Vascularity and angiogenesis as predictors of growth in optic pathway/hypothalamic gliomas
Bibliographic record
Abstract
OBJECT: The authors' aim in conducting this study was to investigate retrospectively the prognostic significance of angiogenic features in optic pathway/hypothalamic gliomas (OPHGs) in children. METHODS: Patients were identified in whom a diagnosis of OPHG was made using pathological analysis at the Toronto Hospital for Sick Children between 1985 and 2002. Tumor specimens were reviewed for diagnostic accuracy and adequacy of the specimen. Sections were immunostained with factor VIII to assess microvessel density (MVD). A ratio of alpha-smooth muscle actin to factor VIII immunostaining was calculated to arrive at a vascular maturity index (VMI). Vascular endothelial growth factor (VEGF) and VEGF receptor (VEGFR) immunostaining were performed to evaluate angiogenic factors. In addition, the MIB-1 labeling index (LI) was used to assess proliferation. These factors were evaluated with respect to progression-free survival (PFS). Forty-one of 60 patients originally identified had adequate samples and follow up for inclusion in the study. Of these, eight patients had coexisting neurofibromatosis Type 1. Twenty-eight patients experienced tumor progression after the initial treatment (surgery with or without adjuvant treatment). Thirty-eight patients are still alive. A high MVD (> 21 vessels/1.2 mm2) was associated with a significantly higher rate of progression compared with a low MVD (< 21 vessels/1.2 mm2; p = 0.017). Microvessel density was also predictive of reduced PFS on multivariate analysis stratified for extent of resection (p = 0.04), and VMI as well as intensity and distribution of VEGF and VEGFR staining and the MIB-1 LI were not significantly associated with PFS. CONCLUSIONS: These findings suggest that MVD is the best current predictor of PFS in incompletely resected OPHGs. This information highlights the importance of angiogenesis in regard to low-grade gliomas.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".