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Artificial intelligence-generated apparent diffusion coefficient (AI-ADC) maps for prostate gland assessment: a

Kutsev Bengisu Ozyoruk1,2, Stephanie A Harmon1,2, Enis C Yilmaz1,2

  • 1Artificial Intelligence Resource, Molecular Imaging Branch, National Cancer Institute, Bethesda, MD, USA.

European Radiology
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Summary
This summary is machine-generated.

Artificial intelligence-generated apparent diffusion coefficient (AI-ADC) maps show superior image quality in prostate cancer detection compared to standard ADC maps. This AI-ADC technology offers improved delineation and reduced artifacts, potentially enhancing diagnostic accuracy.

Keywords:
ADC mapGenerative artificial intelligenceMagnetic resonance imagingProstate

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Radiology
  • Prostate Cancer Diagnostics

Background:

  • Apparent diffusion coefficient (ADC) maps are crucial in prostate cancer (PCa) diagnosis using multiparametric MRI (mpMRI).
  • Image quality limitations in standard ADC maps can hinder accurate interpretation and diagnosis.
  • The potential of artificial intelligence (AI) to enhance medical imaging quality is an area of active research.

Purpose of the Study:

  • To compare the image quality of AI-generated ADC (AI-ADC) maps against standard ADC maps.
  • To evaluate the impact of AI-ADC maps on reader performance and diagnostic confidence in a multi-reader setting.
  • To assess the clinical relevance of AI-ADC maps for prostate cancer detection.

Main Methods:

  • A multi-reader study involving 74 patients with suspected or confirmed PCa who underwent mpMRI.
  • Four readers evaluated T2W-MRI and either standard ADC or AI-ADC maps in two rounds with a washout period.
  • Statistical analyses included Fleiss' kappa, quadratic-weighted Cohen's kappa, and linear mixed-effect models to compare map quality and inter-reader agreement.

Main Results:

  • AI-ADC maps demonstrated significantly higher ratings for windowing ease, prostate boundary delineation, and reduction in distortion and noise compared to standard ADC maps.
  • AI-ADC maps led to a significant reduction in reacquisition requirements across all readers, suggesting improved workflow efficiency.
  • No significant differences were found in inter-reader agreement between AI-ADC and standard ADC maps.

Conclusions:

  • AI-ADC maps offer superior image quality, including better prostate boundary delineation and reduced artifacts, compared to standard ADC maps.
  • The enhanced quality of AI-ADC maps supports their potential as a reliable diagnostic tool, especially in cases with acquired ADC map artifacts.
  • AI-ADC maps show promise for improving diagnostic accuracy and workflow efficiency in prostate cancer assessment via mpMRI.