Prognostic value of VEGFR2 immunoexpression in glioblastoma
Bibliographic record
Abstract
Glioblastoma is the most frequent and aggressive primary tumor of the central nervous system. Prognosis is poor, with a median survival of 15 months after diagnosis. Various tumor biomarkers show prognostic value for glioblastomas, including VEGFR2, which is a receptor of VEGF related to the growth of the blood vessel network. VEGFR2 expression associates with poor prognosis in some tumors. Here we studied the prognostic value of the VEGFR2 immunohistochemical expression in glioblastoma. We used tissue microarrays to analyze 45 surgically excised samples from glioblastomas. Clinical data (age, sex, and Karnofsky Performance Status [KPS]) and morphological data (tumor necrosis, palisading, and vascular thrombosis) were collected. We performed a molecular study of MGMT and IDH1 expression (which are potential prognostic factors for glioblastomas) and an immunohistochemical study of VEGFR2 expression. Our results indicate that age, KPS, tumor necrosis, vascular thrombosis, treatment (STUPP versus other), and VEGFR2 immunoreactivity were related to prognosis (p < .005). In a multivariate analysis, only age > 65 years (Hazard Ratio (HR) (95% CI): 4.9 (2.1–11.4), p < .01), and VEGFR2 immunoexpression (HR (95% CI): 2.8 (1.3–6.1), p = .008), were found to have a statistically significant relation to prognosis. We conclude that immunohistochemical evaluation of VEGFR2 provides added prognostic value to the study of glioblastoma.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".