| Title |
A Novel Volume-Age-KPS (VAK) Glioblastoma Classification Identifies a Prognostic Cognate microRNA-Gene Signature
|
|---|---|
| Published in |
PLOS ONE, August 2012
|
| DOI | 10.1371/journal.pone.0041522 |
| Pubmed ID | |
| Authors |
Pascal O. Zinn, Pratheesh Sathyan, Bhanu Mahajan, John Bruyere, Monika Hegi, Sadhan Majumder, Rivka R. Colen |
| Abstract |
Several studies have established Glioblastoma Multiforme (GBM) prognostic and predictive models based on age and Karnofsky Performance Status (KPS), while very few studies evaluated the prognostic and predictive significance of preoperative MR-imaging. However, to date, there is no simple preoperative GBM classification that also correlates with a highly prognostic genomic signature. Thus, we present for the first time a biologically relevant, and clinically applicable tumor Volume, patient Age, and KPS (VAK) GBM classification that can easily and non-invasively be determined upon patient admission. |
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 % |
|---|---|---|
| United States | 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 85 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 2% |
| Korea, Republic of | 1 | 1% |
| Netherlands | 1 | 1% |
| Brazil | 1 | 1% |
| Unknown | 80 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 17 | 20% |
| Student > Ph. D. Student | 13 | 15% |
| Student > Master | 8 | 9% |
| Student > Bachelor | 8 | 9% |
| Professor > Associate Professor | 7 | 8% |
| Other | 21 | 25% |
| Unknown | 11 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 35 | 41% |
| Computer Science | 6 | 7% |
| Agricultural and Biological Sciences | 6 | 7% |
| Biochemistry, Genetics and Molecular Biology | 6 | 7% |
| Engineering | 5 | 6% |
| Other | 11 | 13% |
| Unknown | 16 | 19% |