Ting Li1, Yuan Hong2, Dexing Kong2, Kangan Li3. 1. The Department of Radiology, First People's Hospital of Changzhou, Jiangsu, P.R. China. 2. School of Mathematical Sciences, Zhejiang University, Hangzhou, P.R. China. 3. Department of Radiology, Shanghai General Hospital, Shanghai, P.R. China.
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.
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.
Authors: Ashley M Mendez; Lauren K Fang; Claire H Meriwether; Summer J Batasin; Stéphane Loubrie; Ana E Rodríguez-Soto; Rebecca A Rakow-Penner Journal: Front Oncol Date: 2022-07-08 Impact factor: 5.738