Handling missing DCE data in prostate cancer detection using multiparametric MRI

The objective of the work presented here is to design classifiers to detect prostate cancer from MRI parametric maps with the capability of handling missing data, specifically DCE parameters. We propose two different methods and show their effectiveness in maintaining high AUC while handling missing parameters. Both methods are based on support vector machine classification. However, one method trains a single classifier and uses k-nearest neighbor imputation of DCE parameters in test cases where DCE is missing. The other method uses two different classifiers trained on DTI and DCE, fuses the two methods in cases where both DTI and DCE are available. We showed that as an increasing number of cases with missing DCE features are presented to the classifiers, KNN imputation of missing features outperforms the fusion of two classifiers. Both methods outperform a DTI only classifier.

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