LGG-16. Malignant transformation of pediatric low-grade gliomas with FGFR alterations: case reports and systematic review of the literature
Bibliographic record
Abstract
Abstract Introduction Central nervous system (CNS) tumors are the most common solid tumours in children, with low-grade gliomas accounting for over 30%. These tumors can be treated with surgical resection alone, but recurrences can occur, with malignant transformation in up to 10%. Molecular alterations have emerged as important prognostic factors and are now incorporated into tumor classification systems. Fibroblast growth factor receptor (FGFR) alterations are identified in up to 10% of pediatric low-grade gliomas. While they are known for their potential role in malignant transformation particularly in glioblastomas and other non-CNS tumors, their prognostic significance in low-grade pediatric CNS tumors remains unclear, with most research focusing on their potential as a therapeutic target. Methods Small case series of consecutive and systematic review of the literature on malignant transformations in pediatric low-grade CNS tumors, with an emphasis on FGFR alterations. ResultsTwo female patients, one with World Health Organization (WHO) grade I pilocytic astrocytoma and the other with polymorphous low-grade neuroepithelial tumor of the young (PLNTY), both harboring FGFR alterations, developed asymptomatic recurrence with malignant transformation to high grade gliomas within 24 months of their initial complete resection. Both patients are currently being treated with adjuvant therapy. The literature review identified three cases of WHO grade I PLNTY with FGFR alterations (age range 9-15 years at diagnosis) which recurred with malignant transformation following complete resection (range 10-60 months after surgery). Recurrence was treated by surgery then radio-chemotherapy and chemotherapy alone for one patient with 100% of survival at last follow-up. Conclusions Preliminary evidence suggests that low-grade gliomas with FGFR alterations may have an increased risk of malignant transformation. Close and long-term follow-up is essential for these patients. Further research is needed to: (1) elucidate the precise role of FGFR alterations in tumor progression; (2) identify additional molecular markers that may predict malignant transformation; and (3) develop targeted therapies to prevent or treat malignant progression in these tumors.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".