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Updated: Jul 10, 2026

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

Design and Implementation of a No-code Graphical User Interface Application for Radiomics Analysis.

Koshi Hasegawa¹, Hayato Saito¹, Naiki Sato¹

¹Department of Radiological Technology, Graduate School of Health Sciences, Okayama, Japan.

Journal of Medical Physics

|July 9, 2026

Summary

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This study introduces a no-code application for radiomics analysis, significantly reducing processing time and enhancing accessibility for researchers and clinicians. The tool streamlines image analysis, feature extraction, and model building for improved diagnostic support.

Area of Science:

Medical Imaging
Computational Biology
Data Science

Background:

Radiomics analysis involves complex computational steps.
Current methods often require significant programming expertise.
There is a need for user-friendly tools to democratize radiomics.

Purpose of the Study:

To develop an integrated, no-code application for comprehensive radiomics analysis.
To provide an intuitive graphical user interface (GUI) for seamless workflow operation.
To enhance the efficiency and accessibility of radiomics for researchers and clinicians.

Main Methods:

Developed a GUI using Tkinter and integrated libraries like PyRadiomics and scikit-learn.
Implemented feature extraction, dimensionality reduction (LASSO, PCA), and model construction.

Keywords:

Application graphical user interface radiomics

Related Experiment Videos

Last Updated: Jul 10, 2026

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

Validated the application using datasets from The Cancer Imaging Archive and compared analysis time with conventional methods.

Main Results:

Reduced radiomics analysis time from ~20 minutes to under 6 minutes.
Achieved diagnostic support through accuracy evaluation, feature correlation visualization, and ROC analysis.
Demonstrated the application's efficiency and ease of use compared to traditional workflows.

Conclusions:

The developed application significantly improves the efficiency and availability of radiomics analysis.
The no-code GUI empowers researchers and clinicians with limited programming experience.
Future enhancements include segmentation, automated optimization, and broader modality/disease applicability.