The aim of this study is to evaluate the diagnostic efficacy of 3.0T MRI diffusion kurtosis imaging and quantitative dynamic contrast enhancement in benign and malignant breast lesions, and to explore the differential diagnosis ability of different pathological types and molecular subtype lesions.
Objective
To evaluate the diagnostic efficacy of 3.0T MRI diffusion kurtosis imaging and quantitative dynamic contrast enhancement in benign and malignant breast lesions, and to explore the differential diagnosis ability of different pathological types and molecular subtype lesions.Methods
Sixty-four females were enrolled in the study of MRI diffusion kurtosis imaging and quantitative dynamic contrast enhancement between November 20 and 2017 in May, respectively. All of them were confirmed benign and malignant after surgical resection or puncture. The MK and MD values were calculated by the DKI model, and the hemodynamic parameters were obtained by quantitative dynamic contrast enhancement, including Ktrans, Kep, Ve and Vp. The DKI parameters and DCE quantitative parameters were compared between different molecular subtypes and prognostic factors.Results
The MK and MD values of breast cancer were 0.879±0.176和1.248±0.380*10-3mm2/s respectively in the DKI model. The MK and MD values of benign lesions were 0.577±0.142和1.792±0.505*10-3mm2/s , the difference was statistically significant (P <0.05). In the DCE model, the Ktrans and Kep mean values of breast cancer were 0.101 ± 0.039min-1 and 0.398 ± 0.194 min-1, respectively. The mean values of Ktrans and Kep of benign lesions were 0.062 ± 0.067 min-1 and 0.210 ± 0.121 min-1, respectively. The difference was statistically significant (p <0.05), and the other DCE parameters were not statistically significant. The highest sensitivity, specificity and accuracy of MK in all parameters were 91.3%, 80.5% and 84.4% respectively. Spearman correlation analysis showed that ER was Moderately positively correlated with Ktrans and Vp, and lymph node expression was low positively correlated with Ve (p = 0.001, 0.016, 0.042). There was no significant correlation between parameters and subtype.Conclusion
3.0T MRI diffusion kurtosis imaging and dynamic contrast-enhanced quantitative hemodynamic parameters are of great value in the differential diagnosis of benign and malignant breast lesions. The combination of both can significantly improve the diagnostic efficiency. There is a certain correlation between some parameters and prognostic factors in DCE model.1. Partridge SC, McDonald ES. Diffusion weighted magnetic resonance imaging of the breast: protocol optimization, interpretation, and clinical applications[J]. Magnetic resonance imaging clinics of North America 2013;21:601-24. PMID: 239282482.
2.Kato F, Kudo K, Yamashita H, et al. Differences in morphological features and minimum apparent diffusion coefficient values among breast cancer subtypes using 3-tesla MRI[J]. European journal of radiology 2016;85:96-102. PMID: 267246533.
3.Bickelhaupt S, Laun FB, Tesdorff J, et al. Fast and Noninvasive Characterization of Suspicious Lesions Detected at Breast Cancer X-Ray Screening: Capability of Diffusion-weighted MR Imaging with MIPs[J]. Radiology 2016;278:689-97. PMID: 264185164.
4.Zaric O, Pinker K, Zbyn S, et al. Quantitative Sodium MR Imaging at 7 T: Initial Results and Comparison with Diffusion-weighted Imaging in Patients with Breast Tumors[J]. Radiology 2016;280:39-48. PMID: 270078035.
5.Nogueira L, Brandao S, Matos E, et al. Application of the diffusion kurtosis model for the study of breast lesions[J]. European radiology 2014;24:1197-203. PMID: 246588716.
6.Jensen JH, Helpern JA, Ramani A, Lu H, Kaczynski K. Diffusional kurtosis imaging: the quantification of non-gaussian water diffusion by means of magnetic resonance imaging[J]. Magnetic resonance in medicine 2005;53:1432-40. PMID: 159063007.
7.Sun K, Chen X, Chai W, et al. Breast Cancer: Diffusion Kurtosis MR Imaging-Diagnostic Accuracy and Correlation with Clinical-Pathologic Factors[J]. Radiology 2015;277:46-55. PMID: 259386798.
8.Wu D, Li G, Zhang J, Chang S, Hu J, Dai Y. Characterization of breast tumors using diffusion kurtosis imaging (DKI) [J]. PloS one 2014;9:e113240. PMID: 25406010