侧脑室脑膜瘤于中枢神经细胞瘤的CT密度和MR信号相似,如何鉴别?

2021-08-03 MedSci原创 MedSci原创

侧脑室脑膜瘤于中枢神经细胞瘤的CT密度和MR信号相似,如何鉴别?

《梅斯直播课:颅脑病变读片入门和提高》

回答问题3:侧脑室脑膜瘤于中枢神经细胞瘤的CT密度和MR信号相似,如何鉴别?

 

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    2022-06-03 kcb069
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    2021-08-05 jiangfeng5067
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    2021-08-05 spoonycyy

    #细胞瘤#

    0

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    2021-08-05 liye789132254
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    2021-08-05 smlt2015
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    2021-08-05 tastas

    #脑室#

    0

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颅内病变的定性诊断是选择治疗的先决条件,而定性诊断主要依靠病理学诊断,病理学诊断也是最直接、最终的诊断结果。尽管先进的影像学技术对颅内病变的诊断具有很重要的意义,但仍有部分病变因其影像表现不典型,无法通过影像特点做出临床诊断,必须经过活检或手术,方能做出病理学诊断。

“气泡状表现”的脑室内占位常见哪些疾病?

“气泡状表现”的脑室内占位常见哪些疾病?

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欧洲神经肿瘤学协会EANO指南:脑膜瘤分子检测用于靶向治疗选择

本评估结果可为临床实践和临床试验中靶向治疗的选择提供指导,并指出需要进一步研究的领域。

BMJ:含孕激素去氧孕烯或左炔诺孕酮的口服避孕药与颅内脑膜瘤风险:全国病例对照研究

本研究为法国全国性病例对照研究,首次明确发现使用含75微克剂量的孕激素“地索孕酮”(desogestrel)口服避孕药超过五年,与颅内脑膜瘤手术风险轻度增加相关,长时间连续使用风险更高。

European Radiology:深度学习放射组图在区分颅内孤立性纤维性肿瘤和血管瘤性脑膜瘤方面的价值

放射组学(Radiomics)是一种从标准医疗成像中高通量挖掘定量图像特征的技术,可以提取数据并在临床决策支持系统中应用,以提高诊断、预后和预测的准确性,在癌症研究中越来越重要。

European Radiology:基于多参数MRI的机器学习模型预测脑膜瘤患者WHO分级

放射组学利用强大的计算机图像处理能力和多种大数据挖掘方法提取多维特征,尽管这些特征很难从视觉上获得,但保留了与研究相关的信息,如数字加密医学图像(CT、MRI和PET)中的一阶、形状、纹理特征。

European Radiology:通过深度学习预测脑膜瘤分级和病理标记物表达

基于深度学习 (DL) 的放射组学是一种新开发的从各种放射图像中提取大量特征的方法。DL算法总是利用卷积神经网络 (CNN) 直接从成像数据中挖掘复杂的视觉信息特征,从医学图像中深度提取特征。

European Radiology:半自动化分割方法在的难治性脑膜瘤患者放疗中的应用

对于弥漫性脑膜瘤或侵袭性脑膜瘤来说,治疗通常是基于手术和/或放射治疗。然而,一些脑膜瘤病例受限于肿瘤位置或重复手术或全脑照射的风险,因此无法接受这类治疗。