Generative AI for spatial tumor growth on MRI: a proof-of-principle study in pediatric diffuse midline glioma

Abstract Background Magnetic resonance imaging (MRI) is a cornerstone of non-invasive diagnosis and response monitoring in neuro-oncology, and predictions of spatial tumor progression conditioned on the patients’ anatomy are increasingly important. We present a proof-of-principle of personalized spa...

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Autores principales:	Daria Laslo, Julia Wolleb, Maria Monzon, Raimund Kottke, Selma Sirin, Timothy Müller, Dror Suhami, Franziska Vogt, Aashim Bhatia, Deep B. Gandhi, Anahita Fathi Kazerooni, Ariana M. Familiar, Thien Nguyen, Zhifan Jiang, Abhijeet Parida, Nicolas U. Gerber, Ana Guerreiro Stücklin, Ali Nabavizadeh, Javad Nazarian, Marius George Linguraru, Andreas M. Rauschecker, Sabine Müller, Catherine Jutzeler, Sarah Brüningk
Formato:	Artigo
Lenguaje:	Inglês
Publicado:	BMC 2026-05-01
Colección:	BMC Medicine
Materias:	Generative AI Denoising Diffusion Models DMG Brain tumor Anatomical tumor growth
Acceso en línea:	https://doi.org/10.1186/s12916-026-04911-y
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