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Artificial Intelligence Based Algorithms for Prostate Cancer Classification and Detection on Magnetic Resonance

Jasper J Twilt1, Kicky G van Leeuwen1, Henkjan J Huisman1

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Artificial intelligence (AI) shows promise for prostate cancer (PCa) diagnosis using MRI. However, current AI applications need more validation for clinical use, focusing on robustness and generalizability.

Keywords:
artificial intelligencecomputer-aided diagnosisdeep learningmachine learningmagnetic resonance imagingprostate neoplasmsradiomics

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Magnetic resonance imaging (MRI) is crucial for prostate cancer (PCa) diagnosis.
  • Numerous artificial intelligence (AI) applications have been developed to assist in PCa detection and diagnosis.
  • A review of studies from 2018 to February 2021 highlights the evolving landscape of AI in PCa assessment.

Purpose of the Study:

  • To provide a comprehensive overview of AI algorithms for PCa lesion classification and detection.
  • To evaluate the current state of AI research in PCa diagnosis based on published studies.
  • To identify limitations and future directions for AI applications in clinical PCa assessment.

Main Methods:

  • Systematic review of scientific literature focusing on AI in PCa diagnosis.
  • Analysis of 59 studies published between 2018 and February 2021.
  • Categorization of AI applications into lesion classification and lesion detection tasks.

Main Results:

  • The majority of research focused on PCa lesion classification (66%) over lesion detection (34%).
  • Significant heterogeneity was observed in study cohort sizes (18-499 patients) and validation methods.
  • Most studies (85%) reported standalone diagnostic accuracy, with limited evidence (15%) on AI's impact on clinical decision-making.

Conclusions:

  • Current AI applications for PCa show limited proof of clinical utility due to a lack of demonstrated impact on diagnostic efficacy.
  • Further validation of AI robustness and generalizability is essential for integration into clinical workflows.
  • External validation and clinical workflow experiments are crucial for advancing PCa AI applications.