ABSTRACT. The current study investigates the feasibility of using texture analysis to quantify the heterogeneity of lesion enhancement kinetics in order to discriminate malignant from benign breast lesions. A total of 82 biopsy-proven breast lesions (51 malignant, 31 benign), originating from 74 women subjected to dynamic contrastenhanced magnetic resonance imaging (DCE-MRI) were analysed. Pixel-wise analysis of DCE-MRI lesion data was performed to generate initial enhancement, post-initial enhancement and signal enhancement ratio (SER) parametric maps; these maps were subsequently subjected to co-occurrence matrix texture analysis. The discriminating ability of texture features extracted from each parametric map was investigated using a least-squares minimum distance classifier and further compared with the discriminating ability of the same texture features extracted from the first post-contrast frame. Selected texture features extracted from the SER map achieved an area under receiver operating characteristic curve of 0.922 ¡ 0.029, a performance similar to post-initial enhancement map features (0.906 ¡ 0.032) and statistically significantly higher than for initial enhancement map (0.767 ¡ 0.053) and first post-contrast frame (0.756 ¡ 0.060) features. Quantifying the heterogeneity of parametric maps that reflect lesion washout properties could contribute to the computer-aided diagnosis of breast lesions in DCE-MRI. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) significantly complements mammography and is characterised by its high sensitivity in detecting breast cancer. However, its specificity in distinguishing malignant from benign lesions is highly varied: reported values range from 37 up to 90% [1]. This variation is mainly due to the variety of image acquisition protocols and interpretation schemes adopted in clinical practice [2].Diagnostic criteria in DCE-MRI of breast masses [3, 4] include assessment of morphological features such as lesion shape, margin and enhancement homogeneity (internal architecture), as well as analysis/assessment of signal intensity-time curves generated from manually selected regions of interest (ROIs) within the lesion area. The analysis of signal intensity-time curves can be performed qualitatively (i.e. visual inspection of the curve shape [5,6]), by means of empirical parameters (e.g. relative enhancement, time-to-peak enhancement, washout ratio [7]) or quantitatively through pharmacokinetic modelling techniques [8].The subjective selection of ROI within the lesion accounts for the increased intra-and interobserver variability in the interpretation of lesion enhancement kinetics and for the discrepancy of reported findings [9,10]. While the selection of an ROI that captures the entire lesion is less subjective [11], it provides average enhancement kinetics estimates and completely ignores the heterogeneity of tumour vascular characteristics, which is diagnostically important [12]. Pixel-wise analysis of enhancement kinetics (based either on pharmacokinetic ...
The current study investigates whether texture features extracted from lesion kinetics feature maps can be used for breast cancer diagnosis. Fifty five women with 57 breast lesions (27 benign, 30 malignant) were subjected to dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) on 1.5T system. A linear-slope model was fitted pixel-wise to a representative lesion slice time series and fitted parameters were used to create three kinetic maps (wash out, time to peak enhancement and peak enhancement). 28 grey level co-occurrence matrices features were extracted from each lesion kinetic map. The ability of texture features per map in discriminating malignant from benign lesions was investigated using a Probabilistic Neural Network classifier. Additional classification was performed by combining classification outputs of most discriminating feature subsets from the three maps, via majority voting. The combined scheme outperformed classification based on individual maps achieving area under Receiver Operating Characteristics curve 0.960 ± 0.029. Results suggest that heterogeneity of breast lesion kinetics, as quantified by texture analysis, may contribute to computer assisted tissue characterization in DCE-MRI.
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