Literature DB >> 23445331

Diffusion tensor invasive phenotypes can predict progression-free survival in glioblastomas.

L A Mohsen1, V Shi, R Jena, J H Gillard, S J Price.   

Abstract

INTRODUCTION: Glioblastomas multiformes (GBM) remain incurable in most cases. Their invasion into normal brain makes current therapies ineffective. Post-mortem studies suggest about a 25% of GBMs invade less than 1 cm from the tumour bulk and 20% invade more than 3 cm.
AIM OF STUDY: The study aims to use DTI to assess tumour extension and determine how previously reported patterns relate to the progression-free survival (PFS).
MATERIALS AND METHODS: Twenty-five patients with GBM treated according to the EORTC/NCIC protocol were retrospectively analysed. Patients were imaged post-operatively at 1.5 T. The sequences were composed of standard anatomical and a standard DTI sequence. As described earlier p and q maps were constructed. For each of the p and q maps, regions of interest were drawn around the visible abnormality. Patients were assigned a diffuse, localised or minimally invasive pattern. Progression was defined according to the RANO criteria (4) and PFS determined in days. Kaplan-Meier plots of survival for the three groups were plotted as were the proportion of patients who had not progressed at 24 months.
RESULTS: The median PFS for the diffuse group was 278 days, for the localised group 605 days and 820 days for the minimally invasive group. Three-fourth of the minimally invasive group were progression-free at 24 months (LOG RANK 9.25; p = 0.010).
CONCLUSION: It is possible to identify three invasive phenotypes in GBMs using Diffusion tensor imaging , and these three phenotypes have different progression free survival. A minimal phenotype (20% of patients) predicts a greater delay to progression.

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Year:  2013        PMID: 23445331     DOI: 10.3109/02688697.2013.771136

Source DB:  PubMed          Journal:  Br J Neurosurg        ISSN: 0268-8697            Impact factor:   1.596


  11 in total

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2.  Increased intratumoral infiltration in IDH wild-type lower-grade gliomas observed with diffusion tensor imaging.

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3.  Accurate Patient-Specific Machine Learning Models of Glioblastoma Invasion Using Transfer Learning.

Authors:  L S Hu; H Yoon; J M Eschbacher; L C Baxter; A C Dueck; A Nespodzany; K A Smith; P Nakaji; Y Xu; L Wang; J P Karis; A J Hawkins-Daarud; K W Singleton; P R Jackson; B J Anderies; B R Bendok; R S Zimmerman; C Quarles; A B Porter-Umphrey; M M Mrugala; A Sharma; J M Hoxworth; M G Sattur; N Sanai; P E Koulemberis; C Krishna; J R Mitchell; T Wu; N L Tran; K R Swanson; J Li
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Review 4.  Conventional and advanced magnetic resonance imaging in patients with high-grade glioma.

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Authors:  Stephen J Price; Adam M H Young; William J Scotton; Jared Ching; Laila A Mohsen; Natalie R Boonzaier; Victoria C Lupson; John R Griffiths; Mary A McLean; Timothy J Larkin
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6.  Multimodal MRI characteristics of the glioblastoma infiltration beyond contrast enhancement.

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7.  Semi-automated construction of patient individualised clinical target volumes for radiotherapy treatment of glioblastoma utilising diffusion tensor decomposition maps.

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8.  Multi-parametric and multi-regional histogram analysis of MRI: modality integration reveals imaging phenotypes of glioblastoma.

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Journal:  Eur Radiol       Date:  2019-02-01       Impact factor: 5.315

9.  Multi-scale segmentation in GBM treatment using diffusion tensor imaging.

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Review 10.  Applications of radiomics and machine learning for radiotherapy of malignant brain tumors.

Authors:  Martin Kocher; Maximilian I Ruge; Norbert Galldiks; Philipp Lohmann
Journal:  Strahlenther Onkol       Date:  2020-05-11       Impact factor: 4.033

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