Abstract LB-285: Computational pathology for predicting prostate cancer recurrence

Background: Conventional methods for predicting prostate cancer (PCa) aggressiveness, which rely heavily on the Gleason score of architectural pattern, remain imperfect. We have developed an approach that combines machine vision and machine learning analysis of routine HE 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr LB-285. doi:10.1158/1538-7445.AM2015-LB-285