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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,...
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Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

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Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
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Related Experiment Video

Updated: Jul 19, 2025

Use of MRI-ultrasound Fusion to Achieve Targeted Prostate Biopsy
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Advancements in MRI-Based Radiomics and Artificial Intelligence for Prostate Cancer: A Comprehensive Review and

Ahmad Chaddad1,2, Guina Tan1, Xiaojuan Liang1

  • 1School of Artificial Intelligence, Guilin Universiy of Electronic Technology, Guilin 541004, China.

Cancers
|August 12, 2023
PubMed
Summary
This summary is machine-generated.

Multiparametric MRI (mpMRI) guides prostate cancer care. Radiomics, using AI and imaging data, offers a non-invasive approach for personalized diagnosis and treatment planning, reducing the need for invasive procedures.

Keywords:
Gleason scorempMRIprostate cancerradiomics

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Area of Science:

  • Radiology
  • Oncology
  • Artificial Intelligence in Medicine

Background:

  • Multiparametric magnetic resonance imaging (mpMRI) is standard for prostate lesion assessment.
  • Radiomics extracts quantitative imaging features for predictive modeling.
  • Minimizing invasive procedures is crucial for prostate cancer (PCa) management.

Purpose of the Study:

  • To review advancements in MRI-based radiomics for PCa.
  • To explore the radiomics pipeline and factors influencing personalized diagnosis.
  • To discuss the integration of AI, radiogenomics, and multi-omics in PCa research.

Main Methods:

  • Literature review of recent research in MRI-based radiomics for PCa.
  • Analysis of the radiomics pipeline, including feature extraction and model development.
  • Discussion on the role of artificial intelligence (AI) and multi-omics data.

Main Results:

  • Radiomics shows promise for non-invasive PCa diagnosis and treatment planning.
  • AI integration enhances predictive model capabilities.
  • The need for multi-institutional data to generalize predictive models is highlighted.

Conclusions:

  • AI-based radiomics models are promising clinical tools for prostate cancer.
  • Further research with diverse datasets is required for robust, generalizable models.
  • This approach supports personalized medicine and improved patient outcomes.