The Evaluation of Combined Diffusion Kurtosis Imaging and Intravoxel Incoherent Motion Diffusion Weighted Imaging in Preoperatively Predicting Sentinel Lymph Node Metastasis of Breast Cancer
Sep 2026· International Journal of General Medicine· Vol 19· 0 citations· 39 references
Medicine
TL;DR
A combined IVIM-DKI model provided better diagnostic performance than individual parameters and may serve as a useful noninvasive adjunct for preoperative axillary assessment.
Abstract
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.
Both DWI parameters (especially ADC and D and TIC type) and TIC type help differentiate benign from malignant BI-RADS 4 lesions and the ADC combined with TIC model offers better diagnostic accuracy and is practical for clinical use.
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