Improved Deep Learning Segmentation of Pediatric Diffuse Midline Gliomas After Treatment.
TL;DR
Training DMG segmentation models with post-treatment scans substantially improves performance in longitudinal clinical trial imaging, enabling more accurate volumetric tracking and response assessment.
Abstract
Purpose
To develop and validate a pediatric diffuse midline glioma (DMG) auto-segmentation tool optimized for longitudinal treatment response assessment across the disease course.
Materials And Methods
In this multi-institutional retrospective study, we included patients aged 1-30 years with DMG from an institutional pediatric cancer center, BraTS-PEDs 2024, and PNOC007, a prospective trial of radiation followed by peptide vaccine plus poly-ICLC, and we trained nnU-Net-based DMGtracker using expert segmentations from 140 institutional pre- and post-treatment studies and all 261 BraTS-PEDs 2024 pre-treatment studies, using four-sequence multiparametric MRI (T1, T1 post-contrast, T2, and FLAIR). We externally validated the model on 88 annotated PNOC007 studies (n = 49 patients) and compared it with the BraTS-PEDs 2024 winning model using median Dice similarity coefficient (DSC) and relative volumetric difference (RVD) for whole-tumor and contrast-enhancing tumor segmentation using the Wilcoxon signed-rank test.
Results
Training and internal testing used 153 scans (59 post-treatment) from 74 patients. Incorporating post-treatment data improved internal whole-tumor DSC for our trained model (0.94 [IQR 0.82-0.96] vs 0.93 [0.81-0.96]; p<0.001). On external validation, DMGtracker outperformed the BraTS-PEDs 2024 winning model for whole-tumor segmentation, with higher DSC (0.90 [0.72-0.95] vs 0.81 [0.66-0.90]) and lower RVD (9.6% [3.7%-31.6%] vs 16.8% [7.3%-39.2%]). This advantage was greatest in post-treatment scans (n = 50 scans, DSC 0.90 [0.73-0.94] vs 0.80 [0.58-0.88]; RVD 9.7% [3.8%-26.2%] vs 19.8% [12.6%-39.8%]; p<0.001 for both). In post-treatment scans, DMGtracker achieved clinically acceptable whole-tumor segmentation (DSC > 0.80) in 64.0% of cases, compared with 52.0% for the BraTS-PEDs winner.
Conclusion
Training DMG segmentation models with post-treatment scans substantially improves performance in longitudinal clinical trial imaging, enabling more accurate volumetric tracking and response assessment.