AI-Guided Inference of Morphodynamic Attractor-like States in Glioblastoma.

ABSTRACT: Background/Objectives: Glioblastoma (GBM) exhibits heterogeneous, nonlinear invasion patterns that challenge conventional modeling and radiomic prediction. Most deep learning approaches describe the morphology but rarely capture the dynamical stability of tumor evolution. We propose an AI framework that approximates a latent attractor landscape of GBM morphodynamics-stable basins in a continuous manifold that are consistent with reproducible morphologic regimes. Methods: Multimodal MRI scans from BraTS 2020 (n = 494) were standardized and embedded with a 3D autoencoder to obtain 128-D latent representations. Unsupervised clustering identified latent basins ("attractors"). A neural ordinary differential equation (neural-ODE) approximated latent dynamics. All dynamics were infe

SUBMITTER: Volovat SR 

PROVIDER: S-EPMC12785452 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature