Deep learning-driven modality imputation and subregion segmentation to enhance high-grade glioma grading.

ABSTRACT:

Purpose

This study aims to develop a deep learning framework that leverages modality imputation and subregion segmentation to improve grading accuracy in high-grade gliomas.

Materials and methods

A retrospective analysis was conducted using data from 1,251 patients in the BraTS2021 dataset as the main cohort and 181 clinical cases collected from a medical center between April 2013 and June 2018 (51 years ± 17; 104 males) as the external test set. We propose a PatchGAN-based modality imputation network with an Aggregated Residual Transformer (ART) module combining Transformer self-attention and CNN feature extraction via residual links, paired with a U-Net variant for segmentation. Generative accuracy used PSNR and SSIM for modality conversions, while segmentation performance

SUBMITTER: Yu J 

PROVIDER: S-EPMC12124081 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature