磁共振扩散加权成像鉴别颅内孤立性纤维瘤和不同级别脑膜瘤的研究进展
Advances in differentiating intracranial isolated fibromas from different grades of meningiomas based on diffusion-weighted imaging
魏令珍 1曾庆师 2陈金明 3李美霖 2刘家皓 2王怀臻4
作者信息
- 1. 济宁医学院临床医学院,济宁 272067;山东第一医科大学第一附属医院(山东省千佛山医院)放射科,济南 250014
- 2. 山东第一医科大学第一附属医院(山东省千佛山医院)放射科,济南 250014
- 3. 山东第一医科大学第一附属医院(山东省千佛山医院)放射科,济南 250014;山东大学齐鲁医学院,济南 250012
- 4. 山东中医药大学第一临床医学院,济南 250011
- 折叠
摘要
颅内孤立性纤维瘤(solitary fibrous tumor,SFT)具有易复发和手术中出血概率高的特点,必须积极地进行周密的术前与术中准备及系统的术后治疗,而与之影像学表现相似的脑膜瘤多为良性,术中不易出血且预后较好,因此手术或治疗前准确区分SFT和脑膜瘤至关重要.本文系统综述了磁共振扩散加权成像在区分颅内SFT和高级别脑膜瘤、低级别脑膜瘤以及血管瘤型脑膜瘤中的研究进展以及病理生理机制,深入探讨该序列在提高鉴别诊断准确性等方面的优势和价值,为后续研究提供参考思路,并在术前评估及术中决策方面为临床医生提供有效的帮助,以期提高患者的预后和生活质量.
Abstract
Intracranial solitary fibrous tumor (SFT) has the characteristics of easy recurrence and high probability of intraoperative hemorrhage. It must be actively carried out preoperative and intraoperative preparations and systematic postoperative treatment. In contrast,meningiomas,which are similar in imaging manifestations with SFTs,are mostly benign,less prone to intraoperative hemorrhage,and have a better prognosis. So,it is crucial to differentiate between SFTs and meningiomas before surgery or treatment accurately. Therefore,accurate differentiation between SFTs and meningiomas before surgery or treatment is crucial. This article aims to systematically review the research progress and pathophysiological mechanisms of magnetic resonance diffusion-weighted imaging in distinguishing intracranial SFT from high-grade meningiomas,low-grade meningiomas,and angiomatous meningiomas. It thoroughly explores the advantages and value of this sequence in improving diagnostic accuracy,providing reference ideas for subsequent studies. Additionally,it aims to offer effective assistance to clinicians in preoperative evaluation and intraoperative decision-making,with the goal of improving patient prognosis and quality of life.
关键词
颅内孤立性纤维瘤/脑膜瘤/扩散加权成像/磁共振成像/鉴别诊断Key words
intracranial solitary fibrous tumor/meningiomas/diffusion weighted imaging/magnetic resonance imaging/differential diagnosis引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据