Histogram analysis of diffusion kurtosis imaging based on whole‐volume images of breast lesions

Abstract

Background Breast diffusion kurtosis imaging (DKI) is a novel MRI technique to assess breast cancer but the effectivity still remains to be improved. Purpose To investigate the performance of whole‐volume histogram parameters derived from a DKI model for differentiating benign and malignant breast lesions. Study Type Retrospective. Population In all, 120 patients with breast lesions (62 malignant, 58 benign). Sequence DKI sequence with seven b‐values (0, 500, 1000, 1500, 2000, 2500, and 3000 s/mm2) and DWI sequence with two b‐values (0 and 1000 s/mm2) on 3.0T MRI. Assessment Histogram parameters of the DKI model (K and D) and the DWI model (ADC), including the minimum, maximum, mean, percentile values (25th, 50th, 75th, and 95th), standard deviation, kurtosis and skewness, were calculated by two radiologists for the whole lesion volume. Statistical Tests Student's t‐test was used to compare malignant and benign lesions. The diagnostic performances were evaluated by receiver operating characteristic (ROC) analysis. Results Kmax, Dmin, and ADCmin had the highest area under the curve (AUC) (0.875, 0.830, and 0.847, respectively), sensitivity (85.5%, 74.2%, and 77.4%, respectively), and accuracy (85.0%, 79.2%, and 81.7%, respectively) in their individual histogram parameter groups, and Kmax was found to outperform Dmin and ADCmin. ADC histogram parameters (from ADCmin to ADCsd) were significantly lower than D histogram parameters in all groups. Data Conclusion Kmax, Dmin, and ADCmin were found to be better metrics than the corresponding average values for differentiating benign from malignant tumors. Histogram parameters derived from the DKI model provided more information and had better diagnostic performance than ADC parameters derived from the DWI model. Level of Evidence: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:627–634.

The Diagnostic Performance of Diffusion Kurtosis Imaging in the Characterization of Breast Tumors: A Meta-Analysis

“…In a previous study, Iima et al (36) used non-Gaussian diffusion and IVIM-DWI to diagnose breast lesions and found that the AUC of ADC0 was significantly higher than that of MK, suggesting the superiority of conventional DWI and it having more clinical availability. A complex and heterogeneous microstructure is a common feature of malignant tumors arising from necrosis, cancer nests, intraductal components, and plenty of barriers and compartmentalization between or within the cells (18). The diffusion of water molecules in this microenvironment will deviate from the mono-exponential Gaussian model at high b-values, leading to inaccurate fitting and calculation of the diffusion coefficient.…”

“…Jensen et al (11) first introduced the DKI model in 2005 and developed new fields in brain, liver, and prostate imaging afterward (12)(13)(14). The investigation of breast tumors using the DKI model reached a peak in 2019 and became an important research area thereafter (8,(15)(16)(17)(18)(19)(20). The model provides microscopic information regarding the deviation of water diffusion from Gaussian distribution with the mean kurtosis (MK) and mean diffusivity (MD), a kurtosiscorrected diffusion coefficient.…”

“…However, the diagnostic performance of DKI in the breast was not consistent, and its applications remain debatable. For example, most studies (8,18,20) have suggested that breast cancer has a higher MK and a lower MD than benign lesions, while Park et al (16) reported that the difference in the MK between them is insignificant. Some studies (25)(26)(27) have reported that the DKIderived MK or MD manifested better diagnostic performance than the DWI-derived ADC, while Palm et al (15) found that DKI did not improve the differentiation performance for breast lesions in clinical protocols.…”

Diffusion Weighted Imaging of Different Breast Cancer Molecular Subtypes. A Systematic Review and Meta Analysis.

“…Thirdly, the study investigated the widely used DWI technique using 2 b-values. However, more advanced MRI sequences, such as intravoxel incoherent motion and diffusion kurtosis imaging might show a better accuracy in discriminating BC phenotypes [57,58]. Yet, there are few studies using these sequences and thus no comprehensive analysis can be made.…”

Diagnostic performance of breast tumor tissue selection in diffusion weighted imaging: A systematic review and meta-analysis

“…The number of malignant and benign lesions ranged from 14-259 and 8-161, respectively, with a mean of 61 malignant and 36 benign lesions per study subset. Seven study subsets were categorized as BTTS1: whole breast tumor tissue selection [35,[38][39][40][41][42][43], 22 as BTTS2: subtracted whole breast tumor tissue selection [15][16][17]20,23,36,37,[44][45][46][47][48][49][50][51][52][53][54][55][56][57][58], 19 as BTTS3: circular breast tumor tissue selection [18,19,21,24,25,36,[59][60][61][62][63][64][65][66][67][68][69][70][71] and 17 as BTTS4: lowest diffusion breast tumor tissue selection…”