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Updated: Sep 9, 2025

Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics

Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics

Published on: January 8, 2018

Rethinking feature reproducibility in radiomics: the elephant in the dark.

Aydin Demircioğlu¹

¹Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany. aydin.demircioglu@uk-essen.de.

European Radiology Experimental

|September 4, 2025

Summary

Radiomics research should prioritize prediction over feature reproducibility. Even non-reproducible features can enhance predictive models, challenging traditional assumptions in clinical applications.

Keywords:

Biomarkers Machine learning Radiomics Reproducibility of results Sample size

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Area of Science:

Medical Imaging Analysis
Radiomics and Quantitative Imaging
Biomedical Data Science

Background:

Radiomics features are typically expected to be reproducible for clinical predictive model development.
Reproducibility is often considered a prerequisite, potentially overlooking valuable predictive information.

Purpose of the Study:

To investigate the impact of non-reproducible radiomics features on predictive model performance.
To challenge the conventional emphasis on feature reproducibility in radiomics.

Main Methods:

Simulated test-retest experiments were conducted to assess feature reproducibility.
Predictive models were evaluated with and without the inclusion of non-reproducible features.

Main Results:

Non-reproducible features significantly contributed to predictive performance.
Excluding non-reproducible features led to a decrease in model accuracy.
Feature interactions were found to be crucial for predictive power.

Conclusions:

The strict emphasis on feature reproducibility in radiomics may be suboptimal.
Radiomics models should consider feature interactions and prioritize clinical prediction.
Non-reproducible features can hold significant predictive value.