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AI-powered segmentation and prognosis with missing MRI in pediatric brain tumors
Dimosthenis Chrysochoou1,2, Deep B Gandhi2,3, Sahand Adib2
1University of Pennsylvania, Department of Bioengineering, Philadelphia, PA, USA.
NPJ Precision Oncology
|January 13, 2026
Summary
Deep learning models for pediatric brain tumor segmentation struggle with incomplete MRI data. A new dropout-trained model maintains segmentation accuracy and prognostic performance even with missing MRI sequences.
Area of Science:
- Neuroimaging
- Artificial Intelligence
- Pediatric Oncology
Background:
- Brain MRI is crucial for pediatric brain tumor diagnosis and treatment planning.
- Deep learning models for tumor segmentation and prognosis require complete MRI sequences.
- Incomplete MRI acquisitions are frequent, limiting AI tool clinical utility.
Purpose of the Study:
- To develop and evaluate strategies for handling missing MRI sequences in pediatric brain tumor analysis.
- To improve the robustness and clinical applicability of AI models for pediatric neuro-oncology.
Main Methods:
- Retrospective study of 715 patients from Children's Brain Tumor Network and BraTS-PEDs, plus 43 patients from PNOC003/007 trials.
- Development of a dropout-trained segmentation model, generative image synthesis, copy-substitution, and zeroed inputs.
- Evaluation of segmentation performance (Dice score) and prognostic accuracy using model-derived tumor volumes.
Main Results:
- The dropout model demonstrated robust segmentation with minimal performance drop (≤0.04 Dice) on incomplete MRI data.
- Generative synthesis produced high-quality images (SSIM > 0.90) and reduced artifacts.
- The dropout model maintained stable prognostic accuracy in survival analysis.
Conclusions:
- AI models trained with input dropout can effectively handle missing MRI sequences in pediatric brain tumors.
- Generative synthesis enhances image quality and interpretability.
- These methods support the broader implementation of AI in pediatric neuro-oncology settings.
