Meeting Banner
Abstract #0782

Prostate Cancer Detection: Multi-Parametric MRI with Diffusion-Weighted Imaging and Dynamic Contrast Enhanced MRI

Deanna Lyn Langer1,2, Theo H. van der Kwast3, Andrew J. Evans3, John Trachtenberg4, Brian C. Wilson5, Masoom A. Haider1,2

1Joint Department of Medical Imaging, Princess Margaret Hospital, University Health Network and Mount Sinai Hospital, Toronto, Ontario, Canada; 2Institute of Medical Sciences, University of Toronto, Toronto, Ontario, Canada; 3Department of Pathology and Laboratory Medicine, Toronto General Hospital, University Health Network, Toronto, Ontario, Canada; 4Department of Surgical Oncology, Princess Margaret Hospital, University Health Network, Toronto, Ontario, Canada; 5Medical Biophysics, University of Toronto, Ontario Cancer Institute, Toronto, Ontario, Canada

A logistic regression (LR) model for identifying prostate cancer (PCa) in the peripheral zone (PZ) was developed and compared to single-parameters (ADC, T2, Ktrans, ve). Pathologically-identified regions of PCa and normal PZ tissue from whole mount histology were used during model development. Areas under ROC curves were compared: ADC was the top single-parameter; significantly higher than Ktrans or ve, and higher than T2 (not significant). The LR-model included ADC, T2, and Ktrans and performed significantly better than T2, Ktrans, and ve, and higher than ADC (not significant). This method permits tumor probability mapping, providing a quantitative method to combine modalities.