Stereotactic radiosurgery for central neurocytomas: an international multicenter retrospective cohort study
Bibliographic record
Abstract
OBJECTIVE: Central neurocytomas (CNs) are uncommon intraventricular tumors, and their rarity renders the risk-to-benefit profile of stereotactic radiosurgery (SRS) unknown. The aim of this multicenter, retrospective cohort study was to evaluate the outcomes of SRS for CNs and identify predictive factors. METHODS: The authors retrospectively analyzed a cohort of patients with CNs treated with SRS at 10 centers between 1994 and 2018. Tumor recurrences were classified as local or distant. Adverse radiation effects (AREs) and the need for a CSF shunt were also evaluated. RESULTS: The study cohort comprised 60 patients (median age 30 years), 92% of whom had undergone prior resection or biopsy and 8% received their diagnosis based on imaging alone. The median tumor volume and margin dose were 5.9 cm3 and 13 Gy, respectively. After a median clinical follow-up of 61 months, post-SRS tumor recurrence occurred in 8 patients (13%). The 5- and 10-year local tumor control rates were 93% and 87%, respectively. The 5- and 10-year progression-free survival rates were 89% and 80%, respectively. AREs were observed in 4 patients (7%), but only 1 was symptomatic (2%). Two patients underwent post-SRS tumor resection (3%). Prior radiotherapy was a predictor of distant tumor recurrence (p = 0.044). Larger tumor volume was associated with pre-SRS shunt surgery (p = 0.022). CONCLUSIONS: Treatment of appropriately selected CNs with SRS achieves good tumor control rates with a reasonable complication profile. Distant tumor recurrence and dissemination were observed in a small proportion of patients, which underscores the importance of close post-SRS surveillance of CN patients. Patients with larger CNs are more likely to require shunt surgery before SRS.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".