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Radiomics Applicability Domain Analysis Classification Framework (RADAN-CF): A method for evaluating prediction

Pablo Rodríguez-Belenguer1, Manuel Marfil-Trujillo1, Aikaterini Vraka1

  • 1Biomedical Imaging Research Group (GIBI230), Instituto de Investigación Sanitaria La Fe, Valencia, Spain.

Computer Methods and Programs in Biomedicine
|April 19, 2026
PubMed
Summary
This summary is machine-generated.

We developed the Radiomics Applicability Domain ANalysis - Classification Framework (RADANCF) to assess radiomics model reliability. RADANCF improves prediction trustworthiness by analyzing data representativeness and model behavior, aiding safer clinical deployment.

Keywords:
Applicability domainEpistemic uncertaintyMachine learningRadiomicsReliability

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Area of Science:

  • Medical imaging analysis
  • Machine learning in healthcare
  • Radiomics applications

Background:

  • Radiomics models offer clinical decision support but lack reliable assessment, especially with data shifts.
  • Existing uncertainty methods fail with differing test data structures.
  • A new framework is needed for reliable radiomics prediction assessment.

Purpose of the Study:

  • Introduce the Radiomics Applicability Domain ANalysis - Classification Framework (RADANCF).
  • Enable transparent, per-prediction reliability assessment for radiomics classification.
  • Address limitations in current uncertainty estimation methods under distributional shift.

Main Methods:

  • RADANCF uses six criteria across data representativeness and model behavior.
  • Reliability categories are summarized using a traffic-light scheme.
  • Evaluated on six datasets with five classifiers, including external validation on 2689 prostate cancer patients.

Main Results:

  • Prediction error associated with RADANCF categories, with intermediate categories showing high error.
  • RADANCF criteria were complementary, with low correlations.
  • RADANCF category significantly impacted predictions, controlling for dataset/model effects.
  • Specific criteria combinations overrepresented in high-error predictions.
  • External validation confirmed RADANCF's diagnostic and risk-oriented nature.

Conclusions:

  • RADANCF offers transparent, per-prediction reliability assessment for radiomics.
  • It accounts for data representativeness and model behavior.
  • Complements traditional metrics, supporting cautious radiomics model deployment.