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Updated: Aug 3, 2025
Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
Published on: December 15, 2014
Application of advanced diffusion models from diffusion weighted imaging in a large cohort study of breast lesions
Ying Ji1, Junqi Xu2, Zilin Wang1
1Department of Radiology, Shanghai General Hospital, Shanghai Jiaotong University School of Medicine, No. 650, New Songjiang Road, Shanghai, 201620, China.
Background:
To evaluate multiple parameters in multiple b-value diffusion-weighted imaging (DWI) in characterizing breast lesions and predicting prognostic factors and molecular subtypes.
Methods:
In total, 504 patients who underwent 3-T magnetic resonance imaging (MRI) with T1-weighted dynamic contrast-enhanced (DCE) sequences, T2-weighted sequences and multiple b-value (7 values, from 0 to 3000 s/mm2) DWI were recruited. The average values of 13 parameters in 6 models were calculated and recorded. The pathological diagnosis of breast lesions was based on the latest World Health Organization (WHO) classification.
Results:
Twelve parameters exhibited statistical significance in differentiating benign and malignant lesions. alpha demonstrated the highest sensitivity (89.5%), while sigma demonstrated the highest specificity (77.7%). The stretched-exponential model (SEM) demonstrated the highest sensitivity (90.8%), while the biexponential model demonstrated the highest specificity (80.8%). The highest AUC (0.882, 95% CI, 0.852-0.912) was achieved when all 13 parameters were combined. Prognostic factors were correlated with different parameters, but the correlation was relatively weak. Among the 6 parameters with significant differences among molecular subtypes of breast cancer, the Luminal A group and Luminal B (HER2 negative) group had relatively low values, and the HER2-enriched group and TNBC group had relatively high values.
Conclusions:
All 13 parameters, independent or combined, provide valuable information in distinguishing malignant from benign breast lesions. These new parameters have limited meaning for predicting prognostic factors and molecular subtypes of malignant breast tumors.
