IMG-25. PREDICTING ANATOMICAL TUMOR GROWTH IN PEDIATRIC HIGH-GRADE GLIOMAS VIA DENOISING DIFFUSION MODELS

Pediatric diffuse midline glioma (DMG) has a poor prognosis with radiotherapy as the standard of palliative care. Radiation strategies using individual geometric doseshaping have the potential to maximize patient benefit. This study aimed to establish a proof-of-principle to generate clinically relevant anatomical representations of the progression of pediatric DMG magnetic resonance image (MRI) slices through denoising diffusion implicit models (DDIM).

Based on the BraTS23 data, we analyzed the generation of MRIs with enlarged tumor sizes starting from a baseline scan. Models were pre-trained on adult glioblastoma (GBM) prior to transfer learning on pediatric high-grade glioma. Test set performance was evaluated using the Frechet Inception Distance (FID), and Structural Similarity Index (SSIM). Qualitative evaluation involved visual classification of real vs generated images by two independent radiology trainees. The optimized network was tested to forecast anatomical tumor growth in five DMG patients from an independent dataset comprising longitudinal T2-FLAIR images.

We obtain high-fidelity generated images, supported by an FID of 12.4 and 0.8 SSIM on the adult test set. Despite the smaller pediatric BraTS dataset yielding a 34.8 FID and 0.84 SSIM, human observers failed to distinguish generated from real MRIs (mean recall and precision of 0.51). The generated anatomical predictions also closely resemble the observed longitudinal tumor progression in the pediatric DMG subset. Notably, the direction and extent of tumor growth align while the surrounding brain anatomy is preserved. Quantitatively, the mean DICE score between segmentations of generated and real tumors was 0.8.

Our model produces high-quality images that conform to growth patterns of pediatric DMGs. Further quantitative validation on larger longitudinal datasets is required, to ensure the method’s robustness. The proposed method enables a personalized approach for defining target regions in radiation therapy which could translate to improved clinical outcomes.