Classification of Lobular and Ductal Breast Carcinomas by Texture Analysis in DCE-MRI Data
Breast cancer can be distinguished into several subtypes, where invasive ductal carcinomas (IDC) and invasive lobular carcinomas (ILC) are the two most common subtypes. These two types of tumor grow at a different speed and exhibit different metastatic patterns. Although both types are malignant, they show different treatment results for the same therapy. Accurate distinction between these two subtypes is helpful for determining therapy strategies. In this paper, we classify IDC and ILC based on their characteristic texture features, which are extracted from a three-dimensional co-occurrence matrix. The texture features at different time points are used instead of the features from a single time point. We employ a non-linear support vector machine (SVM) algorithm and a random forests method as classifiers to separate IDC and ILC via their texture features and achieve a high accuracy of the classification result.
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