Characterization of breast cancer types by texture analysis of magnetic resonance images.
OBJECTIVE
This novel study aims to investigate texture parameters in distinguishing healthy breast tissue and breast cancer in breast magnetic resonance imaging (MRI). A specific aim was to identify possible differences in the texture characteristics of histological types (lobular and ductal) of invasive breast cancer and to determine the value of these differences for computer-assisted lesion classification.


METHODS
Twenty patients (mean age 50.6 + or - SD 10.6; range 37-70 years), with histopathologically proven invasive breast cancer (10 lobular and 10 ductal) were included in this preliminary study. The median MRI lesion size was 25 mm (range, 7-60 mm). The selected T1-weighted precontrast, post-contrast, and subtracted images were analyzed and classified with texture analysis (TA) software MaZda and additional statistical tests were used for testing the parameters separability.


RESULTS
All classification methods employed were able to differentiate between cancer and healthy breast tissue and also invasive lobular and ductal carcinoma with classification accuracy varying between 80% and 100%, depending on the used imaging series and the type of region of interest. We found several parameters to be significantly different between the regions of interest studied. The co-occurrence matrix based parameters proved to be superior to other texture parameters used.


CONCLUSIONS
The results of this study indicate that MRI TA differentiates breast cancer from normal tissue and may be able to distinguish between two histological types of breast cancer providing more accurate characterization of breast lesions thereby offering a new tool for radiological analysis of breast MRI.
Holli
Kirsi
K
Medical Imaging Centre, Tampere University Hospital, Post Box 2000, 33521 Tampere, Finland. kirsi.holli@pshp.fi
Lääperi
Anna-Leena
AL
Harrison
Lara
L
Luukkaala
Tiina
T
Toivonen
Terttu
T
Ryymin
Pertti
P
Dastidar
Prasun
P
Soimakallio
Seppo
S
Eskola
Hannu
H
D000368:Aged; D000465:Algorithms; D001940:Breast / Q000473:pathology*; D001943:Breast Neoplasms / Q000175:diagnosis*; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity