Radiomics for pseudoprogression prediction in high grade gliomas: added value of MR contrast agent.

ABSTRACT:

Objective

Our aim is to define the capabilities of radiomics in predicting pseudoprogression from pre-treatment MR images in patients diagnosed with high-grade gliomas using T1 non-contrast-enhanced and contrast-enhanced images.

Material & methods

In this retrospective IRB-approved study, image segmentation of high-grade gliomas was semi-automatically performed using 3D Slicer. Non-contrast-enhanced T1-weighted images and contrast-enhanced T1-weighted images were used prior to surgical therapy or radio-chemotherapy. Imaging data was split into a training sample and an independent test sample at random. We extracted 107 radiomic features by use of PyRadiomics. Feature selection and model construction were performed using Generalized Boosted Regression Models (GBM).

Results

SUBMITTER: Mammadov O 

PROVIDER: S-EPMC9364026 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature