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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 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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A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
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Related Experiment Video

Updated: Nov 18, 2025

Use of Magnetic Resonance Imaging and Biopsy Data to Guide Sampling Procedures for Prostate Cancer Biobanking
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Magnetic Resonance Imaging Based Radiomic Models of Prostate Cancer: A Narrative Review.

Ahmad Chaddad1,2, Michael J Kucharczyk3, Abbas Cheddad4

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

Cancers
|February 4, 2021
PubMed
Summary

Deep radiomics, an artificial intelligence approach, enhances prostate cancer (PCa) grading by overcoming traditional radiomic model limitations. This AI integration promises improved personalized PCa treatment strategies.

Keywords:
Gleason scoreartificial intelligencemagnetic resonance imagingprostate cancerradiogenomicsradiomics

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Area of Science:

  • Radiology
  • Oncology
  • Artificial Intelligence

Background:

  • Prostate cancer (PCa) management relies on accurate tumor grading, traditionally requiring invasive biopsies.
  • Current radiomic models face challenges in image acquisition, data size, processing, and feature extraction.

Purpose of the Study:

  • To review the application of magnetic resonance imaging (MRI)-based radiomic models for PCa evaluation.
  • To explore how deep radiomics optimizes PCa grade group prediction and mitigates traditional radiomic challenges.
  • To discuss AI's impact on personalized PCa treatment and future research directions.

Main Methods:

  • Review of technical processes for MRI-based radiomic models in PCa.
  • Exploration of deep radiomics, a deep texture analysis using convolutional neural network features.
  • Analysis of existing clinical work and upcoming trials in AI for PCa.

Main Results:

  • Deep radiomics integration addresses key technological hurdles in traditional radiomics.
  • AI-driven radiomics shows potential for optimizing PCa grade group prediction.
  • The review highlights the role of deep texture analysis in feature extraction.

Conclusions:

  • Deep radiomics offers a promising approach to enhance PCa evaluation and personalized treatment.
  • Overcoming data and processing challenges is crucial for clinical translation.
  • Multi-institutional collaboration is essential for building robust imaging libraries for AI development.