ADC mapping of benign and malignant breast tumors.
OBJECTIVE
The purpose of this study was to investigate the utility of diffusion-weighted imaging (DWI) and the apparent diffusion coefficient (ADC) value in differentiating benign and malignant breast lesions and evaluating the detection accuracy of the cancer extension.


METHODS
We used DWI to obtain images of 191 benign and malignant lesions (24 benign, 167 malignant) before surgical excision. The ADC values of the benign and malignant lesions were compared, as were the values of noninvasive ductal carcinoma (NIDC) and invasive ductal carcinoma (IDC). We also evaluated the ADC map, which represents the distribution of ADC values, and compared it with the cancer extension.


RESULTS
The mean ADC value of each type of lesion was as follows: malignant lesions, 1.22+/-0.31 x 10(-3) mm2/s; benign lesions, 1.67+/-0.54 x 10(-3) mm2/s; normal tissues, 2.09+/-0.27 x 10(-3) mm2/s. The mean ADC value of the malignant lesions was statistically lower than that of the benign lesions and normal breast tissues. The ADC value of IDC was statistically lower than that of NIDC. The sensitivity of the ADC value for malignant lesions with a threshold of less than 1.6 x 10(-3) mm2/s was 95% and the specificity was 46%. A full 75% of all malignant cases exhibited a near precise distribution of low ADC values on ADC maps to describe malignant lesions. The main causes of false negative and underestimation of cancer spread were susceptibility artifact because of bleeding and tumor structure. Major histologic types of false-positive lesions were intraductal papilloma and fibrocystic diseases. Fibrocystic diseases also resulted in overestimation of cancer extension.


CONCLUSIONS
DWI has the potential in clinical appreciation to detect malignant breast tumors and support the evaluation of tumor extension. However, the benign proliferative change remains to be studied as it mimics the malignant phenomenon on the ADC map.
Woodhams
Reiko
R
Department of Radiology, Kitasato University School of Medicine, Japan. reiko99@db3.so-net.ne.jp
Matsunaga
Keiji
K
Kan
Shinichi
S
Hata
Hirofumi
H
Ozaki
Masanori
M
Iwabuchi
Keiichi
K
Kuranami
Masaru
M
Watanabe
Masahiko
M
Hayakawa
Kazushige
K
D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D016477:Artifacts; D001185:Artificial Intelligence; D001943:Breast Neoplasms / Q000175:diagnosis*; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D008875:Middle Aged; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity