Deep learning volumetrics reveal distinct clinical trajectories for pediatric low-grade gliomas under surveillance: A multicenter study
Bibliographic record
Abstract
Abstract Background Pediatric low-grade gliomas (pLGGs) have heterogeneous clinical presentations, and given the morbidity of treatment, some patients receive observation with magnetic resonance (MR). The natural histories of untreated pLGGs remain understudied. We leveraged deep learning-based volumetrics to analyze longitudinal growth trajectories and progression risk factors for untreated pLGGs. Methods We conducted a pooled, retrospective study of radiographically diagnosed pLGG patients from two institutions diagnosed between 1992 and 2020 who were surveilled for at least 1 year post-diagnosis. Tumor segmentation was applied to longitudinal T2-weighted MR to calculate 3D tumor volumes. We assessed volume trajectories, disease progression, and associated risk factors using Cox-Hazards regression, survival analysis, and time-series forecasting with autoregressive integrated moving average (ARIMA). Patients were categorized based on volumetric changes into progression (≥25%), regression (≤−25%), or stability. Results Of 99 patients (970 scans; median follow-up: 7.0 years; median diagnosis age: 12.0 years), 55 (55.5%) had tumors that volumetrically progressed, 28 (28.3%) remained stable, and 16 (16.2%) regressed. 42 (42.4%) patients initiated treatment. Risk factors associated with progression included infancy/preschool age, cortical location, and female sex (p ≤ 0.05 for each). Most progressions occurred within five years of diagnosis (80.0%), most commonly in school-aged children (7-13 years old). Time-series forecasting predicted future tumor volume with a mean absolute error of 2.04 cm3. Conclusion Deep learning enables systematic, longitudinal, pLGG growth tracking and characterization of patients on surveillance, yielding insights into untreated tumor trajectories and progression risk. This pipeline is useful at population-level to study growth trends and at patient-level to guide personalized management.
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 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".