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DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...
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Assessing radiomics feature stability with simulated CT acquisitions.

Kyriakos Flouris1, Oscar Jimenez-Del-Toro2, Christoph Aberle3

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Quantitative radiomics features are crucial for medical diagnosis and research. This study developed a CT simulator to assess radiomics feature stability, showing its effectiveness in evaluating feature variability and discriminative power.

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Area of Science:

  • Medical Imaging
  • Radiomics
  • Computational Science

Background:

  • Quantitative imaging features have evolved from disputable to increasingly valuable in clinical research and diagnosis.
  • Advancements in machine learning and automated extraction techniques have enhanced the utility of radiomics features for tissue characterization.
  • The stability of quantitative radiomics features remains a significant challenge due to sensitivity to acquisition variations.

Purpose of the Study:

  • To develop and validate a Computed Tomography (CT) simulator for assessing radiomics feature stability.
  • To evaluate the performance of the simulator by comparing its results to phantom studies.
  • To demonstrate the simulator's utility in quantifying radiomics feature variability and discriminative power.

Main Methods:

  • Development of a CT simulator environment utilizing the ASTRA toolbox.
  • Generation of virtual phantom images using the developed CT simulator.
  • Extraction and analysis of radiomics features from simulated and phantom-acquired images.
  • Comparison of feature variability, stability, and discriminative power between simulated and physical phantom studies.

Main Results:

  • The radiomics features extracted from simulated images showed similar variability, stability, and discriminative power compared to a tandem phantom study.
  • The variability observed in simulated results closely matched that of a multi-center phantom study.
  • The developed CT simulator proved effective in assessing radiomics features' stability and discriminative power.

Conclusions:

  • The validated CT simulator provides a reliable environment for assessing radiomics feature stability and discriminative power.
  • The simulator can aid in understanding and mitigating the impact of acquisition variations on radiomics analysis.
  • This tool facilitates the development and validation of robust radiomics methodologies for clinical applications.