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Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
Published on: December 15, 2014
Added value of diffusion tensor magnetic resonance imaging in prediction of luminal versus non-luminal breast cancer:
E Alnaghy1, A G Sadek1, R Khaled1
1Radiology Department, Faculty of Medicine, Mansoura University, Egypt.
Introduction:
our purpose was to assess the role of diffusion tensor (DT) Magnetic Resonance Imaging (MRI) in prediction of luminal versus non-luminal breast cancer in correlation with pathological findings.
Methods:
This prospective study included 89 consecutive female patients (mean age 48.1 years) diagnosed with breast cancer, they were categorized into luminal (n 72) and non-luminal type (n 17) cases. They underwent breast MRI with DTI. DTI metrics - mean diffusivity (MD), fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD), and relative anisotropy (RA) - were measured for breast lesions and correlated with histological findings.
Results:
The mean MD and RD values were marginally significantly higher in non-luminal vs. luminal cases, (1 × 10-3 mm2/s vs. 1.2 × 10-3 mm2/s), and (1 × 10-3 mm2/s vs. 1.2 × 10-3 mm2/s), p value = 0.059 and 0.051, respectively. FA, AD, and RA values were not significantly different between both groups. Participants with MD > 1.1 had 3.1-times higher odds to exhibit non-luminal type. Others with RD > 1.19 had 3.2-times higher odds to exhibit non-luminal type. Correlation of the DTI parameters with the pathological data revealed that MD and AD were significantly lower in ER positive vs. ER negative cases. AD was low in cases with positive LVI. Cases associated with DCIS had significantly low FA and high MD, AD and RD values.
Conclusion:
DTI parameters can be useful in discriminating luminal vs. non-luminal breast cancer, some of these parameters are well correlated with the important pathological findings.
Implications For Practice:
Using different DTI parameters can enhance the accuracy of breast cancer characterization reducing unnecessary biopsies, provide valuable data to correlate with tumor grade, differentiate luminal from non-luminal lesions, thus providing insights into the aggressiveness of the tumor.
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