Keywords: AI Diffusion Models, Machine Learning/Artificial Intelligence, Oncology, Cancer, DMG, Diffuse Midline Glioma
Motivation: Pediatric diffuse midline gliomas are associated with a poor prognosis, leaving radiotherapy as standard of palliative care. Personalized radiation regimes could maximize the benefit for the patient, and consequently improve clinical outcomes.
Goal(s): This study explores a state-of-the-art computer vision method to predict the anatomical growth of tumors which could inform tailored radiotherapy treatments.
Approach: A denoising diffusion implicit model is employed to generate realistic, high-quality magnetic resonance imaging scans of enlarged tumor sizes starting from a baseline image.
Results: Our proof-of-concept study demonstrates promising results on an external longitudinal pediatric dataset, highlighting the method’s potential to realistically predict visual tumor growth.
Impact: We demonstrate realistic predictions of anatomical (pediatric) brain tumor growth using a generative denoising diffusion implicit model. This enables personalized predictions of tumor growth trajectories to guide localized therapies such as geometric dose shaping for radiotherapy delivery.
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