Image-Based Differentiation of Intracranial Metastasis From Glioblastoma Using Automated Machine Learning.

ABSTRACT:

Purpose

The majority of solitary brain metastases appear similar to glioblastomas (GBMs) on magnetic resonance imaging (MRI). This study aimed to develop and validate an MRI-based model to differentiate intracranial metastases from GBMs using automated machine learning.

Materials and methods

Radiomics features from 354 patients with brain metastases and 354 with GBMs were used to build prediction algorithms based on T2-weighted images, contrast-enhanced (CE) T1-weighted images, or both. The data of these subjects were subjected to a nested 10-fold split in the training and testing groups to build the best algorithms using the tree-based pipeline optimization tool (TPOT). The algorithms were independently validated using data from 124 institutional patients with solitary brai

SUBMITTER: Liu Y 

PROVIDER: S-EPMC9133479 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature