Deep learning volumetrics reveal distinct clinical trajectories for pediatric low-grade gliomas under surveillance: A multicenter study.

Background: Pediatric low-grade gliomas ( 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 cm 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.