Feasibility of radiomic feature harmonization for pooling of [¹⁸F]FET or [¹⁸F]GE-180 PET images of gliomas.

ABSTRACT:

Introduction

Large datasets are required to ensure reliable non-invasive glioma assessment with radiomics-based machine learning methods. This can often only be achieved by pooling images from different centers. Moreover, trained models should perform with high accuracy when applied to data from different centers. In this study, the impact of reconstruction settings and segmentation methods on radiomic features derived from amino acid and TSPO PET images of glioma patients was examined. Additionally, the ability to model and thus reduce feature differences was investigated.

Methods

[¹⁸F]FET and [¹⁸F]GE-180 PET data were acquired from 19 glioma patients. For each acquisition, 10 reconstruction settings and 9 segmentation methods were included to emulate

SUBMITTER: Zounek AJ 

PROVIDER: S-EPMC10068577 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature