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Radiomics and the Image Biomarker Standardisation Initiative (IBSI): A Narrative Review Using a Six-Question Map and

Heriberto Aguirre-Meneses1, Pablo Stoehr-Muñoz2, Mauricio Molina-Gonzalez3

  • 1Department of Medical Systems, Instituto Nacional de Rehabilitación "Luis Guillermo Ibarra Ibarra", Mexico City, MEX.

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|November 25, 2025
PubMed
Summary
This summary is machine-generated.

The Image Biomarker Standardization Initiative (IBSI) improves radiomic feature reproducibility across different software and scanners. Adopting IBSI standards enhances the reliability of diagnostic and prognostic models in clinical practice.

Keywords:
artificial intelligence in radiologybenchmarkingclinical translationimage biomarker standardisation initiativemulticenter validationpreprocessing standardizationquantitative imaging biomarkersradiomicsreproducibility in researchtexture analysis

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Area of Science:

  • Medical Imaging Informatics
  • Radiomics Standardization
  • Quantitative Imaging Biomarkers

Background:

  • Radiomics offers quantitative tumor heterogeneity analysis for diagnosis, prognosis, and treatment assessment.
  • Limited adoption of radiomics is attributed to poor reproducibility across diverse scanners, protocols, and software.
  • The Image Biomarker Standardization Initiative (IBSI) was established to harmonize radiomic feature definitions and preprocessing.

Purpose of the Study:

  • To explain the standardization efforts of IBSI, its implementation, and its clinical relevance.
  • To provide clinicians with essential knowledge regarding IBSI for improved radiomics adoption.
  • To outline the IBSI framework for achieving reproducible radiomic analyses.

Main Methods:

  • A narrative review approach was employed, utilizing targeted literature searches (PubMed/Google Scholar, 2010-2025).
  • Focus on IBSI manuals, consensus statements, multicenter evaluations, and clinically oriented studies.
  • Explanation of IBSI's standardization of mathematical definitions for radiomic features and upstream processing steps.

Main Results:

  • IBSI specifies the mathematics for radiomic features and preprocessing steps like interpolation, discretization, filtering, and aggregation.
  • IBSI provides compliance resources, including digital phantoms and benchmark values, to improve cross-software agreement.
  • Enhanced agreement is observed, particularly for first-order and shape features, with clarified variability in higher-order textures.

Conclusions:

  • IBSI acts as a clinical quality infrastructure for radiomics, enhancing reproducibility and generalizability of models.
  • Adoption of IBSI standards reduces inter-software variability, supporting multicenter workflows and machine learning integration.
  • Continued refinement and transparent reporting are crucial for fostering clinical trust and advancing radiomics applications.