Feature Selection in Computer-Aided Breast Cancer Diagnosis via Dynamic Contrast-Enhanced Magnetic Resonance Images

Abstract

The accuracy of computer-aided diagnosis (CAD) for early detection and classification of breast cancer in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is dependent upon the features used by the CAD classifier. Here, we show that fast orthogonal search (FOS), which provides a more efficient iterative manner of computing stepwise regression feature selection, can select features with predictive value from a set of kinetic and texture candidate features computed from dynamic contrast-enhanced magnetic resonance images. FOS can in minutes search candidate feature sets of millions of terms, which may include cross-products of features up to second-, third- or fourth-order. This method is tested on a set of 83 DCE-MRI images, of which 20 are for cancerous and 63 for benign cases, using a leave-one-out trial. The features selected by FOS were used in a FOS predictor and nearest-neighbour predictor and had an area under the receiver operating curve (AUC) of 0.889 and 0.791 respectively. The FOS predictor AUC is significantly improved over the signal enhancement ratio predictor with an AUC of 0.706 (p = 0.0035 for the difference in the AUCs). Moreover, using FOS-selected features in a support vector machine increased the AUC over that resulting when the features were manually selected.

Models and methods for analyzing DCE‐MRI: A review

“…The accuracy of early detection and/or diagnosis using the nonparametric DCE-MRI measurements has been tested and improved in a number of CAD systems. [52][53][54][55][56][57][58][59][60][61][62][63][64][65][66][67] An approach for the extraction and visualization of perfusion parameters of breast DCE-MRI was proposed by Glaßer et al 52 To reveal the most suspicious region and the heterogeneity of the tumor, their study employed voxel-wise parametric maps of relative enhancement of breast tumors. Karahaliou et al 53 investigated the feasibility of discriminating between the malignant and benign breast tumors by texture analysis.…”

Computer-Aided Diagnosis Scheme for Distinguishing Between Benign and Malignant Masses in Breast DCE-MRI

“…The dynamic change in signal intensity over time has often been used in previous studies [12,[14][15][16][17]. In order to evaluate the contribution of the image features in distinguishing between benign and malignant masses, the objective features used for the QDA were selected from only four objective features (i.e., features 8-11) regarding the dynamic changes in signal intensity by the stepwise method.…”

“…CAD is defined as a diagnostic method wherein radiologists use the results analyzed by a computer as a Bsecond opinion.^In fact, CAD schemes for DCE-MRI images have been developed in many studies [12][13][14][15][16][17]. In a breast DCE-MRI examination, cancer patterns tend to show rapid initial enhancement, followed by a washout or a plateau in signal intensity over time [18].…”

Analysis of contrast-enhanced medical images.

“…The accuracy of early detection and/or diagnosis using the nonparametric DCE-MRI measurements has been tested and improved in a number of CAD systems [52,56,[105][106][107][108][109][110][111][112][113][114][115][116][117]. An approach for the extraction and visualization of perfusion parameters of breast DCE-MRI was proposed by Glaßer et al [105].…”

“…These studies try to correlate DCE-MRI measurements with diseases. The early DCE-MRI-based diagnosis was explored in different clinical studies, including head and neck [89], cardiac [90][91][92][93][94][95][96][97][98], pelvic [99], rectal [100], pancreatic cancer [101], liver [102], lung [103], colon [104], breast [52,56,[105][106][107][108][109][110][111][112][113][114][115][116][117], renal [3,[118][119][120][121][122][123][124][125][126][127][128][129], and prostate [75,77,[130][131]…”