Literature DB >> 30389510

Differentiation between glioblastomas and brain metastases and regarding their primary site of malignancy using dynamic susceptibility contrast MRI at 3T.

K Askaner1, A Rydelius2, S Engelholm3, L Knutsson4, J Lätt5, K Abul-Kasim6, P C Sundgren7.   

Abstract

BACKGROUND: Differentiation between glioblastoma and brain metastasis may be challenging in conventional contrast-enhanced MRI.
PURPOSE: To investigate if perfusion-weighted MRI is able to differentiate glioblastoma from metastasis and, as a second aim was to see if it was possible in the latter group, to predict the primary site of neoplasm.
MATERIAL AND METHODS: Hundred and fourteen patients with newly discovered tumor lesion (76 metastases and 38 glioblastomas) underwent conventional contrast-enhanced MRI including dynamic susceptibility contrast perfusion sequence. The calculated relative cerebral blood volumes were analyzed in the solid tumor area, peritumoral area, area adjacent to peritumoral area, and normal appearing white matter in contralateral semioval center. The Student t-test was used to detect statistically significant differences in relative cerebral blood volume between glioblastomas and metastases in the aforementioned areas. Furthermore, the metastasis group was divided in four sub groups (lung-, breast-, melanoma-, and gastrointestinal origin) and using one-way ANOVA test. P-values < 0.05 were considered significant.
RESULTS: Relative cerebral blood volume (rCBV) in the peritumoral edema was significantly higher in glioblastomas than in metastases (mean 3.2 ± 1.4 and mean 0.9 ± 0.7), respectively, (P < 0.0001). No significant differences in the solid tumor area or the area adjacent to edema were found, (P = 0.28 and 0.21 respectively). There were no significant differences among metastases in the four groups.
CONCLUSION: It is possible to differentiate glioblastomas from metastases by measuring the CBV in the peritumoral edema. It is not possible to differentiate between brain metastases from different primaries (lung-, breast-, melanoma or gastrointestinal) using CBV-measurements in the solid tumor area, peritumoral edema or area adjacent to edema.
Copyright © 2018 Elsevier Masson SAS. All rights reserved.

Entities:  

Keywords:  Brain; CNS; Glioblastoma; MRI; Metastasis; Perfusion

Mesh:

Substances:

Year:  2018        PMID: 30389510     DOI: 10.1016/j.neurad.2018.09.006

Source DB:  PubMed          Journal:  J Neuroradiol        ISSN: 0150-9861            Impact factor:   3.447


  8 in total

1.  Image-Based Differentiation of Intracranial Metastasis From Glioblastoma Using Automated Machine Learning.

Authors:  Yukun Liu; Tianshi Li; Ziwen Fan; Yiming Li; Zhiyan Sun; Shaowu Li; Yuchao Liang; Chunyao Zhou; Qiang Zhu; Hong Zhang; Xing Liu; Lei Wang; Yinyan Wang
Journal:  Front Neurosci       Date:  2022-05-12       Impact factor: 5.152

2.  Voxel-level analysis of normalized DSC-PWI time-intensity curves: a potential generalizable approach and its proof of concept in discriminating glioblastoma and metastasis.

Authors:  Albert Pons-Escoda; Alonso Garcia-Ruiz; Pablo Naval-Baudin; Francesco Grussu; Juan Jose Sanchez Fernandez; Angels Camins Simo; Noemi Vidal Sarro; Alejandro Fernandez-Coello; Jordi Bruna; Monica Cos; Raquel Perez-Lopez; Carles Majos
Journal:  Eur Radiol       Date:  2022-02-01       Impact factor: 5.315

3.  Comparison of Intraoperative Ultrasound B-Mode and Strain Elastography for the Differentiation of Glioblastomas From Solitary Brain Metastases. An Automated Deep Learning Approach for Image Analysis.

Authors:  Santiago Cepeda; Sergio García-García; Ignacio Arrese; Gabriel Fernández-Pérez; María Velasco-Casares; Manuel Fajardo-Puentes; Tomás Zamora; Rosario Sarabia
Journal:  Front Oncol       Date:  2021-02-02       Impact factor: 6.244

4.  Differentiation Between Glioblastoma Multiforme and Metastasis From the Lungs and Other Sites Using Combined Clinical/Routine MRI Radiomics.

Authors:  Yuqi Han; Lingling Zhang; Shuzi Niu; Shuguang Chen; Bo Yang; Hongyan Chen; Fei Zheng; Yuying Zang; Hongbo Zhang; Yu Xin; Xuzhu Chen
Journal:  Front Cell Dev Biol       Date:  2021-08-26

5.  Diffusion-Weighted Imaging Combined with Perfusion-Weighted Imaging under Segmentation Algorithm in the Diagnosis of Melanoma.

Authors:  Chuankui Shi; Peng Ge; Yuping Zhao; Guobao Huang
Journal:  Contrast Media Mol Imaging       Date:  2022-06-27       Impact factor: 3.009

6.  Handcrafted and Deep Learning-Based Radiomic Models Can Distinguish GBM from Brain Metastasis.

Authors:  Zhiyuan Liu; Zekun Jiang; Li Meng; Jun Yang; Ying Liu; Yingying Zhang; Haiqin Peng; Jiahui Li; Gang Xiao; Zijian Zhang; Rongrong Zhou
Journal:  J Oncol       Date:  2021-06-03       Impact factor: 4.375

Review 7.  Hemodynamic Imaging in Cerebral Diffuse Glioma-Part A: Concept, Differential Diagnosis and Tumor Grading.

Authors:  Lelio Guida; Vittorio Stumpo; Jacopo Bellomo; Christiaan Hendrik Bas van Niftrik; Martina Sebök; Moncef Berhouma; Andrea Bink; Michael Weller; Zsolt Kulcsar; Luca Regli; Jorn Fierstra
Journal:  Cancers (Basel)       Date:  2022-03-10       Impact factor: 6.639

Review 8.  Machine Learning Applications for Differentiation of Glioma from Brain Metastasis-A Systematic Review.

Authors:  Leon Jekel; Waverly R Brim; Marc von Reppert; Lawrence Staib; Gabriel Cassinelli Petersen; Sara Merkaj; Harry Subramanian; Tal Zeevi; Seyedmehdi Payabvash; Khaled Bousabarah; MingDe Lin; Jin Cui; Alexandria Brackett; Amit Mahajan; Antonio Omuro; Michele H Johnson; Veronica L Chiang; Ajay Malhotra; Björn Scheffler; Mariam S Aboian
Journal:  Cancers (Basel)       Date:  2022-03-08       Impact factor: 6.639

  8 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.