Targeted therapies in optic pathway gliomas
Bibliographic record
Abstract
AIM: This study provides a systematic synthesis of current evidence on targeted therapies for optic pathway gliomas (OPGs), emphasizing their molecular rationale, clinical effectiveness, safety profiles, relevance in both Neurofibromatosis type 1 (NF1) -associated and sporadic cases. METHODS: A systematic literature review was conducted in accordance with PRISMA guidelines using PubMed, Web of Science, and Scopus databases up to April 2025. Eligible studies focused on systemic targeted therapies for OPGs, evaluating efficacy, molecular targets, and adverse events. Both preclinical and clinical data were included, with study quality assessed using the Newcastle-Ottawa Scale. RESULTS: Of 414 records screened, 13 studies (11 clinical and 2 preclinical) met inclusion criteria. Targeted agents included MEK inhibitors, mTOR inhibitors, anti-VEGF agents, and BRAF inhibitors. MEK inhibitors showed promising progression-free survival outcomes, particularly in NF1-associated OPGs, while anti-VEGF therapies rapidly improved visual symptoms in select cases. MEK inhibitors showed the most consistent progression-free survival benefits, particularly in NF1-associated OPGs, with selumetinib emerging as the leading agent with favorable efficacy and safety profiles. These findings support the growing role of biomarker-driven targeted strategies while underscoring unresolved challenges related to long-term safety and optimal treatment duration. CONCLUSION: Targeted therapies constitute a potentially paradigm-shifting development in the management of OPGs, enhancing disease control while improving the prospects for long-term visual preservation. This review underscores the need for individualized, biomarker-driven approaches and highlights challenges including resistance, long-term safety, and therapy duration.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".