Computer-Aided Prostate Cancer Detection Using Ultrasound RF Time Series: In Vivo Feasibility Study
Farhad Imani, Purang Abolmaesumi, Eli Gibson, Amir Khojaste, Mena Gaed, Madeleine Moussa, José A. Gómez, Cesare Romagnoli, Michael Leveridge, Silvia D. Chang, David Robert Siemens, Aaron Fenster, Aaron D. Ward, Parvin Mousavi · IEEE Transactions on Medical Imaging · 2015
UNLABELLED: This paper presents the results of a computer-aided intervention solution to demonstrate the application of RF time series for characterization of prostate cancer, in vivo. METHODS: We pre-process RF time series features extracted from 14 patients using hierarchical clustering to remove possible outliers. Then, we demonstrate that the mean central frequency and wavelet features extracted from a group of patients can be used to build a nonlinear classifier which can be applied successfully to differentiate between cancerous and normal tissue regions of an unseen patient. RESULTS: In a cross-validation strategy, we show an average area under receiver operating characteristic curve (AUC) of 0.93 and classification accuracy of 80%. To validate our results, we present a detailed ultrasound to histology registration framework. CONCLUSION: Ultrasound RF time series results in differentiation of cancerous and normal tissue with high AUC.