Open-radiomics: a collection of standardized datasets and a technical protocol for reproducible radiomics machine learning pipelines.

ABSTRACT:

Background

As an important branch of machine learning pipelines in medical imaging, radiomics faces two major challenges namely reproducibility and accessibility. In this work, we introduce open-radiomics, a set of radiomics datasets along with a comprehensive radiomics pipeline based on our proposed technical protocol to investigate the effects of radiomics feature extraction on the reproducibility of the results.

Methods

We curated large-scale radiomics datasets based on three open-source datasets; BraTS 2020 for high-grade glioma (HGG) versus low-grade glioma (LGG) classification and survival analysis, BraTS 2023 for O6-methylguanine-DNA methyltransferase (MGMT) classification, and non-small cell lung cancer (NSCLC) survival analysis from the Cancer Imaging Archive (TCIA)

SUBMITTER: Namdar K 

PROVIDER: S-EPMC12323200 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature