Assessing preoperative risk of STR in skull meningiomas using MR radiomics and machine learning.

ABSTRACT: Our aim is to predict possible gross total and subtotal resections of skull meningiomas from pre-treatment T1 post contrast MR-images using radiomics and machine learning in a representative patient cohort. We analyse the accuracy of our model predictions depending on the tumor location within the skull and the postoperative tumor volume. In this retrospective, IRB-approved study, image segmentation of the contrast enhancing parts of the tumor was semi-automatically performed using the 3D Slicer open-source software platform. Imaging data were split into training data and independent test data at random. We extracted a total of 107 radiomic features by hand-delineated regions of interest on T1 post contrast MR images. Feature preselection and model construction were performed with eight di

SUBMITTER: Musigmann M 

PROVIDER: S-EPMC9388514 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature