Exploring deep learning and hybrid approaches in molecular subgrouping and prognostic-related genetic signatures of medulloblastoma.

ABSTRACT:

Background

Deep learning (DL) based on MRI of medulloblastoma enables risk stratification, potentially aiding in therapeutic decisions. This study aims to develop DL models that identify four medulloblastoma molecular subgroups and prognostic-related genetic signatures.

Methods

This retrospective study enrolled 325 patients for model development and an independent external validation cohort of 124 patients, totaling 449 MB patients from 2 medical institutes. Consecutive patients with newly diagnosed MB at MRI (T1-weighted, T2-weighted, and contrast-enhanced T1-weighted) at two medical institutes between January 2015 and June 2023 were identified. Two-stage sequential DL models were designed-MB-CNN that first identifies wingless (WNT), sonic hedgehog (SHH), Group 3, and Group

SUBMITTER: Li Y 

PROVIDER: S-EPMC12434915 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature