Differentiating Benign and Malignant Breast Lesions in Diffusion Kurtosis MRI: Does the Averaging Procedure Matter?

BACKGROUND: Diffusion kurtosis imaging (DKI) is used to differentiate between benign and malignant breast lesions. DKI fits are performed either on voxel-by-voxel basis or using volume-averaged signal. PURPOSE: Investigate and compare DKI parameters' diagnostic performance using voxel-by-voxel and volume-averaged signal fit approach. STUDY TYPE: Retrospective. STUDY POPULATION: A total of 104 patients, aged 24.1-86.4 years. FIELD STRENGTH/SEQUENCE: A 3 T Spin-echo planar diffusion-weighted sequence with b-values: 50 s/mm2 , 750 s/mm2 , and 1500 s/mm2 . Dynamic contrast enhanced (DCE) sequence. ASSESSMENT: Lesions were manually segmented by M.P. under supervision of S.O. (2 and 5 years of experience in breast MRI). DKI fits were performed on voxel-by-voxel basis and with volume-averaged signal. Diagnostic performance of DKI parameters D K (kurtosis corrected diffusion coefficient) and kurtosis K was compared between both approaches. STATISTICAL TESTS: Receiver operating characteristics analysis and area under the curve (AUC) values were computed. Wilcoxon rank sum and Students t-test tested DKI parameters for significant (P <0.05) difference between benign and malignant lesions. DeLong test was used to test the DKI parameter performance for significant fit approach dependency. Correlation between parameters of the two approaches was determined by Pearson correlation coefficient. RESULTS: DKI parameters were significantly different between benign and malignant lesions for both fit approaches. Median benign vs. malignant values for voxel-by-voxel and volume-averaged approach were 2.00 vs. 1.28 ( D K in µm2 /msec), 2.03 vs. 1.26 ( D K in µm2 /msec), 0.54 vs. 0.90 ( K ), 0.55 vs. 0.99 ( K ). AUC for voxel-by-voxel and volume-averaged fit were 0.9494 and 0.9508 ( D K ); 0.9175 and 0.9298 ( K ). For both, AUC did not differ significantly (P = 0.20). Correlation of values between the two approaches was very high (r = 0.99 for D K and r = 0.97 for K ). DATA CONCLUSION: Voxel-by-voxel and volume-averaged signal fit approach are equally well suited for differentiating between benign and malignant breast lesions in DKI. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 3.

On the Field Strength Dependence of Bi- and Triexponential Intravoxel Incoherent Motion (IVIM) Parameters in the Liver.

BACKGROUND: Studies on intravoxel incoherent motion (IVIM) imaging are carried out with different acquisition protocols. PURPOSE: To investigate the dependence of IVIM parameters on the B0 field strength when using a bi- or triexponential model. STUDY TYPE: Prospective. STUDY POPULATION: 20 healthy volunteers (age: 19-28 years). FIELD STRENGTH/SEQUENCE: Volunteers were examined at two field strengths (1.5 and 3T). Diffusion-weighted images of the abdomen were acquired at 24 b-values ranging from 0.2 to 500 s/mm2 . ASSESSMENT: ROIs were manually drawn in the liver. Data were fitted with a bi- and a triexponential IVIM model. The resulting parameters were compared between both field strengths. STATISTICAL TESTS: One-way analysis of variance (ANOVA) and Kruskal-Wallis test were used to test the obtained IVIM parameters for a significant field strength dependency. RESULTS: At b-values below 6 s/mm2 , the triexponential model provided better agreement with the data than the biexponential model. The average tissue diffusivity was D = 1.22/1.00 µm2 /msec at 1.5/3T. The average pseudodiffusion coefficients for the biexponential model were D* = 308/260 µm2 /msec at 1.5/3T; and for the triexponential model D1* = 81.3/65.9 µm2 /msec, D2* = 2453/2333 µm2 /msec at 1.5/3T. The average perfusion fractions for the biexponential model were f = 0.286/0.303 at 1.5/3T; and for the triexponential model f1 = 0.161/0.174 and f2 = 0.152/0.159 at 1.5/3T. A significant B0 dependence was only found for the biexponential pseudodiffusion coefficient (ANOVA/KW P = 0.037/0.0453) and tissue diffusivity (ANOVA/KW: P < 0.001). DATA CONCLUSION: Our experimental results suggest that triexponential pseudodiffusion coefficients and perfusion fractions obtained at different field strengths could be compared across different studies using different B0 . However, it is recommended to take the field strength into account when comparing tissue diffusivities or using the biexponential IVIM model. Considering published values for oxygenation-dependent transversal relaxation times of blood, it is unlikely that the two blood compartments of the triexponential model represent venous and arterial blood. LEVEL OF EVIDENCE: 1 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019;50:1883-1892.

Stability of Radiomic Features against Variations in Lesion Segmentations Computed on Apparent Diffusion Coefficient Maps of Breast Lesions.

Diffusion-weighted imaging (DWI) combined with radiomics can aid in the differentiation of breast lesions. Segmentation characteristics, however, might influence radiomic features. To evaluate feature stability, we implemented a standardized pipeline featuring shifts and shape variations of the underlying segmentations. A total of 103 patients were retrospectively included in this IRB-approved study after multiparametric diagnostic breast 3T MRI with a spin-echo diffusion-weighted sequence with echoplanar readout (b-values: 50, 750 and 1500 s/mm2). Lesion segmentations underwent shifts and shape variations, with >100 radiomic features extracted from apparent diffusion coefficient (ADC) maps for each variation. These features were then compared and ranked based on their stability, measured by the Overall Concordance Correlation Coefficient (OCCC) and Dynamic Range (DR). Results showed variation in feature robustness to segmentation changes. The most stable features, excluding shape-related features, were FO (Mean, Median, RootMeanSquared), GLDM (DependenceNonUniformity), GLRLM (RunLengthNonUniformity), and GLSZM (SizeZoneNonUniformity), which all had OCCC and DR > 0.95 for both shifting and resizing the segmentation. Perimeter, MajorAxisLength, MaximumDiameter, PixelSurface, MeshSurface, and MinorAxisLength were the most stable features in the Shape category with OCCC and DR > 0.95 for resizing. Considering the variability in radiomic feature stability against segmentation variations is relevant when interpreting radiomic analysis of breast DWI data.