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Related Experiment Video

Updated: Apr 27, 2026

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
15:48

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Published on: December 15, 2014

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Beyond Pathology: MRI-derived Metrics Unlock Preoperative Prognostic Risk Stratification in Breast Cancer.

Mehrad Zare1, Alisa Mohebbi1, Afshin Mohammadi2

  • 1School of Medicine, Tehran University of Medical Sciences, Tehran, Iran; Association of Nuclear Medicine and Molecular Imaging (ANMMI), Universal Scientific Education and Research Network (USERN), Tehran, Iran.

Clinical Breast Cancer
|April 25, 2026
PubMed
Summary

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This summary is machine-generated.

Magnetic resonance imaging (MRI) can predict breast cancer (BC) prognosis before surgery, offering immediate risk stratification. This noninvasive approach bypasses the need for postoperative pathology, enabling faster treatment decisions.

Area of Science:

  • Radiology
  • Oncology
  • Medical Imaging

Background:

  • The Nottingham Prognostic Index (NPI) is the standard for breast cancer (BC) risk stratification but requires postoperative pathology, delaying treatment.
  • Magnetic resonance imaging (MRI) offers potential for preoperative risk assessment, enabling immediate classification without histology.

Purpose of the Study:

  • To evaluate the efficacy of MRI in predicting high-risk NPI grades preoperatively.
  • To determine if quantitative and categorical MRI features can serve as noninvasive prognostic indicators for breast cancer.

Main Methods:

  • A retrospective analysis of 791 invasive BC patients using quantitative (size, volume, enhancement) and categorical (lymphadenopathy, multicentricity) MRI features.
  • Receiver operating characteristic analysis and multivariable logistic regression were used to assess discriminative performance, stratified by molecular subtype, tumor size, and breast density.
Keywords:
GradingMagnetic resonance imaging (MRI)Nottingham Prognostic Index (NPI)Prognosis

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Main Results:

  • Quantitative MRI metrics, including tumor size (AUC 0.855) and enhancing volume (AUC 0.867), showed strong discriminatory power.
  • Performance varied by molecular subtype; volumetric metrics were superior for HER2-enriched tumors (AUC 0.965), while washout kinetics excelled in triple-negative disease (AUC 0.932).
  • A combined model using tumor size, lymphadenopathy, and multicentricity achieved an AUC of 0.883 with 89.8% sensitivity and 72.0% specificity, robust across breast densities.

Conclusions:

  • MRI is a potent noninvasive tool that accurately reflects pathological NPI, facilitating preoperative risk-stratified counseling and treatment planning.
  • Tailoring MRI interpretation to molecular subtypes supports a precision medicine approach for individualized, preoperative breast cancer management.