Source:http://linkedlifedata.com/resource/pubmed/id/17219140
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| Predicate | Object |
|---|---|
| rdf:type | |
| lifeskim:mentions | |
| pubmed:issue |
7
|
| pubmed:dateCreated |
2007-6-19
|
| pubmed:abstractText |
Although multimodality therapy for high-grade gliomas is making some improvement in outcome, most patients will still die from their disease within a short time. We need tools that allow treatments to be tailored to an individual. In this study we used diffusion tensor imaging (DTI), a technique sensitive to subtle disruption of white-matter tracts due to tumour infiltration, to see if it can be used to predict patterns of glioma recurrence. In this study we imaged 26 patients with gliomas using DTI. Patients were imaged after 2 years or on symptomatic tumour recurrence. The diffusion tensor was split into its isotropic (p) and anisotropic (q) components, and these were plotted on T(2)-weighted images to show the pattern of DTI abnormality. This was compared to the pattern of recurrence. Three DTI patterns could be identified: (a) a diffuse pattern of abnormality where p exceeded q in all directions and was associated with diffuse increase in tumour size; (b) a localised pattern of abnormality where the tumour recurred in one particular direction; and (c) a pattern of minimal abnormality seen in some patients with or without evidence of recurrence. Diffusion tensor imaging is able to predict patterns of tumour recurrence and may allow better individualisation of tumour management and stratification for randomised controlled trials.
|
| pubmed:language |
eng
|
| pubmed:journal | |
| pubmed:citationSubset |
IM
|
| pubmed:status |
MEDLINE
|
| pubmed:month |
Jul
|
| pubmed:issn |
0938-7994
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| pubmed:author | |
| pubmed:issnType |
Print
|
| pubmed:volume |
17
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| pubmed:owner |
NLM
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| pubmed:authorsComplete |
Y
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| pubmed:pagination |
1675-84
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| pubmed:meshHeading |
pubmed-meshheading:17219140-Adult,
pubmed-meshheading:17219140-Aged,
pubmed-meshheading:17219140-Aged, 80 and over,
pubmed-meshheading:17219140-Biopsy,
pubmed-meshheading:17219140-Brain,
pubmed-meshheading:17219140-Brain Neoplasms,
pubmed-meshheading:17219140-Chemotherapy, Adjuvant,
pubmed-meshheading:17219140-Combined Modality Therapy,
pubmed-meshheading:17219140-Diffusion Magnetic Resonance Imaging,
pubmed-meshheading:17219140-Disease Progression,
pubmed-meshheading:17219140-Female,
pubmed-meshheading:17219140-Follow-Up Studies,
pubmed-meshheading:17219140-Glioma,
pubmed-meshheading:17219140-Humans,
pubmed-meshheading:17219140-Image Processing, Computer-Assisted,
pubmed-meshheading:17219140-Male,
pubmed-meshheading:17219140-Middle Aged,
pubmed-meshheading:17219140-Neoplasm Invasiveness,
pubmed-meshheading:17219140-Neoplasm Recurrence, Local,
pubmed-meshheading:17219140-Nerve Fibers, Myelinated,
pubmed-meshheading:17219140-Prognosis
|
| pubmed:year |
2007
|
| pubmed:articleTitle |
Predicting patterns of glioma recurrence using diffusion tensor imaging.
|
| pubmed:affiliation |
Academic Neurosurgery Unit, University of Cambridge and Cambridge University Hospitals NHS Foundation Trust, Addenbrooke's Hospital, UK. sjp58@cam.ac.uk
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| pubmed:publicationType |
Journal Article,
Research Support, Non-U.S. Gov't
|