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Related Concept Videos

Imaging Studies IV: Magnetic Resonance Imaging01:27

Imaging Studies IV: Magnetic Resonance Imaging

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,...
Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

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...
Proteomics01:33

Proteomics

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.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term proteomics...

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Related Experiment Video

Updated: Jul 9, 2026

A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound
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A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound

Published on: March 21, 2025

Magnetic Resonance Imaging-Based Artificial Intelligence in Predicting Prostate Cancer Biochemical Recurrence:

Yanjun Jin1, Tianzuo Yuan1, Zhiyuan Chen2,3

  • 1Faculty of Health Sciences, University of Macau, Avenida da Universidade, Macau, 999078, China.

Journal of Medical Internet Research
|July 7, 2026
PubMed
Summary
This summary is machine-generated.

This meta-analysis shows that artificial intelligence (AI) models using magnetic resonance imaging (MRI) accurately predict prostate cancer (PCa) recurrence, with comparable performance in both internal and external validation groups. Findings guide AI development and highlight the need for standardized protocols for clinical use.

Keywords:
artificial intelligencebiochemical recurrencemeta-analysispredictprostate cancer

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Use of Magnetic Resonance Imaging and Biopsy Data to Guide Sampling Procedures for Prostate Cancer Biobanking
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Published on: October 10, 2019

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Last Updated: Jul 9, 2026

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Use of Magnetic Resonance Imaging and Biopsy Data to Guide Sampling Procedures for Prostate Cancer Biobanking
05:49

Use of Magnetic Resonance Imaging and Biopsy Data to Guide Sampling Procedures for Prostate Cancer Biobanking

Published on: October 10, 2019

Area of Science:

  • Oncology
  • Radiology
  • Artificial Intelligence
  • Medical Informatics

Background:

  • Artificial intelligence (AI) shows promise for prostate cancer (PCa) risk stratification and outcome prediction.
  • Existing studies often lack external validation, have small sample sizes, and exhibit intermodel variability and overfitting concerns.

Purpose of the Study:

  • To comprehensively evaluate the diagnostic performance of MRI-based AI models in predicting biochemical recurrence (BCR) of PCa.
  • To compare the performance of AI models between internal and external validation cohorts.

Main Methods:

  • Systematic literature search of PubMed, Embase, Web of Science, and Cochrane Library databases.
  • Inclusion of studies using MRI-based AI for PCa BCR prediction with defined reference standards.
  • Quality assessment using PROBAST+AI and meta-analysis using a bivariate random effects model to pool sensitivity, specificity, and AUC.

Main Results:

  • 28 studies with 2623 patients (internal) and 1134 patients (external) were included.
  • Pooled performance metrics showed high accuracy: Internal validation (Sensitivity: 0.80, Specificity: 0.83, AUC: 0.86) and External validation (Sensitivity: 0.82, Specificity: 0.83, AUC: 0.84).
  • No significant differences were found between internal and external validation metrics; AI method and MRI acquisition timing were identified as sources of heterogeneity.

Conclusions:

  • MRI-based AI models demonstrate robust and comparable performance for PCa BCR prediction in both internal and external validation settings.
  • Identified key factors influencing performance heterogeneity, such as AI method and MRI acquisition timing.
  • Emphasizes the need for standardized imaging protocols and prospective multicenter studies for safe clinical translation of AI tools.