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Guidelines and Experience Using Imaging Biomarker Explorer (IBEX) for Radiomics
10:17

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Published on: January 8, 2018

MRCP-derived radiomics features associated with biliary dilation: a multi-step feature reduction, multivariable

Alok Verma1, Naresh Kumar Mangalhara1, Shashank Sharma1

  • 1Department of Interventional Radiology, Sawai Man Singh Medical College and Hospital, Jaipur, India.

Abdominal Radiology (New York)
|May 15, 2026
PubMed
Summary

Magnetic Resonance Cholangiopancreatography (MRCP)-derived radiomics features show promise in identifying biliary dilation. These radiomic features demonstrate strong diagnostic performance, even in borderline cases, suggesting potential for improved clinical assessment.

Keywords:
Biliary dilationCommon bile ductImaging biomarkersMagnetic resonance cholangiopancreatographyMultivariable regressionQuantitative imagingRadiomics

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Area of Science:

  • Radiomics and Medical Imaging
  • Hepatobiliary and Pancreatic Diseases
  • Quantitative Imaging Biomarkers

Background:

  • Biliary dilation is a key indicator of various hepatobiliary conditions.
  • Accurate identification of biliary dilation is crucial for timely diagnosis and treatment.
  • Magnetic Resonance Cholangiopancreatography (MRCP) is a non-invasive imaging modality for evaluating the biliary tree.

Purpose of the Study:

  • To identify radiomics features from MRCP images associated with image-defined biliary dilation.
  • To assess the independence of these features from demographic confounders.
  • To evaluate the diagnostic performance of radiomics in classifying biliary dilation, including challenging borderline cases.

Main Methods:

  • Retrospective analysis of 135 patients undergoing MRCP.
  • Independent radiologist classification of biliary dilation.
  • 3D segmentation of the common bile duct (CBD) and radiomics feature extraction using PyRadiomics.
  • Feature selection using reproducibility, variance, correlation, and statistical thresholds.
  • Multivariable regression adjusted for demographic factors.
  • Classification models (Random Forest, Logistic Regression, Naïve Bayes) trained on texture features.

Main Results:

  • Twelve features (4 shape, 8 texture) met selection criteria, with RunLengthNonUniformity and GrayLevelNonUniformity as strongest discriminators.
  • Eleven features were significant independent correlates of biliary dilation (p < 0.001).
  • Random Forest model achieved high performance in the full cohort (AUC=0.963, accuracy=92.1%) and promising results in the borderline subgroup (AUC=0.832).

Conclusions:

  • MRCP-derived radiomics features are significantly associated with image-defined CBD dilation.
  • These features demonstrate robust diagnostic performance, including in equivocal borderline cases.
  • Prospective multicenter validation is necessary prior to clinical implementation.