A multicenter study on deep learning for glioblastoma auto-segmentation with prior knowledge in multimodal imaging.

ABSTRACT: A precise radiotherapy plan is crucial to ensure accurate segmentation of glioblastomas (GBMs) for radiation therapy. However, the traditional manual segmentation process is labor-intensive and heavily reliant on the experience of radiation oncologists. In this retrospective study, a novel auto-segmentation method is proposed to address these problems. To assess the method's applicability across diverse scenarios, we conducted its development and evaluation using a cohort of 148 eligible patients drawn from four multicenter datasets and retrospective data collection including noncontrast CT, multisequence MRI scans, and corresponding medical records. All patients were diagnosed with histologically confirmed high-grade glioma (HGG). A deep learning-based method (PKMI-Net) for automatically

SUBMITTER: Tian S 

PROVIDER: S-EPMC11447882 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature