Radiomics for residual tumour detection and prognosis in newly diagnosed glioblastoma based on postoperative [¹¹C] methionine PET and T1c-w MRI.

ABSTRACT: Personalized treatment strategies based on non-invasive biomarkers have potential to improve patient management in patients with newly diagnosed glioblastoma (GBM). The residual tumour burden after surgery in GBM patients is a prognostic imaging biomarker. However, in clinical patient management, its assessment is a manual and time-consuming process that is at risk of inter-rater variability. Furthermore, the prediction of patient outcome prior to radiotherapy may identify patient subgroups that could benefit from escalated radiotherapy doses. Therefore, in this study, we investigate the capabilities of traditional radiomics and 3D convolutional neural networks for automatic detection of the residual tumour status and to prognosticate time-to-recurrence (TTR) and overall survival (OS) in G

SUBMITTER: Shahzadi I 

PROVIDER: S-EPMC10894870 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature