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Related Concept Videos

Imaging Studies IV: Magnetic Resonance Imaging01:27

Imaging Studies IV: Magnetic Resonance Imaging

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Introduction:Magnetic Resonance Imaging, or MRI, can include a specialized imaging technique of the urinary system known as Magnetic Resonance Urography (MRU). This radiation-free technique uses strong magnetic fields and radio waves to produce detailed images with the help of a computer. MRU is particularly effective for visualizing fluid-filled structures like the kidneys, ureters, and bladder.Applications of MRI in the Genitourinary SystemKidneys and Ureters: MRI detects tumors, cysts,...
216

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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
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Next-Generation Advances in Prostate Cancer Imaging and Artificial Intelligence Applications.

Kathleen H Miao1, Julia H Miao2, Mark Finkelstein1

  • 1Department of Diagnostic, Molecular, and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.

Journal of Imaging
|November 26, 2025
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) is revolutionizing prostate cancer imaging, enhancing detection and treatment planning across MRI, PET/CT, and ultrasound. While challenges remain, AI integration promises more accurate diagnostics and personalized care for prostate cancer patients.

Keywords:
PI-RADSPSMA PET/CTartificial intelligencemachine learningmultiparametric MRIprostate cancertransrectal ultrasound

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Area of Science:

  • Radiology and Oncology
  • Medical Imaging Informatics
  • Artificial Intelligence in Medicine

Background:

  • Prostate cancer is a major global health concern, with imaging crucial for diagnosis and management.
  • Artificial intelligence (AI) offers significant potential to improve accuracy, efficiency, and consistency in prostate imaging.

Purpose of the Study:

  • To review the integration and impact of AI in prostate cancer diagnostics.
  • To explore AI applications across various imaging modalities and their clinical utility.

Main Methods:

  • Review of AI applications in multiparametric MRI (mpMRI), PSMA PET/CT, and transrectal ultrasound (TRUS).
  • Analysis of AI techniques including machine learning, deep learning, and radiomics for lesion detection, segmentation, and treatment planning.
  • Evaluation of AI's role in PI-RADS scoring, biopsy targeting, and radiation therapy optimization.

Main Results:

  • AI demonstrates enhanced capabilities in lesion detection, risk stratification, and automated segmentation.
  • AI-assisted tools improve PI-RADS scoring accuracy and guide targeted biopsies effectively.
  • AI shows potential in optimizing radiation therapy planning and delivery for prostate cancer.

Conclusions:

  • AI integration in prostate imaging shows transformative potential for diagnosis and treatment.
  • Challenges in data heterogeneity, generalizability, and clinical implementation need to be addressed.
  • Multimodal AI models and multidisciplinary collaboration are key to advancing precision medicine in prostate cancer care.