The Evolving Role of Radiosurgery in the Management of Radiation-Induced Meningiomas: A Review of Current Advances and Future Directions
Bibliographic record
Abstract
Meningiomas are among the most common primary adult brain tumors, which arise either spontaneously or secondary to environmental factors such as ionizing radiation. The latter are referred to as radiation-induced meningiomas (RIMs) which, while much less common than their spontaneous counterparts, are challenging from a management point of view. Similar to spontaneous meningiomas, the optimal management of RIMs is complete surgical resection. However, given their high grade, multiplicity, tendency to invade bone and venous sinuses, and high recurrence rate, this cannot always be accomplished safely. Therefore, other therapeutic modalities, such as stereotactic radiosurgery, have emerged. In the current review, we provide an overview of the historical outcomes achieved for RIMs through radiosurgery and microsurgical resection. Furthermore, we provide a discussion of clinical and radiological parameters that affect the decision-making process with regard to the management of RIMs. We also provide an outline of recent changes in our understanding of RIMs, based on molecular and genetic markers, and how these will change our management perspective. We conclude the review by summarizing some of the current obstacles in the management of RIMs with SRS and how current and future research can address these challenges.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".