The Role of Angiogenesis in the Transformation of Plexiform Neurofibroma into Malignant Peripheral Nerve Sheath Tumors in Children With Neurofibromatosis Type 1
Bibliographic record
Abstract
PURPOSE: The role of angiogenesis in the transformation of peripheral neurofibroma (PNF) to malignant peripheral nerve sheath tumor (MPNST) in neurofibromatosis type 1 (NF1) remains elusive and forms the objective of this study. EXPERIMENTAL DESIGN: Archival tissue from 5 children with NF1 and PNF, who developed MPNST between the ages of 8 and 15 years were analyzed for differences in microvasculature. The role of proangiogenic growth factors such as Vascular Endothelial Growth Factor (VEGF), and its receptors Flk-1 and Flt-1, and vessel maturity, defined as von Willebrand factor (vWf), α-smooth muscle actin+ (SMA+), were evaluated by immuno-histochemistry. RESULTS: A qualitative evaluation of the vasculature showed predominantly α-SMA+/vWf+ more stable vessels in PNF, and an irregular meshwork of α-SMA-/vWf+ endothelial cells structures in MPNST. In NF and PNF tumor cells were VEGF-, in contrast to VEGF+ tumor cells in MPNST. If present, the VEGF stain was confined mainly to the perivascular spaces in PNF, unlike the mainly stromal VEGF stain in MPNST. VEGF receptors also manifested a tumor stage-specific pattern. Flk-1 and Flt-1 were restricted to the mature, well-formed vasculature in PNF, but exhibited a diffuse pattern in MPNST. CONCLUSION: Our study provides a rare opportunity to document consistent and histologically detectable differences in the vascular organization of PNF and MPNST. It permits a pair-wise evaluation of the malignant conversion of benign PNF into its malignant counterpart, in the same patients. The phenotypic variations and characteristics of the vessels in these tumors are consistent with the idea that a strong proangiogenic drive contributes to the progressive growth in MPNST.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".