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Magnetic Resonance Imaging01:24

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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 13, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
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MRI-Derived Radiomics For Classifying Breast Cancer Molecular Subtypes: a Modeling Approach.

Tran Thi Hue1,2, Nguyen Thu Huong2, Tran Quoc Long3

  • 1Department of Radiology, Hanoi Medical University, Hanoi, Vietnam.

Acta Informatica Medica : AIM : Journal of the Society for Medical Informatics of Bosnia & Herzegovina : Casopis Drustva Za Medicinsku Informatiku Bih
|November 24, 2025
PubMed
Summary

MRI radiomics can predict breast cancer molecular subtypes, showing high accuracy for triple-negative breast cancer (TNBC) and HER2-enriched types. While distinguishing luminal A (LA) and luminal B (LB) remains challenging, this method aids precision oncology.

Keywords:
MRIbreast cancermolecular subtypesradiomics

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Area of Science:

  • Oncology
  • Radiology
  • Medical Imaging

Background:

  • Breast cancer is a heterogeneous disease with four main molecular subtypes impacting prognosis and treatment.
  • Accurate subtype identification is crucial for effective patient management.
  • MRI-based radiomics offers a non-invasive approach to assess tumor heterogeneity and predict subtypes.

Purpose of the Study:

  • To develop and validate a logistic-regression model using MRI-derived radiomic features.
  • To predict the four major molecular subtypes of invasive breast cancer: luminal A (LA), luminal B (LB), HER2-enriched (HER2), and triple-negative breast cancer (TNBC).

Main Methods:

  • Retrospective analysis of 169 invasive breast carcinoma patients who underwent pre-treatment DCE-MRI.
  • Extraction and z-score normalization of radiomic texture features using LIFEx.
  • Feature selection via L1-regularized logistic regression (LASSO) and training of four one-vs-rest logistic-regression models with 5-fold cross-validation.

Main Results:

  • The models achieved AUCs of 0.840 for TNBC, 0.788 for HER2, 0.661 for LA, and 0.635 for LB.
  • Highest accuracy was observed for TNBC (0.923), with lowest sensitivity for LB (0.393).
  • Good classification performance for TNBC and HER2, but frequent misclassification between LA and LB. TNBC features were primarily intensity- and entropy-based.

Conclusions:

  • MRI-derived radiomic signatures can non-invasively differentiate breast cancer molecular phenotypes.
  • The model demonstrates strong predictive performance for TNBC and HER2 subtypes.
  • The LASSO-logistic-regression framework shows potential as a decision-support tool in precision oncology, despite limitations in LA-LB differentiation.