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Histogram analysis of diffusion kurtosis imaging based on whole‐volume images of breast lesions
Author(s) -
Li Ting,
Hong Yuan,
Kong Dexing,
Li Kangan
Publication year - 2020
Publication title -
journal of magnetic resonance imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.563
H-Index - 160
eISSN - 1522-2586
pISSN - 1053-1807
DOI - 10.1002/jmri.26884
Subject(s) - kurtosis , percentile , medicine , histogram , nuclear medicine , receiver operating characteristic , effective diffusion coefficient , standard deviation , breast cancer , breast mri , skewness , diffusion mri , radiology , magnetic resonance imaging , mathematics , cancer , mammography , artificial intelligence , statistics , computer science , image (mathematics)
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/mm 2 ) and DWI sequence with two b‐values (0 and 1000 s/mm 2 ) 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 K max , D min , and ADC min 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 K max was found to outperform D min and ADC min . ADC histogram parameters (from ADC min to ADC sd ) were significantly lower than D histogram parameters in all groups. Data Conclusion K max , D min , and ADC min 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.