Toward image-based personalization of glioblastoma therapy: A clinical and biological validation study of a novel, deep learning-driven tumor growth model.

ABSTRACT:

Background

The diffuse growth pattern of glioblastoma is one of the main challenges for accurate treatment. Computational tumor growth modeling has emerged as a promising tool to guide personalized therapy. Here, we performed clinical and biological validation of a novel growth model, aiming to close the gap between the experimental state and clinical implementation.

Methods

One hundred and twenty-four patients from The Cancer Genome Archive (TCGA) and 397 patients from the UCSF Glioma Dataset were assessed for significant correlations between clinical data, genetic pathway activation maps (generated with PARADIGM; TCGA only), and infiltration (D_w) as well as proliferation (ρ) parameters stemming from a Fisher-Kolmogorov growth model. To further evaluate cl

SUBMITTER: Metz MC 

PROVIDER: S-EPMC10907005 | biostudies-literature | 2024 Jan-Dec

REPOSITORIES: biostudies-literature