Literature DB >> 24613549

Non-invasive assessment of intratumoral vascularity using arterial spin labeling: A comparison to susceptibility-weighted imaging for the differentiation of primary cerebral lymphoma and glioblastoma.

J Furtner1, V Schöpf2, M Preusser3, U Asenbaum4, R Woitek5, A Wöhrer6, J A Hainfellner7, S Wolfsberger8, D Prayer9.   

Abstract

Using conventional MRI methods, the differentiation of primary cerebral lymphomas (PCNSL) and other primary brain tumors, such as glioblastomas, is difficult due to overlapping imaging characteristics. This study was designed to discriminate tumor entities using normalized vascular intratumoral signal intensity values (nVITS) obtained from pulsed arterial spin labeling (PASL), combined with intratumoral susceptibility signals (ITSS) from susceptibility-weighted imaging (SWI). Thirty consecutive patients with glioblastoma (n=22) and PCNSL (n=8), histologically classified according to the WHO brain tumor classification, were included. MRIs were acquired on a 3T scanner, and included PASL and SWI sequences. nVITS was defined by the signal intensity ratio between the tumor and the contralateral normal brain tissue, as obtained by PASL images. ITSS was determined as intratumoral low signal intensity structures detected on SWI sequences and were divided into four different grades. Potential differences in the nVITS and ITSS between glioblastomas and PCNSLs were revealed using statistical testing. To determine sensitivity, specificity, and diagnostic accuracy, as well as an optimum cut-off value for the differentiation of PCNSL and glioblastoma, a receiver operating characteristic analysis was used. We found that nVITS (p=0.011) and ITSS (p=0.001) values were significantly higher in glioblastoma than in PCNSL. The optimal cut-off value for nVITS was 1.41 and 1.5 for ITSS, with a sensitivity, specificity, and accuracy of more than 95%. These findings indicate that nVITS values have a comparable diagnostic accuracy to ITSS values in differentiating glioblastoma and PCNSL, offering a completely non-invasive and fast assessment of tumoral vascularity in a clinical setting.
Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Entities:  

Keywords:  Arterial spin labeling; Glioblastoma; Intratumoural susceptibility signals; Primary CNS lymphoma; Susceptibility-weighted imaging

Mesh:

Substances:

Year:  2014        PMID: 24613549     DOI: 10.1016/j.ejrad.2014.01.017

Source DB:  PubMed          Journal:  Eur J Radiol        ISSN: 0720-048X            Impact factor:   3.528


  9 in total

1.  Molecular MRI differentiation between primary central nervous system lymphomas and high-grade gliomas using endogenous protein-based amide proton transfer MR imaging at 3 Tesla.

Authors:  Shanshan Jiang; Hao Yu; Xianlong Wang; Shilong Lu; Yufa Li; Lyujin Feng; Yi Zhang; Hye-Young Heo; Dong-Hoon Lee; Jinyuan Zhou; Zhibo Wen
Journal:  Eur Radiol       Date:  2015-04-30       Impact factor: 5.315

2.  Contribution of susceptibility- and diffusion-weighted magnetic resonance imaging for grading gliomas.

Authors:  Jianxing Xu; Hai Xu; Wei Zhang; Jiangang Zheng
Journal:  Exp Ther Med       Date:  2018-04-02       Impact factor: 2.447

3.  Differentiation of grade II/III and grade IV glioma by combining "T1 contrast-enhanced brain perfusion imaging" and susceptibility-weighted quantitative imaging.

Authors:  Jitender Saini; Pradeep Kumar Gupta; Prativa Sahoo; Anup Singh; Rana Patir; Suneeta Ahlawat; Manish Beniwal; K Thennarasu; Vani Santosh; Rakesh Kumar Gupta
Journal:  Neuroradiology       Date:  2017-10-31       Impact factor: 2.804

4.  Differentiation of Enhancing Glioma and Primary Central Nervous System Lymphoma by Texture-Based Machine Learning.

Authors:  P Alcaide-Leon; P Dufort; A F Geraldo; L Alshafai; P J Maralani; J Spears; A Bharatha
Journal:  AJNR Am J Neuroradiol       Date:  2017-04-27       Impact factor: 3.825

5.  Primary central nervous system lymphoma: is absence of intratumoral hemorrhage a characteristic finding on MRI?

Authors:  Akihiko Sakata; Tomohisa Okada; Akira Yamamoto; Mitsunori Kanagaki; Yasutaka Fushimi; Toshiki Dodo; Yoshiki Arakawa; Jun C Takahashi; Susumu Miyamoto; Kaori Togashi
Journal:  Radiol Oncol       Date:  2015-03-25       Impact factor: 2.991

Review 6.  The performance of MR perfusion-weighted imaging for the differentiation of high-grade glioma from primary central nervous system lymphoma: A systematic review and meta-analysis.

Authors:  Weilin Xu; Qun Wang; Anwen Shao; Bainan Xu; Jianmin Zhang
Journal:  PLoS One       Date:  2017-03-16       Impact factor: 3.240

7.  Noninvasive Differentiation of Meningiomas and Dural Metastases Using Intratumoral Vascularity Obtained by Arterial Spin Labeling.

Authors:  Julia Furtner; Isabelle Oth; Veronika Schöpf; Karl-Heinz Nenning; Ulrika Asenbaum; Adelheid Wöhrer; Ramona Woitek; Georg Widhalm; Barbara Kiesel; Anna S Berghoff; Johannes A Hainfellner; Matthias Preusser; Daniela Prayer
Journal:  Clin Neuroradiol       Date:  2019-06-26       Impact factor: 3.649

8.  Differentiation of Neoplastic and Non-neoplastic Intracranial Enhancement Lesions Using Three-Dimensional Pseudo-Continuous Arterial Spin Labeling.

Authors:  Wen-Zhong Hu; Fan Guo; Yong-Qiang Xu; Yi-Bin Xi; Bei He; Hong Yin; Xiao-Wei Kang
Journal:  Front Neurosci       Date:  2022-02-24       Impact factor: 4.677

9.  Machine Learning and Deep Learning CT-Based Models for Predicting the Primary Central Nervous System Lymphoma and Glioma Types: A Multicenter Retrospective Study.

Authors:  Guang Lu; Yuxin Zhang; Wenjia Wang; Lixin Miao; Weiwei Mou
Journal:  Front Neurol       Date:  2022-08-30       Impact factor: 4.086

  9 in total

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