18 March 2016 Classification of prostate cancer grade using temporal ultrasound: in vivo feasibility study
Author Affiliations +
Temporal ultrasound has been shown to have high classification accuracy in differentiating cancer from benign tissue. In this paper, we extend the temporal ultrasound method to classify lower grade Prostate Cancer (PCa) from all other grades. We use a group of nine patients with mostly lower grade PCa, where cancerous regions are also limited. A critical challenge is to train a classifier with limited aggressive cancerous tissue compared to low grade cancerous tissue. To resolve the problem of imbalanced data, we use Synthetic Minority Oversampling Technique (SMOTE) to generate synthetic samples for the minority class. We calculate spectral features of temporal ultrasound data and perform feature selection using Random Forests. In leave-one-patient-out cross-validation strategy, an area under receiver operating characteristic curve (AUC) of 0.74 is achieved with overall sensitivity and specificity of 70%. Using an unsupervised learning approach prior to proposed method improves sensitivity and AUC to 80% and 0.79. This work represents promising results to classify lower and higher grade PCa with limited cancerous training samples, using temporal ultrasound.
© (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Sahar Ghavidel, Sahar Ghavidel, Farhad Imani, Farhad Imani, Siavash Khallaghi, Siavash Khallaghi, Eli Gibson, Eli Gibson, Amir Khojaste, Amir Khojaste, Mena Gaed, Mena Gaed, Madeleine Moussa, Madeleine Moussa, Jose A. Gomez, Jose A. Gomez, D. Robert Siemens, D. Robert Siemens, Michael Leveridge, Michael Leveridge, Silvia Chang, Silvia Chang, Aaron Fenster, Aaron Fenster, Aaron D. Ward, Aaron D. Ward, Purang Abolmaesumi, Purang Abolmaesumi, Parvin Mousavi, Parvin Mousavi, } "Classification of prostate cancer grade using temporal ultrasound: in vivo feasibility study", Proc. SPIE 9786, Medical Imaging 2016: Image-Guided Procedures, Robotic Interventions, and Modeling, 97860K (18 March 2016); doi: 10.1117/12.2216922; https://doi.org/10.1117/12.2216922

Back to Top