• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

基于MRI标准鉴别多灶性中枢神经系统淋巴瘤和胶质母细胞瘤。

Differentiation between multifocal CNS lymphoma and glioblastoma based on MRI criteria.

作者信息

Müller Sebastian Johannes, Khadhraoui Eya, Henkes Hans, Ernst Marielle, Rohde Veit, Schatlo Bawarjan, Malinova Vesna

机构信息

Institute of Neuroradiology, University Medical Center, Göttingen, Germany.

Clinic for Neuroradiology, Katharinen-Hospital Stuttgart, Stuttgart, Germany.

出版信息

Discov Oncol. 2024 Sep 1;15(1):397. doi: 10.1007/s12672-024-01266-9.

DOI:10.1007/s12672-024-01266-9
PMID:39217585
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11366735/
Abstract

PURPOSE

Differentiating between glioblastoma (GB) with multiple foci (mGB) and multifocal central nervous system lymphoma (mCNSL) can be challenging because these cancers share several features at first appearance on magnetic resonance imaging (MRI). The aim of this study was to explore morphological differences in MRI findings for mGB versus mCNSL and to develop an interpretation algorithm with high diagnostic accuracy.

METHODS

In this retrospective study, MRI characteristics were compared between 50 patients with mGB and 50 patients with mCNSL treated between 2015 and 2020. The following parameters were evaluated: size, morphology, lesion location and distribution, connections between the lesions on the fluid-attenuated inversion recovery sequence, patterns of contrast enhancement, and apparent diffusion coefficient (ADC) values within the tumor and the surrounding edema, as well as MR perfusion and susceptibility weighted imaging (SWI) whenever available.

RESULTS

A total of 187 mCNSL lesions and 181 mGB lesions were analyzed. The mCNSL lesions demonstrated frequently a solid morphology compared to mGB lesions, which showed more often a cystic, mixed cystic/solid morphology and a cortical infiltration. The mean measured diameter was significantly smaller for mCNSL than mGB lesions (p < 0.001). Tumor ADC ratios were significantly smaller in mCNSL than in mGB (0.89 ± 0.36 vs. 1.05 ± 0.35, p < 0.001). The ADC ratio of perilesional edema was significantly higher (p < 0.001) in mCNSL than in mGB. In SWI / T2*-weighted imaging, tumor-associated susceptibility artifacts were more often found in mCNSL than in mGB (p < 0.001).

CONCLUSION

The lesion size, ADC ratios of the lesions and the adjacent tissue as well as the vascularization of the lesions in the MR-perfusion were found to be significant distinctive patterns of mCNSL and mGB allowing a radiological differentiation of these two entities on initial MRI. A diagnostic algorithm based on these parameters merits a prospective validation.

摘要

目的

鉴别具有多个病灶的胶质母细胞瘤(GB)与多灶性中枢神经系统淋巴瘤(mCNSL)具有挑战性,因为这些癌症在磁共振成像(MRI)初次表现时具有若干共同特征。本研究的目的是探讨mGB与mCNSL在MRI表现上的形态学差异,并开发一种具有高诊断准确性的解读算法。

方法

在这项回顾性研究中,比较了2015年至2020年间接受治疗的50例mGB患者和50例mCNSL患者的MRI特征。评估了以下参数:大小、形态、病变位置和分布、液体衰减反转恢复序列上病变之间的连接、对比增强模式、肿瘤及周围水肿内的表观扩散系数(ADC)值,以及在可获得时的MR灌注和磁敏感加权成像(SWI)。

结果

共分析了187个mCNSL病灶和181个mGB病灶。与mGB病灶相比,mCNSL病灶常表现为实性形态,mGB病灶更常表现为囊性、囊实性混合形态及皮质浸润。mCNSL的平均测量直径显著小于mGB病灶(p < 0.001)。mCNSL的肿瘤ADC比值显著低于mGB(0.89 ± 0.36 vs. 1.05 ±  0.35,p < 0.001)。mCNSL的瘤周水肿ADC比值显著高于mGB(p < 0.001)。在SWI/T2*加权成像中,mCNSL比mGB更常发现肿瘤相关的磁敏感伪影(p < 0.001)。

结论

发现病变大小、病变及相邻组织的ADC比值以及MR灌注中病变的血管化是mCNSL和mGB的显著不同模式,可在初次MRI上对这两种实体进行影像学鉴别。基于这些参数的诊断算法值得进行前瞻性验证。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc00/11366735/8fe431172e47/12672_2024_1266_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc00/11366735/9f178e5175c5/12672_2024_1266_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc00/11366735/faac33b76003/12672_2024_1266_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc00/11366735/0e9a7603e95b/12672_2024_1266_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc00/11366735/8fe431172e47/12672_2024_1266_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc00/11366735/9f178e5175c5/12672_2024_1266_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc00/11366735/faac33b76003/12672_2024_1266_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc00/11366735/0e9a7603e95b/12672_2024_1266_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc00/11366735/8fe431172e47/12672_2024_1266_Fig4_HTML.jpg

