Dynamic characterization of the lesion enhancement pattern can improve the classification performance of small diagnostically challenging lesions on dynamic-contrast enhanced MRI. This involves extraction of texture features from all post-contrast images of the lesion rather than using the first post-contrast image alone. In this study, statistical texture features derived from gray-level co-occurrence matrices are extracted from all five post-contrast images of 60 lesions and then used in a supervised learning task with a support vector regressor. Our results show that this approach significantly improves the performance of classifying small lesions (
< 0.05). This suggests that such dynamic characterization of lesion enhancement has significant potential in assisting breast cancer diagnosis for small lesions.
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