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Abstract #2638

Prostate Cancer Probability Estimation Based on DCE-DTI Features & Support Vector Machine Classification

Mehdi Moradi1, Septimiu E. Salcudean1, Silvia D. Chang2, Edward C. Jones3, S. Larry Goldenberg4,5, Piotr Kozlowski2,6

1Electrical & Computer Engineering, University of British Columbia, Vancouver, BC, Canada; 2Radiology, University of British Columbia; 3Pathology & Laboratory Medicine, University of British Columbia; 4Urologic Sciences, University of British Columbia; 5Vancouver Prostate Centre, University of British Columbia; 6MRI Research Centre, University of British Columbia, Vancouver, Canada

We use five parameters extracted from Diffusion Tensor Imaging (DTI) and Dynamic Contrast Enhanced (DCE) MRI for prostate cancer detection. 29 patients were involved. The method is based on support vector machine classification and calculation of posterior class probabilities. These cancer probability estimates are used for creating cancer maps validated based on histopathologic analysis of biopsy samples. We also found a correlation between the proposed measure of cancer probability and the Gleason grade of the tumors. The average probability value was 0.555 for tumors of grade 3+3, 0.778 for tumors of grades 3+4 and 4+3, and 0.963 for grade 4+5.