| Title |
Glioma recurrence versus radiation necrosis: accuracy of current imaging modalities
|
|---|---|
| Published in |
Journal of Neuro-Oncology, October 2009
|
| DOI | 10.1007/s11060-009-9897-1 |
| Pubmed ID | |
| Authors |
George A. Alexiou, Spyridon Tsiouris, Athanasios P. Kyritsis, Spyridon Voulgaris, Maria I. Argyropoulou, Andreas D. Fotopoulos |
| Abstract |
Treatment for brain gliomas is a combined approach of surgery, radiation therapy and chemotherapy. Nevertheless, high-grade gliomas usually recur despite treatment. Ionizing radiation therapy to the central nervous system may cause post-radiation damage. Differentiation between post-irradiation necrosis and recurrent glioma on the basis of clinical signs and symptomatology has not been possible. Computed tomography (CT) and magnetic resonance imaging (MRI) suffer from significant limitations when applied to differentiate recurrent brain tumor from radiation necrosis. We reviewed the contribution of recent MRI techniques, single-photon emission CT and positron emission tomography to discriminate necrosis for glioma recurrence. We concluded that despite the progress being made, further research is needed to establish reliable imaging modalities that distinguish between true tumour progression and treatment-related necrosis. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Congo | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 80 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 4% |
| Unknown | 77 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 14 | 18% |
| Researcher | 12 | 15% |
| Student > Ph. D. Student | 9 | 11% |
| Student > Postgraduate | 7 | 9% |
| Professor > Associate Professor | 6 | 8% |
| Other | 15 | 19% |
| Unknown | 17 | 21% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 40 | 50% |
| Engineering | 10 | 13% |
| Agricultural and Biological Sciences | 4 | 5% |
| Mathematics | 2 | 3% |
| Computer Science | 2 | 3% |
| Other | 3 | 4% |
| Unknown | 19 | 24% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 17 April 2014.
All research outputs
#15,299,491
of 22,753,345 outputs
Outputs from Journal of Neuro-Oncology
#1,942
of 2,961 outputs
Outputs of similar age
#77,814
of 93,319 outputs
Outputs of similar age from Journal of Neuro-Oncology
#12
of 14 outputs
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We're also able to compare this research output to 14 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.