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Computer-aided detection (CADe) systems significantly improve breast cancer detection using MRI. While deep learning advances show promise, challenges in accuracy, generalizability, and interpretability require further research for clinical integration.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Healthcare
  • Oncology

Background:

  • Breast cancer remains a significant health issue, underscoring the need for effective early detection methods.
  • Magnetic resonance imaging (MRI) offers high sensitivity for detecting invasive breast cancers, crucial for improving patient outcomes.
  • Computer-aided detection (CADe) systems enhance MRI efficacy by identifying suspicious lesions and assisting radiologists.

Purpose of the Study:

  • To provide a comprehensive review of current computer-aided detection (CADe) systems in breast MRI.
  • To analyze the technical aspects of CADe pipelines and segmentation models, from classical methods to deep learning.
  • To identify challenges and future directions for CADe systems in clinical breast MRI practice.

Main Methods:

  • Review of technical pipelines and segmentation models in breast MRI CADe.
  • Analysis of classical intensity-based methods, machine learning (ML), and deep learning (DL) architectures.
  • Examination of CADe implementation with multi-parametric MRI acquisitions.

Main Results:

  • Advancements range from traditional algorithms to sophisticated DL models like U-Nets.
  • Current CADe systems face challenges including variable accuracy rates, data interpretation complexity, and performance variability.
  • Technical hurdles include image artifacts and the need for explainable detection algorithms.

Conclusions:

  • Despite progress, CADe systems require more robust, generalizable, and interpretable algorithms for clinical adoption.
  • Future research should focus on explainable AI, multi-purpose AI, and integration with large language models for enhanced reporting.
  • Standardizing MRI protocols and reducing costs are essential for optimizing CADe system accessibility and clinical utility.