相似文献

1
Differentiation between multifocal CNS lymphoma and glioblastoma based on MRI criteria.基于MRI标准鉴别多灶性中枢神经系统淋巴瘤和胶质母细胞瘤。
Discov Oncol. 2024 Sep 1;15(1):397. doi: 10.1007/s12672-024-01266-9.
2
Differentiation of multiple brain metastases and glioblastoma with multiple foci using MRI criteria.使用 MRI 标准区分多发性脑转移瘤和多灶性脑胶质母细胞瘤。
BMC Med Imaging. 2024 Jan 2;24(1):3. doi: 10.1186/s12880-023-01183-3.
3
Primary central nervous system lymphoma and atypical glioblastoma: multiparametric differentiation by using diffusion-, perfusion-, and susceptibility-weighted MR imaging.原发性中枢神经系统淋巴瘤和非典型性脑胶质瘤:弥散、灌注和磁化率加权 MR 成像的多参数鉴别诊断。
Radiology. 2014 Sep;272(3):843-50. doi: 10.1148/radiol.14132740. Epub 2014 May 3.
4
Multiparametric imaging-based differentiation of lymphoma and glioblastoma: using T1-perfusion, diffusion, and susceptibility-weighted MRI.基于多参数成像的淋巴瘤与胶质母细胞瘤鉴别:利用T1灌注、扩散及磁敏感加权磁共振成像
Clin Radiol. 2018 Nov;73(11):986.e7-986.e15. doi: 10.1016/j.crad.2018.07.107. Epub 2018 Sep 7.
5
Role of Multiparametric Magnetic Resonance Imaging of the Brain in Differentiating Neurocysticercosis From Tuberculoma.脑部多参数磁共振成像在鉴别神经囊尾蚴病和结核瘤中的作用
Cureus. 2023 May 14;15(5):e39003. doi: 10.7759/cureus.39003. eCollection 2023 May.
6
Lymphomas and glioblastomas: differences in the apparent diffusion coefficient evaluated with high b-value diffusion-weighted magnetic resonance imaging at 3T.淋巴瘤和胶质母细胞瘤:3T 高 b 值扩散加权磁共振成像评估表观扩散系数的差异。
Eur J Radiol. 2012 Feb;81(2):339-44. doi: 10.1016/j.ejrad.2010.11.005. Epub 2010 Dec 3.
7
Differentiating between Glioblastoma and Primary CNS Lymphoma Using Combined Whole-tumor Histogram Analysis of the Normalized Cerebral Blood Volume and the Apparent Diffusion Coefficient.利用归一化脑血容量和表观扩散系数的全肿瘤直方图分析鉴别胶质母细胞瘤和原发性中枢神经系统淋巴瘤。
Magn Reson Med Sci. 2019 Jan 10;18(1):53-61. doi: 10.2463/mrms.mp.2017-0135. Epub 2018 May 31.
8
Primary central nervous system lymphoma and atypical glioblastoma: Differentiation using radiomics approach.原发性中枢神经系统淋巴瘤和非典型性脑胶质瘤:基于放射组学方法的鉴别诊断。
Eur Radiol. 2018 Sep;28(9):3832-3839. doi: 10.1007/s00330-018-5368-4. Epub 2018 Apr 6.
9
Susceptibility-weighted imaging and diffusion-weighted imaging findings in central nervous system monomorphic B cell post-transplant lymphoproliferative disorder before and after treatment and comparison with primary B cell central nervous system lymphoma.中枢神经系统单形性B细胞移植后淋巴组织增生性疾病治疗前后的磁敏感加权成像和扩散加权成像表现及与原发性B细胞中枢神经系统淋巴瘤的比较
J Neurooncol. 2015 Nov;125(2):297-305. doi: 10.1007/s11060-015-1903-1. Epub 2015 Sep 4.
10
Role of intra-tumoral vasculature imaging features on susceptibility weighted imaging in differentiating primary central nervous system lymphoma from glioblastoma: a multiparametric comparison with pathological validation.基于磁敏感加权成像的肿瘤内血管成像特征在鉴别原发性中枢神经系统淋巴瘤与胶质母细胞瘤中的作用:与病理验证的多参数比较。
Neuroradiology. 2022 Sep;64(9):1801-1818. doi: 10.1007/s00234-022-02946-5. Epub 2022 Apr 18.

