|
Since different imaging modalities provide complementary information
regarding the same lesion, combining information from different modalities
may increase diagnostic accuracy. In this study, we investigated the
use of computerized features of lesions imaged via both full-field
digital mammography (FFDM) and dynamic contrast-enhanced magnetic
resonance imaging (DCE-MRI) in the classification of breast lesions.
Using a manually identified lesion location, i.e. a seed point on
FFDM images or a ROI on DCE-MRI images, the computer automatically
segmented mass lesions and extracted a set of features for each lesion.
Linear stepwise feature selection was firstly performed on single
modality, yielding one feature subset for each modality. Then, these
selected features served as the input to another feature selection
procedure when extracting useful information from both modalities.
The selected features were merged by linear discriminant analysis
(LDA) into a discriminant score. Receiver operating characteristic
(ROC) analysis was used to evaluate the performance of the selected
feature subset in the task of distinguishing between malignant and
benign lesions. From a FFDM database with 321 lesions (167 malignant
and 154 benign), and a DCE-MRI database including 181 lesions
(97 malignant and 84 benign), we constructed a multi-modality
dataset with 51 lesions (29 malignant and 22 benign). With
leave-one-out-by-lesion evaluation on the multi-modality dataset,
the mammography-only features yielded an area under the ROC curve
(AUC) of 0.62 ± 0.08 and the DCE-MRI-only features yielded an AUC
of 0.94±0.03. The combination of these two modalities, which
included a spiculation feature from mammography and a kinetic feature
from DCE-MRI, yielded an AUC of 0.94. The improvement of
combining multi-modality information was statistically significant
as compared to the use of mammography only (p=0.0001). However,
we failed to show the statistically significant improvement as compared
to DCE-MRI, with the limited multi-modality dataset (p=0.22).
|
