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Abstract #1509

Machine Learning for Target Selection in MR-Guided Prostate Biopsy: A Preliminary Study

Mehdi Moradi1, Andriy Fedorov1, William M. Wells1, Kemal Tuncali1, Sandeep N. Gupta2, Fiona M. Fennessy1, Clare M. Tempany1

1Radiology, Brigham and Women's Hospital - A Teaching Affiliate of Harvard Medical School, Boston, MA, United States; 2GE Global Research Center, Niskayuna, NY

We propose to use machine learning to enhance the process of target selection for 3T MR-guided transperineal prostate biopsy. Support vector machine and Gaussian classifiers with different combinations of diffusion and DCE MRI are examined. Training is performed on data from 13 prostatectomy cases with histologically confirmed cancer in the peripheral zone. The trained classifier was used to determine the outcome of in ten PZ biopsy samples from five patients. The Bayesian classifier with ADC as the only feature resulted in the ROC area of 0.964 in leave-one-patient-out cross-validation on the training dataset. The outcomes of eight of the ten biopsies, including all three cancer samples, were correctly determined.