Dual-center validation of using magnetic resonance imaging radiomics to predict stereotactic radiosurgery outcomes.

ABSTRACT:

Background

MRI radiomic features and machine learning have been used to predict brain metastasis (BM) stereotactic radiosurgery (SRS) outcomes. Previous studies used only single-center datasets, representing a significant barrier to clinical translation and further research. This study, therefore, presents the first dual-center validation of these techniques.

Methods

SRS datasets were acquired from 2 centers (n = 123 BMs and n = 117 BMs). Each dataset contained 8 clinical features, 107 pretreatment T1w contrast-enhanced MRI radiomic features, and post-SRS BM progression endpoints determined from follow-up MRI. Random decision forest models were used with clinical and/or radiomic features to predict progression. 250 bootstrap repetitions were used for single-cen

SUBMITTER: DeVries DA 

PROVIDER: S-EPMC10289521 | biostudies-literature | 2023 Jan-Dec

REPOSITORIES: biostudies-literature