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AI-generated virtual contrast-enhanced (VAbCE) breast MRI shows promise for lesion detection, improving inter-rater agreement and sensitivity compared to unenhanced images. This approach offers a potential new method for breast cancer screening applications.

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Area of Science:

  • Radiology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Maximum intensity projections (MIPs) are crucial for rapid lesion detection in breast MRI using contrast-enhanced (CE) and diffusion-weighted imaging (DWI).
  • Evaluating AI-based virtual CE subtraction MIPs as a feasible reading approach is essential for improving diagnostic efficiency.

Purpose of the Study:

  • To assess the feasibility of AI-generated virtual abbreviated contrast-enhanced (VAbCE) subtraction MIPs for breast MRI lesion detection.
  • To compare the diagnostic performance, image quality, and artifact presence of VAbCE MIPs against unenhanced (UnE) and abbreviated CE (AbCE) MIPs.

Main Methods:

  • A retrospective study of 540 multi-parametric breast MRI exams (2017-2020) with multi-b-value DWI.
  • A 2D U-Net model was trained to generate VAbCE subtractions from UnE images.
  • Two radiologists evaluated lesion suspicion, image quality, artifacts, and lesion conspicuity for UnE, VAbCE, and AbCE images.

Main Results:

  • Cancer detection rates were 90.0% (UnE), 91.4% (VAbCE), and 94.3% (AbCE).
  • VAbCE MIPs demonstrated higher inter-rater agreement (Cohen κ=0.53) than UnE (0.39), comparable to AbCE (0.58).
  • No significant differences in conspicuity, quality, or reading time were found between VAbCE and AbCE; VAbCE had fewer artifacts.

Conclusions:

  • AI-based VAbCE breast MRI enhances inter-rater agreement and offers slightly improved sensitivity over UnE images, approaching AbCE sensitivity.
  • VAbCE MIPs derived from neural networks enable rapid visual assessment, suggesting potential for screening applications.
  • Further research is needed to fully explore the diagnostic capabilities of VAbCE breast MRI.