Deep learning-based postoperative glioblastoma segmentation and extent of resection evaluation: Development, external validation, and model comparison.

ABSTRACT:

Background

The pursuit of automated methods to assess the extent of resection (EOR) in glioblastomas is challenging, requiring precise measurement of residual tumor volume. Many algorithms focus on preoperative scans, making them unsuitable for postoperative studies. Our objective was to develop a deep learning-based model for postoperative segmentation using magnetic resonance imaging (MRI). We also compared our model's performance with other available algorithms.

Methods

To develop the segmentation model, a training cohort from 3 research institutions and 3 public databases was used. Multiparametric MRI scans with ground truth labels for contrast-enhancing tumor (ET), edema, and surgical cavity, served as training data. The models were trained using MONAI and nnU-Net frame

SUBMITTER: Cepeda S 

PROVIDER: S-EPMC11631186 | biostudies-literature | 2024 Jan-Dec

REPOSITORIES: biostudies-literature