Characterization of breast masses as benign or malignant at 3.0T MRI with whole-lesion histogram analysis of the apparent diffusion coefficient.
OBJECTIVE
To investigate the utility of whole-lesion apparent diffusion coefficient (ADC) histogram analysis in capturing breast lesion heterogeneity and determine which ADC metric may help best differentiate benign from malignant breast mass lesions at 3.0T magnetic resonance imaging (MRI).


METHODS
We retrospectively included 101 women with breast mass lesions (benign:malignant = 36:65) who underwent 3.0T diffusion-weighted imaging (DWI) and subsequently had histopathologic confirmation. ADC histogram parameters, including the mean, minimum, maximum, 10th/25th/50th/75th/90th percentile, skewness, kurtosis, and entropy ADCs, were derived for the whole-lesion volume in each patient. Mann-Whitney U-test, univariate and multivariate logistic regression, area under the receiver-operating characteristic curve (Az ), intraclass correlation coefficient (ICC), and Bland-Altman test were used for statistical analysis.


RESULTS
Mean, minimum, maximum, and 10th/25th/50th/75th/90th percentile ADCs were significantly lower (all P < 0.0001), while skewness and entropy ADCs were significantly higher (P < 0.001 and P = 0.001, respectively) in malignant lesions compared with benign ones. The Az values of minimum and 25th percentile ADCs were significantly higher than that of mean ADC (P = 0.0194 and P = 0.0154, respectively) or that of median ADC (P = 0.0300 and P = 0.0401, respectively), indicating that minimum and 25th percentile ADCs may be more accurate for lesion discrimination. Multivariate logistic regression showed that the minimum ADC was the unique independent predictor of breast malignancy. Minimum and 25th percentile ADCs had excellent interobserver agreement (ICC = 0.943 and 0.989, respectively; narrow width of 95% limits of agreement).


CONCLUSIONS
These results suggest that whole-lesion ADC histogram analysis may facilitate the differentiation between benign and malignant breast mass lesions.
Department of Radiology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Shiteng
S
Suo
Department of Radiology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Kebei
K
Zhang
Department of Radiology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Mengqiu
M
Cao
School of Medical Imaging, Tianjin Medical University, Tianjin, China.
Xinjun
X
Suo
Department of Radiology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Jia
J
Hua
Department of Radiology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Xiaochuan
X
Geng
Department of Radiology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Jie
J
Chen
Department of Radiology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Zhiguo
Z
Zhuang
School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
Xiang
X
Ji
Department of Radiology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Qing
Q
Lu
Philips Research China, Shanghai, China.
He
H
Wang
Department of Radiology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Jianrong
J
Xu
D000293:Adolescent; D000328:Adult; D000368:Aged; D000465:Algorithms; D001940:Breast / Q000000981:diagnostic imaging / Q000473:pathology; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D038524:Diffusion Magnetic Resonance Imaging; D005260:Female; D018226:Fibroadenoma / Q000000981:diagnostic imaging; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008875:Middle Aged; D009038:Motion; D015588:Observer Variation; D012372:ROC Curve; D012044:Regression Analysis; D015203:Reproducibility of Results; D012189:Retrospective Studies; D018709:Statistics, Nonparametric; D055815:Young Adult
apparent diffusion coefficient; breast mass; diffusion-weighted imaging; heterogeneity; histogram analysis