Abstract #1226

Quantitative and Automated MRI Cancer Risk Maps used for Identification of Prostate Cancer Progression

Matthew Gibbons¹, Janet E Cowan², Peter R Carroll², Matthew R Cooperberg², and Susan M Noworolski¹

¹Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States, ²Department of Urology, University of California, San Francisco, San Francisco, CA, United States

Synopsis

Keywords: Prostate, CancerThis study’s objective was to determine whether automated mpMRI cancer risk maps could identify prostate cancer progression during active surveillance. Derived lesion masks were used to analyze factors for progression. A decision tree model for progression was generated with sensitivity = 0.84, specificity = 0.56, and ROC AUC 0.75. The identification results indicate the potential of mpMRI and MRI cancer risk maps to assist in identifying progression during prostate cancer active surveillance.

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