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Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
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Incorporating diffusion-weighted imaging in a diagnostic algorithm for multiparametric MR mammography.

Katerina Vassiou1, Michael Fanariotis2,3, Ioannis Tsougos4

  • 1Department of Anatomy, Faculty of Medicine, 37786University of Thessaly, Biopolis, Larissa, Greece.

Acta Radiologica (Stockholm, Sweden : 1987)
|October 4, 2021
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Summary

Combining apparent diffusion coefficient (ADC) measurements with BI-RADS classification in 3 Tesla magnetic resonance mammography (MRM) improves breast lesion malignancy assessment. This integrated approach enhances both sensitivity and specificity for diagnosing breast cancer.

Keywords:
BI-RADSMagnetic resonance imagingapparent diffusion coefficientbreast neoplasmsdiffusion-weighted imaging

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Area of Science:

  • Radiology
  • Oncology
  • Medical Imaging

Background:

  • Apparent diffusion coefficient (ADC) measurements are not currently integrated into the Breast Imaging Reporting and Data System (BI-RADS) classification.
  • Magnetic resonance mammography (MRM) is a valuable tool for breast lesion evaluation.

Purpose of the Study:

  • To enhance the specificity of 3 Tesla MRM for breast lesion malignancy assessment.
  • To evaluate the combined diagnostic performance of ADC measurements and BI-RADS classification.

Main Methods:

  • A prospective study included 296 biopsy-proven breast lesions evaluated with 3 T MRM, including dynamic contrast-enhanced MRI (DCE-MRI) and echo-planar diffusion-weighted imaging.
  • Apparent diffusion coefficient (ADC) values were calculated, and lesions were classified using BI-RADS. Logistic regression combined BI-RADS and ADC values to predict malignancy.
  • Sensitivity, specificity, and diagnostic accuracy were calculated for BI-RADS alone and the combined method.

Main Results:

  • The study analyzed 153 malignant and 143 benign lesions, including masses and non-mass-like enhancements.
  • The combined method achieved 96% sensitivity and 86% specificity.
  • BI-RADS classification alone yielded 95% sensitivity and 81% specificity.

Conclusions:

  • A novel method combining BI-RADS classification and ADC values in a single formula improves the assessment of breast lesion malignancy probability.
  • This integrated approach offers increased sensitivity and specificity compared to using BI-RADS classification alone for MRM evaluations.