QOL-06. RECURRENCE PATTERNS AND SURVEILLANCE IMAGING IN PEDIATRIC BRAIN TUMOR SURVIVORS
Bibliographic record
Abstract
Abstract BACKGROUND Surveillance magnetic resonance imaging (MRI) is routinely used to detect recurrence in pediatric central nervous system (CNS) tumors. Frequency of neuroimaging varies with no standardized approach. METHODS A single institution retrospective cohort study evaluated frequency and pattern of recurrences. Pediatric patients (birth to age 21 years) diagnosed with a primary CNS tumor between 1988 and 2011 treated at Lurie Children’s Hospital were included. 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%). 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). Treatment at relapse included surgical managment more often than non-surgical approach (59% vs. 41%; p=0.0016) in patients, leading to pathology confirmation of recurrence. Mean time to first relapse for the entire cohort was 2.5 years (range 1 day-24.8 years). Patients diagnosed with meningioma demonstrated the longest mean time to first relapse (74.7 months) whereas those with Atypical Teratoid Rhabdoid Tumor and Choroid plexus carcinoma tended to have the shortest time to relapse (8.9 months and 9 months, respectively). Overall, 22 patients sustained first relapse >10 years from initial diagnosis, including those diagnosed with LGG, medulloblastoma, pineoblastoma, craniopharyngioma, GCT, and meningioma. CONCLUSION With a higher percentage of tumor recurrence/progression seen on neuroimaging before development of symptoms, surveillance imaging is necessary in routine follow up of pediatric CNS tumor survivors. While the study is limited since we did not look at overall survival, 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.
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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".