Glioblastoma multiforme after stereotactic radiotherapy for acoustic neuroma: Case report and review of the literature
Bibliographic record
Abstract
Indications for the use of radiotherapy in the management of a variety of benign intracranial neoplastic and nonneoplastic pathologies are increasing. Although the short-term risks are minimal, the long-term risks of radiation-induced de novo secondary neoplasms or malignant progression of the primary benign tumor need to be considered. There are currently 19 reported cases of tumors linked with stereotactic radiotherapy/radiosurgery, to which we add our second institutional experience of a patient who succumbed to a glioblastoma multiforme (GBM) after stereotactic radiotherapy for an acoustic neuroma (AN). Review of these 20 cases revealed 10 de novo secondary tumors, of which eight were malignant, with six being malignant gliomas. The majority of the cases (14 of 20) involved AN, with most being in patients with neurofibromatosis-2 (NF2; 8 of 14), reflecting the large numbers and long-term use of radiotherapy for AN. Accelerated growth of the primary benign AN, some 2 to 6 years after focused radiotherapy, was found in six of eight NF2 patients, with pathological verification of a malignant nerve sheath tumor documented in most. The exact carcinogenic risk after radiotherapy is unknown but likely extremely low. However, the risk is not zero and requires discussion with the patient, with specific consideration in young patients and those with a cancer predisposition.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".