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弥散张量成像(DTI)在脑转移瘤和类似肿瘤中的生物标志物改变:DTI 和轨迹成像研究的系统评价。

Diffusion Tensor Imaging (DTI) Biomarker Alterations in Brain Metastases and Comparable Tumors: A Systematic Review of DTI and Tractography Findings.

机构信息

Department of Neurology, Neuromuscular Research Center, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran; Department of Neuroscience and Addiction Studies, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran.

Department of Neuroscience and Addiction Studies, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran.

出版信息

World Neurosurg. 2024 Oct;190:113-129. doi: 10.1016/j.wneu.2024.07.037. Epub 2024 Jul 8.

Abstract

BACKGROUND

Brain metastases (BMs) are the most frequent tumors of the central nervous system. Diffusion tensor imaging (DTI) is a magnetic resonance imaging technique that provides insights into brain microstructural alterations and tensor metrics and generates tractography to visualize white matter fiber tracts based on diffusion directionality. This systematic review assessed evidence from DTI biomarker alterations in BMs and comparable tumors such as glioblastoma.

METHODS

PubMed, Scopus, and Web of Science were searched, and published between January 2000 and August 2023. The key inclusion criteria were studies reporting DTI metrics in BMs and comparisons with other tumors. Data on study characteristics, tumor types, sample details, and main DTI findings were extracted.

RESULTS

Fifty-seven studies with 1592 BM patients and 1578 comparable brain tumors were included. Peritumoral fractional anisotropy (FA) consistently differentiates BMs from primary brain tumors, whereas intratumoral FA shows limited discriminatory power. Mean diffusivity increased in BMs versus comparators. Intratumoral metrics were less consistent but revealed differences in BM origin. Axial and radial diffusivity have provided insights into the effects of radiation, tumor origin, and infiltration. Axial diffusivity/radial diffusivity differentiated tumor infiltration from vasogenic edema. Tractography revealed anatomical relationships between white matter tracts and BMs. In addition, tractography-guided BM surgery and radiotherapy planning are required. Machine learning models incorporating DTI biomarkers/metrics accurately classified BMs versus comparators and improved diagnostic classification.

CONCLUSIONS

DTI metrics provide noninvasive biomarkers for distinguishing BMs from other tumors and predicting outcomes. Key metrics included peritumoral FA and mean diffusivity.

摘要

背景

脑转移瘤(BMs)是中枢神经系统最常见的肿瘤。弥散张量成像(DTI)是一种磁共振成像技术,可深入了解脑微观结构的改变和张量指标,并生成示踪术,以根据扩散方向性可视化白质纤维束。本系统评价评估了 DTI 生物标志物在 BMs 及类似肿瘤(如胶质母细胞瘤)中的改变的证据。

方法

检索了 PubMed、Scopus 和 Web of Science,并于 2000 年 1 月至 2023 年 8 月发表。主要纳入标准为报告 BMs 中 DTI 指标并与其他肿瘤进行比较的研究。提取了研究特征、肿瘤类型、样本细节和主要 DTI 发现的数据。

结果

共纳入 57 项研究,涉及 1592 例 BMs 患者和 1578 例可比脑肿瘤患者。瘤周部分各向异性分数(FA)可一致地区分 BMs 与原发性脑肿瘤,而瘤内 FA 的鉴别能力有限。与对照相比,BMs 中的平均弥散度增加。瘤内指标不太一致,但显示了 BM 起源的差异。轴突和径向弥散度为辐射、肿瘤起源和浸润的影响提供了见解。轴突弥散度/径向弥散度区分了肿瘤浸润与血管源性水肿。示踪术揭示了白质束与 BMs 之间的解剖关系。此外,需要进行基于示踪术的 BM 手术和放疗计划。纳入 DTI 生物标志物/指标的机器学习模型可以准确地将 BMs 与对照区分开来,并提高诊断分类。

结论

DTI 指标为区分 BMs 与其他肿瘤以及预测结局提供了非侵入性的生物标志物。关键指标包括瘤周 FA 和平均弥散度。

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