Ultrasound-Based Characterization of Prostate Cancer: An in vivo Clinical Feasibility Study

UNLABELLED This paper presents the results of an in vivo clinical study to accurately characterize prostate cancer using new features of ultrasound RF time series. METHODS The mean central frequency and wavelet features of ultrasound RF time series from seven patients are used along with an elaborate framework of ultrasound to histology registration to identify and verify cancer in prostate tissue regions as small as 1.7 mm x 1.7 mm. RESULTS In a leave-one-patient-out cross-validation strategy, an average classification accuracy of 76% and the area under ROC curve of 0.83 are achieved using two proposed RF time series features. The results statistically significantly outperform those achieved by previously reported features in the literature. The proposed features show the clinical relevance of RF time series for in vivo characterization of cancer.

[1]  Aaron Fenster,et al.  Prostate boundary segmentation from ultrasound images using 2D active shape models: Optimisation and extension to 3D , 2006, Comput. Methods Programs Biomed..

[2]  A. Fenster,et al.  Registration of prostate histology images to ex vivo MR images via strand‐shaped fiducials , 2012, Journal of magnetic resonance imaging : JMRI.

[3]  H. Ermert,et al.  Tissue-characterization of the prostate using radio frequency ultrasonic signals , 1999, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[4]  Tomohiko Ichikawa,et al.  Clinical evaluation of transrectal power Doppler imaging in the detection of prostate cancer , 2004, International Urology and Nephrology.

[5]  Chien-Cheng Chang,et al.  Imaging local scatterer concentrations by the Nakagami statistical model. , 2007, Ultrasound in medicine & biology.

[6]  Jin Young Choi,et al.  Computer-aided Prostate Cancer Detection using Texture Features and Clinical Features in Ultrasound Image , 2008, Journal of Digital Imaging.

[7]  Septimiu E. Salcudean,et al.  Viscoelasticity Modeling of the Prostate Region Using Vibro-elastography , 2006, MICCAI.

[8]  Kathryn R Nightingale,et al.  Acoustic radiation force impulse imaging of human prostates ex vivo. , 2010, Ultrasound in medicine & biology.

[9]  Lasse Riis Østergaard,et al.  Active Surface Approach for Extraction of the Human Cerebral Cortex from MRI , 2006, MICCAI.

[10]  Mehdi Moradi,et al.  Ultrasound RF time series for tissue typing: first in vivo clinical results , 2013, Medical Imaging.

[11]  Andrew Kalisz,et al.  Recent Developments in Tissue-Type Imaging (TTI) for Planning and Monitoring Treatment of Prostate Cancer , 2004, Ultrasonic imaging.