中国现代神经疾病杂志 ›› 2017, Vol. 17 ›› Issue (1): 69-73. doi: 10.3969/j.issn.1672-6731.2017.01.013

• 综述 • 上一篇    下一篇

2 胶质瘤分级及分子遗传学标志物相关磁共振成像研究进展

张姗姗, 于林   

  1. 300052 天津医科大学总医院医学影像科(张姗姗);300070 天津医科大学基础医学院生物化学与分子生物学系(于林)
  • 出版日期:2017-01-25 发布日期:2017-01-22
  • 通讯作者: 于林(Email:onoblivion@tijmu.edu.cn)
  • 基金资助:

    国家自然科学基金青年科学基金资助项目(项目编号:81202102);国家自然科学基金资助项目(项目编号:81672592);天津市应用基础及前沿技术研究计划项目( 项目编号:13JCQNJC12100);天津市应用基础及前沿技术研究计划项目(项目编号:15JCZDJC34600)

Research progress of MRI in glioma grading and molecular genetic biomarkers

ZHANG Shan-shan1, YU Lin2   

  1. 1Department of Medical Image, Tianjin Medical University General Hospital, Tianjin 300052, China
    2Department of Biochemistry and Molecular Biology, School of Basic Medical Science, Tianjin Medical University, Tianjin 300070, China
  • Online:2017-01-25 Published:2017-01-22
  • Contact: YU Lin (Email: onoblivion@tijmu.edu.cn)
  • Supported by:

    This study was supported by the National Natural Science Foundation of China for Young Scientists (No. 81202102), the National Natural Science Foundation of China (No. 81672592), and Project of Applicative Basic Research and Advanced Technology of Tianjin Municipal Science and Technology Commission (No. 13JCQNJC12100, 15JCZDJC34600).

摘要:

近年来胶质瘤病理学和影像学诊断均有显著进展。胶质瘤分级和分子遗传学标志物既是重要的预后预测因素,又可以指导治疗策略的制定。本文主要介绍应用扩散加权成像、扩散张量成像、扩散峰度成像、动态对比增强磁共振成像、灌注成像和磁共振波谱等新型MRI技术进行胶质瘤分级和分子遗传学标志物检测方面的新进展。分子遗传学标志物联合上述新型MRI技术可以更精确地对胶质瘤进行诊断和分级,并无创性检测胶质瘤分子特征,从而提高对患者预后评价的准确性,更好地指导个体化治疗。

关键词: 神经胶质瘤, 生物学标记, 磁共振成像, 综述

Abstract:

The pathological and imaging diagnosis of glioma has significantly evolved in recent years. Glioma grading, together with a number of molecular genetic biomarkers, has been recognized as an important prognostic and predictive factor, which can also guide the treatment strategy of glioma. This article highlights the research progress of MRI for noninvasively grading and molecular characterization of gliomas, including diffusion-weighted imaging (DWI), diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), dynamic contrast-enhanced MRI (DCE-MRI), perfusion-weighted imaging (PWI) and magnetic resonance spectroscopy (MRS). The multiparametric imaging data analysis could improve imaging diagnosis, introduce the potential to noninvasively detect underlying molecular features of glioma, finally improve the accuracy of prognosis prediction and guide the individual-based treatment for glioma patients.

Key words: Glioma, Biological markers, Magnetic resonance imaging, Review