  Gleason grading of prostate cancer is an important prognostic factor but
suffers from poor reproducibility, particularly among non-subspecialist
pathologists. Although artificial intelligence (A.I.) tools have demonstrated
Gleason grading on-par with expert pathologists, it remains an open question
whether A.I. grading translates to better prognostication. In this study, we
developed a system to predict prostate-cancer specific mortality via A.I.-based
Gleason grading and subsequently evaluated its ability to risk-stratify
patients on an independent retrospective cohort of 2,807 prostatectomy cases
from a single European center with 5-25 years of follow-up (median: 13,
interquartile range 9-17). The A.I.'s risk scores produced a C-index of 0.84
(95%CI 0.80-0.87) for prostate cancer-specific mortality. Upon discretizing
these risk scores into risk groups analogous to pathologist Grade Groups (GG),
the A.I. had a C-index of 0.82 (95%CI 0.78-0.85). On the subset of cases with a
GG in the original pathology report (n=1,517), the A.I.'s C-indices were 0.87
and 0.85 for continuous and discrete grading, respectively, compared to 0.79
(95%CI 0.71-0.86) for GG obtained from the reports. These represent
improvements of 0.08 (95%CI 0.01-0.15) and 0.07 (95%CI 0.00-0.14) respectively.
Our results suggest that A.I.-based Gleason grading can lead to effective
risk-stratification and warrants further evaluation for improving disease
management.
