traumatic brain injury has the characteristics of high incidence, high disability rate and high mortality rate. Its pathological mechanism is complex and highly heterogeneous, posing huge challenges to clinical precise diagnosis and treatment. In recent years, multimodal radiology, as an emerging field of cross-integration of medical imaging and high-throughput data analysis, integrates multi-source data such as structural magnetic resonance imaging, functional magnetic resonance imaging, positron emission tomography, and computed tomography. Combined with machine learning and deep learning algorithms, it provides a new perspective for analyzing the pathophysiological mechanisms of TBI and achieving personalized and precise diagnosis and treatment. This article systematically reviews the latest progress of multimodal radiology in TBI research, focusing on the genomic feature extraction methods of different imaging modalities, multimodal data fusion strategies and their clinical application value in TBI typing, efficacy evaluation and prognosis prediction. In addition, this paper summarizes in detail the key issues in current research such as high data heterogeneity, weak model generalization ability, and difficulty in clinical transformation, and discusses the application prospects of federated learning and interpretable artificial intelligence in future TBI research. By sorting out existing results, this paper aims to provide theoretical basis and technical reference for TBI''s precision medicine research.