3 March 2009 Breast cancer classification with mammography and DCE-MRI
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Proceedings Volume 7260, Medical Imaging 2009: Computer-Aided Diagnosis; 72600O (2009) https://doi.org/10.1117/12.813723
Event: SPIE Medical Imaging, 2009, Lake Buena Vista (Orlando Area), Florida, United States
Abstract
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).
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Yading Yuan, Yading Yuan, Maryellen L. Giger, Maryellen L. Giger, Hui Li, Hui Li, Charlene Sennett, Charlene Sennett, } "Breast cancer classification with mammography and DCE-MRI", Proc. SPIE 7260, Medical Imaging 2009: Computer-Aided Diagnosis, 72600O (3 March 2009); doi: 10.1117/12.813723; https://doi.org/10.1117/12.813723
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