引用本文的文献

1
CNS Non-Hodgkin's Lymphoma With Atypical T2 Hyperintensity and Necrosis Mimicking Glioblastoma Multiforme: A Radiological Enigma.具有非典型T2高信号和坏死、酷似多形性胶质母细胞瘤的中枢神经系统非霍奇金淋巴瘤:一个影像学谜团。
Cureus. 2025 Aug 4;17(8):e89353. doi: 10.7759/cureus.89353. eCollection 2025 Aug.
2
Late enhancement and wash-out maps for differentiation of glioblastoma and metastases.用于鉴别胶质母细胞瘤和转移瘤的延迟强化及廓清图。
BMC Med Imaging. 2025 Aug 27;25(1):353. doi: 10.1186/s12880-025-01889-6.

本文引用的文献

1
Presurgical diagnosis of diffuse gliomas in adults: Post-WHO 2021 practical perspectives from radiologists in neuro-oncology units.成人弥漫性胶质瘤的术前诊断:神经肿瘤学单位放射科医生基于 2021 年世卫组织的实用观点。
Radiologia (Engl Ed). 2024 May-Jun;66(3):260-277. doi: 10.1016/j.rxeng.2024.03.002. Epub 2024 Mar 23.
2
An accessible deep learning tool for voxel-wise classification of brain malignancies from perfusion MRI.一种可用于从灌注 MRI 对脑恶性肿瘤进行体素分类的易于使用的深度学习工具。
Cell Rep Med. 2024 Mar 19;5(3):101464. doi: 10.1016/j.xcrm.2024.101464. Epub 2024 Mar 11.
3
Imaging of Lymphomas Involving the CNS: An Update-Review of the Full Spectrum of Disease with an Emphasis on the World Health Organization Classifications of CNS Tumors 2021 and Hematolymphoid Tumors 2022.
中枢神经系统淋巴瘤的影像学表现:全面更新——重点介绍 2021 年世界卫生组织中枢神经系统肿瘤分类和 2022 年血液淋巴组织肿瘤分类
AJNR Am J Neuroradiol. 2023 Apr;44(4):358-366. doi: 10.3174/ajnr.A7795. Epub 2023 Feb 23.
4
Classifying primary central nervous system lymphoma from glioblastoma using deep learning and radiomics based machine learning approach - a systematic review and meta-analysis.使用深度学习和基于影像组学的机器学习方法从胶质母细胞瘤中鉴别原发性中枢神经系统淋巴瘤——一项系统评价和荟萃分析
Front Oncol. 2022 Oct 3;12:884173. doi: 10.3389/fonc.2022.884173. eCollection 2022.
5
Diffuse Large B-Cell Epstein-Barr Virus-Positive Primary CNS Lymphoma in Non-AIDS Patients: High Diagnostic Accuracy of DSC Perfusion Metrics.非艾滋病患者弥漫性大 B 细胞 EBV 阳性原发性中枢神经系统淋巴瘤:DSC 灌注指标具有较高的诊断准确性。
AJNR Am J Neuroradiol. 2022 Nov;43(11):1567-1574. doi: 10.3174/ajnr.A7668. Epub 2022 Oct 6.
6
First clinical application of a novel T1 mapping of the whole brain.首例新型全脑 T1 映射的临床应用。
Neuroradiol J. 2022 Dec;35(6):684-691. doi: 10.1177/19714009221084244. Epub 2022 Apr 21.
7
Differentiation Between Primary Central Nervous System Lymphoma and Atypical Glioblastoma Based on MRI Morphological Feature and Signal Intensity Ratio: A Retrospective Multicenter Study.基于MRI形态学特征和信号强度比鉴别原发性中枢神经系统淋巴瘤与非典型胶质母细胞瘤:一项回顾性多中心研究
Front Oncol. 2022 Jan 31;12:811197. doi: 10.3389/fonc.2022.811197. eCollection 2022.
8
Classification of glioblastoma versus primary central nervous system lymphoma using convolutional neural networks.基于卷积神经网络的胶质母细胞瘤与原发性中枢神经系统淋巴瘤的分类。
Sci Rep. 2021 Jul 26;11(1):15219. doi: 10.1038/s41598-021-94733-0.
9
Radiomics-based differentiation between glioblastoma and primary central nervous system lymphoma: a comparison of diagnostic performance across different MRI sequences and machine learning techniques.基于放射组学的脑胶质母细胞瘤与原发性中枢神经系统淋巴瘤鉴别:不同 MRI 序列和机器学习技术的诊断效能比较。
Eur Radiol. 2021 Nov;31(11):8703-8713. doi: 10.1007/s00330-021-07845-6. Epub 2021 Apr 23.
10
Differentiation of brain metastases from small and non-small lung cancers using apparent diffusion coefficient (ADC) maps.利用表观扩散系数(ADC)图区分小细胞肺癌和非小细胞肺癌脑转移瘤。
BMC Med Imaging. 2021 Apr 15;21(1):70. doi: 10.1186/s12880-021-00602-7.