A Predictive Clinical-Radiomics Nomogram for Survival Prediction of Glioblastoma Using MRI.

ABSTRACT: Glioblastoma (GBM) is the most common and aggressive primary brain tumor in adult patients with a median survival of around one year. Prediction of survival outcomes in GBM patients could represent a huge step in treatment personalization. The objective of this study was to develop machine learning (ML) algorithms for survival prediction of GBM patient. We identified a radiomic signature on a training-set composed of data from the 2019 BraTS challenge (210 patients) from MRI retrieved at diagnosis. Then, using this signature along with the age of the patients for training classification models, we obtained on test-sets AUCs of 0.85, 0.74 and 0.58 (0.92, 0.88 and 0.75 on the training-sets) for survival at 9-, 12- and 15-months, respectively. This signature was then validated on an independe

SUBMITTER: Ammari S 

PROVIDER: S-EPMC8624566 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature