AutoRadiomics: A Framework for Reproducible Radiomics Research.

ABSTRACT:

Purpose

Machine learning based on radiomics features has seen huge success in a variety of clinical applications. However, the need for standardization and reproducibility has been increasingly recognized as a necessary step for future clinical translation. We developed a novel, intuitive open-source framework to facilitate all data analysis steps of a radiomics workflow in an easy and reproducible manner and evaluated it by reproducing classification results in eight available open-source datasets from different clinical entities.

Methods

The framework performs image preprocessing, feature extraction, feature selection, modeling, and model evaluation, and can automatically choose the optimal parameters for a given task. All analysis steps can be reproduced with a web applic

SUBMITTER: Woznicki P 

PROVIDER: S-EPMC10365084 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature