Recurrence Patterns and Surveillance Imaging in Pediatric Brain Tumor Survivors
Bibliographic record
Abstract
Surveillance magnetic resonance imaging (MRI) is routinely used to detect recurrence in pediatric central nervous system (CNS) tumors. The frequency of neuroimaging surveillance varies without a standardized approach. A single-institutional retrospective cohort study evaluated the frequency of recurrences. This study included 476 patients with the majority diagnosed with low-grade glioma (LGG) (n=138, 29%), high-grade glioma (HGG) (n=77, 16%), ependymoma (n=70, 15%), or medulloblastoma (n=61, 13%). LGG, HGG, and ependymoma patients more commonly had multiply recurrent disease ( P =0.08), with ependymoma patients demonstrating ≥2 relapses in 47% of cases. Recurrent disease was identified by imaging more often than clinical symptoms (65% vs. 32%; P =<0.01). Patients diagnosed with meningioma demonstrated the longest mean time to first relapse (74.7 mo) whereas those with atypical teratoid rhabdoid tumor and choroid plexus carcinoma tended to have the shortest time to relapse (8.9 and 9 mo, respectively). Overall, 22 patients sustained first relapse >10 years from initial diagnosis. With a higher tendency toward detection of tumor recurrence/progression on MRI surveillance in comparison to clinical progression, surveillance imaging is necessary in routine follow up of pediatric CNS tumor survivors. With some relapses >10 years from initial diagnosis, imaging beyond this time point may be useful in particular tumor types. While the study is limited in outcome analysis, earlier detection of recurrence would lead to earlier initiation of treatment and implementation of salvage treatment regimens which can impact survival and quality of life.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".