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Updated: Sep 26, 2026

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
Published on: December 15, 2014
The Evaluation of Combined Diffusion Kurtosis Imaging and Intravoxel Incoherent Motion Diffusion Weighted Imaging in
Xiaoxiao Li1,2, Junfang Fang1, Xuexue Zou1
1Department of Radiology, Binzhou Medical University Hospital, Binzhou, Shandong, 256699, People's Republic of China.
Purpose:
To evaluate the diagnostic performance of quantitative diffusion kurtosis imaging (DKI) and intravoxel incoherent motion (IVIM) parameters for preoperative, noninvasive detection of sentinel lymph node metastasis (SLNM) in breast cancer.
Methods And Materials:
Clinicopathologic and MRI data from 61 patients with histologically confirmed breast cancer were reviewed, including 32 patients with SLNM and 29 without SLNM. All patients underwent multi-b-value diffusion-weighted imaging at 3.0 T. Images were analyzed using simplified DKI and biexponential IVIM models on the iCare SpinX workstation. Derived parameters included the diffusion coefficient D, kurtosis K, pseudodiffusion coefficient D*slow diffusion coefficient Dslow, and perfusion fraction f. Group differences were assessed using independent-samples t-tests and logistic regression. Diagnostic performance was evaluated using receiver operating characteristic analysis.
Results:
Compared with the non-SLNM group, the SLNM group had a significantly higher f value (0.551 ± 0.108 vs 0.453 ± 0.115; P=0.001) and a significantly lower D value (0.0010 ± 0.0002 vs 0.0012 ± 0.0003 mm2/s; P<0.001). D*Dslow, and K did not differ significantly between groups. In univariate logistic regression, f (P=0.004) and D (P=0.003) were significant predictors of SLNM. D showed an area under the curve of 0.815, with 84.4% sensitivity and 75.9% specificity. The combined IVIM-DKI model achieved the best performance, with an area under the curve of 0.855, 75.0% sensitivity, and 89.7% specificity.
Conclusion:
Quantitative DKI and IVIM parameters may help identify SLNM preoperatively in patients with breast cancer. A combined IVIM-DKI model provided better diagnostic performance than individual parameters and may serve as a useful noninvasive adjunct for preoperative axillary assessment.
