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Updated: Jun 16, 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

Radiomics: a primer on high-throughput image phenotyping.

Kyle J Lafata^1,2,3, Yuqi Wang⁴, Brandon Konkel⁵

¹Department of Radiology, Duke University School of Medicine, Durham, NC, USA. kyle.lafata@duke.edu.

Abdominal Radiology (New York)

|August 26, 2021

Summary

Radiomics, a quantitative imaging analysis, extracts disease patterns from scans using artificial intelligence. This introduction covers its methods, potential pitfalls, and aims to improve reproducibility in medical imaging.

Keywords:

Artificial intelligence Biomarkers Image-based phenotyping Machine learning Radiomics

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Area of Science:

Quantitative Imaging
Medical Imaging Analysis
Radiology

Background:

Radiomics is a high-throughput image phenotyping technique.
It extracts quantitative features from radiological images using computer algorithms.
These features act as digital fingerprints for disease detection and characterization.

Purpose of the Study:

To introduce the field of radiomics.
To explain its role as an advanced data analytics application.
To highlight the driving force of artificial intelligence in radiomics.

Main Methods:

Formal introduction to radiomics as a data analytics application.
Illustrative examples provided in abdominal radiology.
Comparison of common artificial intelligence techniques used in radiomics.

Main Results:

The radiomic phenotyping process is broken down into five key phases.
Potential pitfalls in each phase are identified.
Recommendations are provided to enhance reproducibility and reduce errors.

Conclusions:

Radiomics offers a powerful approach to image phenotyping.
Understanding the process and potential pitfalls is crucial for reliable results.
Artificial intelligence is central to the advancement of radiomics.