The Utility of Routine MRI Surveillance Screening in Pediatric CNS Tumor Survivors
Bibliographic record
Abstract
Abstract Purpose Surveillance magnetic resonance imaging (MRI) is routinely used to detect recurrence in pediatric central nervous system (CNS) tumors. Frequency of neuroimaging surveillance varies with no standardized approach. Methods We sought via a single institution retrospective cohort study to evaluate the frequency of recurrence identified by surveillance neuroimaging versus those detected clinically. Results This study included 476 patients; the majority diagnosed with a low-grade glioma (LGG) (n = 138; 29%), high grade glioma (HGG) (n = 77; 16%), ependymoma (n = 70; 15%) or medulloblastoma (n = 61; 13%). Patients with LGG, HGG and ependymoma more commonly had multiply recurrent disease (p = 0.08), with those with ependymoma demonstrating two or more relapses in 49% of cases. Recurrent disease was identified by imaging more often than clinical symptoms (65% vs 32%; p = < 0.01). Mean time to first relapse and subsequent relapse for the entire cohort was 30 months (range 1 day − 24.8 years) and 19.5 months (range 1 week-19.6 years), respectively. Patients diagnosed with meningioma demonstrated the longest mean time to first relapse (74.7 months), whereas those with Atypical Teratoid Rhabdoid Tumor (ATRT) and Choroid plexus papilloma tended to have the shortest time to relapse (8.9 months and 5.5 months, respectively). Overall, 22 patients sustained the first relapse > 10 years from initial diagnosis (9 LGG, 4 medulloblastoma, 3 meningioma, 2 germ cell tumor, 1 pineoblastoma, 1 craniopharyngioma, and 2 other). Conclusion With a higher tendency towards detection of tumor recurrence/progression on MRI surveillance in comparison to clinical progression, surveillance imaging should be considered 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.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".