Correlation Based Random Subspace Ensembles for Predicting Number of Axillary Lymph Node Metastases in Breast DCE-MRI Tumors

An important problem in quantitative medical image analysis is a large number of features (often highly correlated) to instance ratio. To handle this, we developed a feature selector and an ensemble classifier based on a modified version of random subspace method. We propose using a fusion of feature selection concepts: ranking based, correlation based and random subspaces, to develop a concordance correlation coefficient based random subspace method (CCC RSM) feature selector. It forms random feature subsets with weakly correlated yet relevant features while the ensemble classification is achieved by training the base classifier with these feature subsets. Axillary lymph node (ALNs) metastases is one of the most important prognostic factors in breast cancer. We applied CCC RSM for four binary class classifications based on the number of metastatic ALNs: (i) = 1 vs 0 (ii) 1-3 vs 0 (iii) 1-3 vs = 4, and (iv) 4 vs 0. We extracted textural kinetics from habitats of fifty eight dynamic contrast enhanced magnetic resonance imaging breast tumors. We used three classifiers to compare the accuracies achieved by CCC RSM with random subspaces (RS), wrappers and correlation based feature selector (CFS). For each binary classification we achieved the best accuracy (= 78%) using CCC RSM.