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Related Concept Videos

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...
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,...

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Related Experiment Video

Updated: Jun 13, 2026

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
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MRI-based texture analysis for breast cancer subtype classification in a multi-ethnic population.

Nazimah Ab Mumin1,2, Chuin-Hen Liew3, Song-Quan Ong4

  • 1Department of Radiology, Faculty of Medicine, Universiti Teknologi MARA, 47000, Sungai Buloh, Selangor, Malaysia. nazimah_mumin@uitm.edu.my.

Magma (New York, N.Y.)
|August 12, 2025
PubMed
Summary

This study shows that MRI radiomics and machine learning can non-invasively classify breast cancer subtypes. Texture features from MRI effectively predict molecular subtypes, aiding personalized treatment.

Keywords:
BreastBreast neoplasmLuminal, HER2-enriched, Triple-negative breast cancerMachine learningMagnetic resonance imaging

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Area of Science:

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

Background:

  • Breast cancer subtyping is crucial for treatment and prognosis, but traditional methods are invasive.
  • Magnetic Resonance Imaging (MRI) radiomics offers a non-invasive approach to extract quantitative imaging features.
  • Limited research exists on MRI radiomics for breast cancer subtyping in diverse populations.

Purpose of the Study:

  • To identify predictive radiomic features from multiple MRI sequences for breast cancer subtype classification.
  • To compare the performance of four MRI sequences in breast cancer subtyping.
  • To determine the optimal machine learning (ML) model for non-invasive breast cancer subtyping.

Main Methods:

  • Retrospective analysis of 162 breast cancer MRI cases with semi-automated segmentation.
  • Extraction of 256 radiomic features from multiple MRI sequences.
  • Development of a multimodal ML framework using random forest and recursive feature elimination for feature selection based on AUROC.

Main Results:

  • Key predictive features included patient age, tumor size, margin characteristics, and intra-tumoral intensity patterns.
  • Inversion recovery and T1 post-contrast MRI sequences demonstrated superior performance for subtyping.
  • Texture-based ML models achieved AUROC values of 0.735 (luminal), 0.630 (HER2-enriched), and 0.747 (triple-negative), emulating visual assessment.

Conclusions:

  • MRI-based texture features and advanced ML show significant potential for enhancing breast cancer diagnosis.
  • Radiomics provides a non-invasive tool for personalized treatment planning in breast cancer.
  • This approach can complement existing clinical workflows for breast cancer management.