Reliability of tumor segmentation in glioblastoma: Impact on the robustness of MRI-radiomic features.

ABSTRACT:

Purpose

The use of radiomic features as biomarkers of treatment response and outcome or as correlates to genomic variations requires that the computed features are robust and reproducible. Segmentation, a crucial step in radiomic analysis, is a major source of variability in the computed radiomic features. Therefore, we studied the impact of tumor segmentation variability on the robustness of MRI radiomic features.

Method

Fluid-attenuated inversion recovery (FLAIR) and contrast-enhanced T1-weighted (T1WI_CE ) MRI of 90 patients diagnosed with glioblastoma were segmented using a semiautomatic algorithm and an interactive segmentation with two different raters. We analyzed the robustness of 108 radiomic features from five categories (intensity histogram, gray-level c

SUBMITTER: Tixier F 

PROVIDER: S-EPMC6692188 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature