Convolutional Neural Network Quantification of Gleason Pattern 4 and Association With Biochemical Recurrence in Intermediate-Grade Prostate Tumors.

Differential classification of prostate cancer grade group (GG) 2 and 3 tumors remains challenging, likely because of the subjective quantification of the percentage of Gleason pattern 4 (%GP4). Artificial intelligence assessment of %GP4 may improve its accuracy and reproducibility and provide information for prognosis prediction. To investigate this potential, a convolutional neural network (CNN) model was trained to objectively identify and quantify Gleason pattern (GP) 3 and 4 areas, estimate %GP4, and assess whether CNN-predicted %GP4 is associated with biochemical recurrence (BCR) risk in intermediate-risk GG 2 and 3 tumors.