Quan-Xin Yang1, Xing Ji2, Lin-Lin Feng1, Long Zheng3, Xiao-Qian Zhou1, Qian Wu4, Xin Chen1. 1. Department of Radiology, Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shannxi, China. 2. Department of Radiology, Affiliated Hospital of Yan'an University, Yan'an, Shannxi, China. 3. Department of Nuclear Medicine, Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shannxi, China. 4. Department of Epidemiology, Medical College of Xi'an Jiaotong University, Xi'an, Shannxi, China.
Abstract
OBJECTIVE: To explore and evaluate new malignant predictors of breast non-mass enhancement lesions using the new BI-RADS MRI lexicon. METHODS: A dataset involving 422 consecutive women underwent breast 3.0 T MRI between January 2014 and July 2016 was assembled for this study. Each case was retrospectively reviewed by 3 radiologists. Eighty-four lesions that present non-mass enhancement in 79 patients were identified in the study. Dynamic contrast-enhanced MRI features were analyzed using univariate and multivariate analyses to identify significant indicators of malignancy. RESULTS: Of 84 non-mass enhancement lesions, 52 (61.9%) were malignant and 32 (38.1%) were benign. Segmental distribution (P = 0.015 from univariate analysis; OR = 4.739, P = 0.008 from multivariate analysis), cluster ring enhancement (P = 0.017 from univariate analysis; OR = 3.601, P = 0.032 from multivariate analysis), time-intensity curve of plateau (P = 0.002 from univariate analysis; OR = 3.525, P = 0.027 from multivariate analysis) and phase to peak (P = 0.06 from univariate analysis; OR = 6.327, P = 0.015 from multivariate analysis) were significantly different between malignant and benign lesions. CONCLUSIONS: This study demonstrated that segmental distribution, clustered ring enhancement, and short time to peak could act as new malignant predictors for breast non-mass enhancement detected on 3.0 T MRI.
OBJECTIVE: To explore and evaluate new malignant predictors of breast non-mass enhancement lesions using the new BI-RADS MRI lexicon. METHODS: A dataset involving 422 consecutive women underwent breast 3.0 T MRI between January 2014 and July 2016 was assembled for this study. Each case was retrospectively reviewed by 3 radiologists. Eighty-four lesions that present non-mass enhancement in 79 patients were identified in the study. Dynamic contrast-enhanced MRI features were analyzed using univariate and multivariate analyses to identify significant indicators of malignancy. RESULTS: Of 84 non-mass enhancement lesions, 52 (61.9%) were malignant and 32 (38.1%) were benign. Segmental distribution (P = 0.015 from univariate analysis; OR = 4.739, P = 0.008 from multivariate analysis), cluster ring enhancement (P = 0.017 from univariate analysis; OR = 3.601, P = 0.032 from multivariate analysis), time-intensity curve of plateau (P = 0.002 from univariate analysis; OR = 3.525, P = 0.027 from multivariate analysis) and phase to peak (P = 0.06 from univariate analysis; OR = 6.327, P = 0.015 from multivariate analysis) were significantly different between malignant and benign lesions. CONCLUSIONS: This study demonstrated that segmental distribution, clustered ring enhancement, and short time to peak could act as new malignant predictors for breast non-mass enhancement detected on 3.0 T MRI.
Entities:
Keywords:
Breast; breast imaging reporting and data system; magnetic resonance imaging; non-mass enhancement