Characterization of aggressive prostate cancer using ultrasound RF time series

Amir Khojaste; Farhad Imani; Mehdi Moradi; David Berman; D. Robert Siemens; Eric E. Sauerberi; Alexander H. Boag; Purang Abolmaesumi; Parvin Mousavi

doi:10.1117/12.2082663

20 March 2015 Characterization of aggressive prostate cancer using ultrasound RF time series

Amir Khojaste, Farhad Imani, Mehdi Moradi, David Berman, D. Robert Siemens, Eric E. Sauerberi, Alexander H. Boag, Purang Abolmaesumi, Parvin Mousavi

Author Affiliations +

Proceedings Volume 9414, Medical Imaging 2015: Computer-Aided Diagnosis; 94141A (2015) https://doi.org/10.1117/12.2082663
Event: SPIE Medical Imaging, 2015, Orlando, Florida, United States

Abstract

Prostate cancer is the most prevalently diagnosed and the second cause of cancer-related death in North American men. Several approaches have been proposed to augment detection of prostate cancer using different imaging modalities. Due to advantages of ultrasound imaging, these approaches have been the subject of several recent studies. This paper presents the results of a feasibility study on differentiating between lower and higher grade prostate cancer using ultrasound RF time series data. We also propose new spectral features of RF time series to highlight aggressive prostate cancer in small ROIs of size 1 mm × 1 mm in a cohort of 19 ex vivo specimens of human prostate tissue. In leave-one-patient-out cross-validation strategy, an area under accumulated ROC curve of 0.8 has been achieved with overall sensitivity and specificity of 81% and 80%, respectively. The current method shows promising results on differentiating between lower and higher grade of prostate cancer using ultrasound RF time series.

Citation Download Citation

Amir Khojaste, Farhad Imani, Mehdi Moradi, David Berman, D. Robert Siemens, Eric E. Sauerberi, Alexander H. Boag, Purang Abolmaesumi, and Parvin Mousavi "Characterization of aggressive prostate cancer using ultrasound RF time series", Proc. SPIE 9414, Medical Imaging 2015: Computer-Aided Diagnosis, 94141A (20 March 2015); https://doi.org/10.1117/12.2082663

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