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Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
Published on: December 15, 2014
Diagnostic Performance of Diffusion-Weighted Imaging for Evaluation of Breast Lesions: Correlation With Prognostic
Osman Konukoglu1, Murat Kaya1, Baris Can Arslan1
1Department of Radiology, Gaziantep City Hospital, Gaziantep, Turkey.
Purpose:
To investigate the role of diffusion-weighted imaging (DWI) in the differential diagnosis of breast lesions and to determine whether there is an association with histological subtypes and prognostic factors.
Materials & Methods:
This retrospective study was approved by our institution's ethics committee. A total of 456 patients who underwent ultrasound-guided tru-cut biopsy between January 2024 and January 2025 were evaluated. Patients who had pre-biopsy breast MRI examinations with diagnostically adequate DWI sequences were included. Apparent diffusion coefficient values (ADCmean and ADCratio) were measured.
Results:
The mean age of the patients was 46.79 ± 12.55 years. A total of 96 breast masses were evaluated in the study, of which 44.79% (n = 43) were benign, and 55.21% (n = 53) were malignant. A statistically significant difference was observed between the benign group (1.22 ± 0.24 × 10-3 mm2/s) and the malignant group (0.76 ± 0.18 × 10-3 mm2/s) in terms of ADCmean values (p < 0.001). The cut-off value for the ADCmean was determined to be 0.955 × 10-3 mm2/s (p < 0.001). No statistically significant association was found among histological phenotypes within the malignant group (p > 0.05). A statistically significant difference in ADCratio values was observed between HER2-negative cases (0.56 ± 0.12) and HER2-positive cases (0.49 ± 0.14) (p = 0.043).
Conclusion:
DWI and ADC represent reliable methods for distinguishing between benign and malignant breast lesions. The observed association between ADCratio values and HER2 expression suggests that this parameter may be useful in identifying breast cancer patients with poor prognostic profiles.

