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Radiomic features from dual-energy CT (DECT) show high repeatability under consistent conditions. Reproducibility decreases with varying virtual monoenergetic image (VMI) energies or DECT methods, impacting machine learning classification of liver lesions.

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Area of Science:

  • Medical Imaging
  • Radiology
  • Artificial Intelligence in Medicine

Background:

  • Dual-energy CT (DECT) enables the generation of virtual monoenergetic images (VMI) at various energy levels.
  • Radiomic features extracted from medical images can aid in disease diagnosis and characterization.
  • Understanding the repeatability and reproducibility of radiomic features is crucial for their clinical application.

Purpose of the Study:

  • To evaluate the test-retest repeatability and reproducibility of radiomic features in VMI from DECT.
  • To assess the influence of VMI energy, radiation dose, and DECT acquisition methods on feature stability.
  • To determine the impact of VMI energy and feature repeatability on machine learning classification of liver lesions in vivo.

Main Methods:

  • Radiomic features were analyzed in phantom studies using dual-source (DSDE) and split-filter (SFDE) DECT scanners.
  • Repeatability and reproducibility were quantified using concordance-correlation-coefficient (CCC) and dynamic range (DR) metrics.
  • Machine learning models (penalized regression, random forests) were trained and tested on in vivo data of 72 hypodense liver lesions.

Main Results:

  • High test-retest repeatability of radiomic features was observed when VMI energy and scan conditions remained constant.
  • Reproducibility significantly decreased when comparing different VMI energies or DECT acquisition approaches.
  • Radiation dose variations (5 vs. 15 mGy) had a minimal impact (<10%) on feature repeatability.
  • Optimal VMI energy selection (low-energy for hemangiomas/metastases, high-energy for cysts) improved in vivo lesion classification.
  • Feature selection based on repeatability did not enhance classification performance.

Conclusions:

  • Radiomic features extracted from DECT-VMI are highly repeatable under consistent imaging and reconstruction parameters.
  • Clinical application requires careful consideration of VMI energy and DECT technique due to diminished reproducibility across variations.
  • Task-specific VMI energy optimization is essential for improving the accuracy of machine learning-based liver lesion classification.