Literature DB >> 28124178

Quantitative multi-modal MR imaging as a non-invasive prognostic tool for patients with recurrent low-grade glioma.

Evan Neill1, Tracy Luks2, Manisha Dayal1, Joanna J Phillips3, Arie Perry3, Llewellyn E Jalbert1, Soonmee Cha1, Annette Molinaro4, Susan M Chang4, Sarah J Nelson1.   

Abstract

Low-grade gliomas can vary widely in disease course and therefore patient outcome. While current characterization relies on both histological and molecular analysis of tissue resected during surgery, there remains high variability within glioma subtypes in terms of response to treatment and outcome. In this study we hypothesized that parameters obtained from magnetic resonance data would be associated with progression-free survival for patients with recurrent low-grade glioma. The values considered were derived from the analysis of anatomic imaging, diffusion weighted imaging, and 1H magnetic resonance spectroscopic imaging data. Metrics obtained from diffusion and spectroscopic imaging presented strong prognostic capability within the entire population as well as when restricted to astrocytomas, but demonstrated more limited efficacy in the oligodendrogliomas. The results indicate that multi-parametric imaging data may be applied as a non-invasive means of assessing prognosis and may contribute to developing personalized treatment plans for patients with recurrent low-grade glioma.

Entities:  

Keywords:  Diffusion; Glioma; MRI; Progression-free survival

Mesh:

Year:  2017        PMID: 28124178      PMCID: PMC5373029          DOI: 10.1007/s11060-016-2355-y

Source DB:  PubMed          Journal:  J Neurooncol        ISSN: 0167-594X            Impact factor:   4.130


  30 in total

1.  A prognostic model based on preoperative MRI predicts overall survival in patients with diffuse gliomas.

Authors:  A Hilario; J M Sepulveda; A Perez-Nuñez; E Salvador; J M Millan; A Hernandez-Lain; V Rodriguez-Gonzalez; A Lagares; A Ramos
Journal:  AJNR Am J Neuroradiol       Date:  2014-01-23       Impact factor: 3.825

2.  MRI apparent diffusion coefficient reflects histopathologic subtype, axonal disruption, and tumor fraction in diffuse-type grade II gliomas.

Authors:  Inas S Khayal; Scott R Vandenberg; Kenneth J Smith; Colleen P Cloyd; Susan M Chang; Soonmee Cha; Sarah J Nelson; Tracy R McKnight
Journal:  Neuro Oncol       Date:  2011-08-24       Impact factor: 12.300

Review 3.  The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary.

Authors:  David N Louis; Arie Perry; Guido Reifenberger; Andreas von Deimling; Dominique Figarella-Branger; Webster K Cavenee; Hiroko Ohgaki; Otmar D Wiestler; Paul Kleihues; David W Ellison
Journal:  Acta Neuropathol       Date:  2016-05-09       Impact factor: 17.088

4.  Relationships between choline magnetic resonance spectroscopy, apparent diffusion coefficient and quantitative histopathology in human glioma.

Authors:  R K Gupta; T F Cloughesy; U Sinha; J Garakian; J Lazareff; G Rubino; L Rubino; D P Becker; H V Vinters; J R Alger
Journal:  J Neurooncol       Date:  2000-12       Impact factor: 4.130

5.  Implementation of 3 T lactate-edited 3D 1H MR spectroscopic imaging with flyback echo-planar readout for gliomas patients.

Authors:  Ilwoo Park; Albert P Chen; Matthew L Zierhut; Esin Ozturk-Isik; Daniel B Vigneron; Sarah J Nelson
Journal:  Ann Biomed Eng       Date:  2010-07-23       Impact factor: 3.934

6.  Survival analysis in patients with glioblastoma multiforme: predictive value of choline-to-N-acetylaspartate index, apparent diffusion coefficient, and relative cerebral blood volume.

Authors:  Joonmi Oh; Roland G Henry; Andrea Pirzkall; Ying Lu; Xiaojuan Li; Isabelle Catalaa; Susan Chang; William P Dillon; Sarah J Nelson
Journal:  J Magn Reson Imaging       Date:  2004-05       Impact factor: 4.813

7.  Three-dimensional J-resolved H-1 magnetic resonance spectroscopic imaging of volunteers and patients with brain tumors at 3T.

Authors:  Yan Li; Albert P Chen; Jason C Crane; Susan M Chang; Daniel B Vigneron; Sarah J Nelson
Journal:  Magn Reson Med       Date:  2007-11       Impact factor: 4.668

8.  Relative cerebral blood volume measurements of low-grade gliomas predict patient outcome in a multi-institution setting.

Authors:  Gisele B Caseiras; Sophie Chheang; James Babb; Jeremy H Rees; Nicole Pecerrelli; Daniel J Tozer; Christopher Benton; David Zagzag; Glyn Johnson; Adam D Waldman; H R Jäger; Meng Law
Journal:  Eur J Radiol       Date:  2009-02-06       Impact factor: 3.528

9.  Identification of MRI and 1H MRSI parameters that may predict survival for patients with malignant gliomas.

Authors:  Xiaojuan Li; Hua Jin; Ying Lu; Joonmi Oh; Susan Chang; Sarah J Nelson
Journal:  NMR Biomed       Date:  2004-02       Impact factor: 4.044

10.  IDH mutation, 1p19q codeletion and ATRX loss in WHO grade II gliomas.

Authors:  Heather E Leeper; Alissa A Caron; Paul A Decker; Robert B Jenkins; Daniel H Lachance; Caterina Giannini
Journal:  Oncotarget       Date:  2015-10-06
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  4 in total

1.  Increasing FLAIR signal intensity in the postoperative cavity predicts progression in gross-total resected high-grade gliomas.

Authors:  Guan-Min Quan; Yong-Li Zheng; Tao Yuan; Jian-Ming Lei
Journal:  J Neurooncol       Date:  2018-03-21       Impact factor: 4.130

2.  MRS as an Aid to Diagnose Malignant Transformation in Low-Grade Gliomas with Increasing Contrast Enhancement.

Authors:  C H Toh; M Castillo; K-C Wei; P-Y Chen
Journal:  AJNR Am J Neuroradiol       Date:  2020-07-30       Impact factor: 3.825

3.  Automated Detection of Brain Tumor through Magnetic Resonance Images Using Convolutional Neural Network.

Authors:  Sahar Gull; Shahzad Akbar; Habib Ullah Khan
Journal:  Biomed Res Int       Date:  2021-11-30       Impact factor: 3.411

4.  An automated approach for predicting glioma grade and survival of LGG patients using CNN and radiomics.

Authors:  Chenan Xu; Yuanyuan Peng; Weifang Zhu; Zhongyue Chen; Jianrui Li; Wenhao Tan; Zhiqiang Zhang; Xinjian Chen
Journal:  Front Oncol       Date:  2022-08-12       Impact factor: 5.738

  4 in total

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