  Prostate cancer pathology plays a crucial role in clinical management but is
time-consuming. Artificial intelligence (AI) shows promise in detecting
prostate cancer and grading patterns. We tested an AI-based digital twin of a
pathologist, vPatho, on 2,603 histology images of prostate tissue stained with
hematoxylin and eosin. We analyzed various factors influencing tumor-grade
disagreement between vPatho and six human pathologists. Our results
demonstrated that vPatho achieved comparable performance in prostate cancer
detection and tumor volume estimation, as reported in the literature.
Concordance levels between vPatho and human pathologists were examined.
Notably, moderate to substantial agreement was observed in identifying
complementary histological features such as ductal, cribriform, nerve, blood
vessels, and lymph cell infiltrations. However, concordance in tumor grading
showed a decline when applied to prostatectomy specimens (kappa = 0.44)
compared to biopsy cores (kappa = 0.70). Adjusting the decision threshold for
the secondary Gleason pattern from 5% to 10% improved the concordance level
between pathologists and vPatho for tumor grading on prostatectomy specimens
(kappa from 0.44 to 0.64). Potential causes of grade discordance included the
vertical extent of tumors toward the prostate boundary and the proportions of
slides with prostate cancer. Gleason pattern 4 was particularly associated with
discordance. Notably, grade discordance with vPatho was not specific to any of
the six pathologists involved in routine clinical grading. In conclusion, our
study highlights the potential utility of AI in developing a digital twin of a
pathologist. This approach can help uncover limitations in AI adoption and the
current grading system for prostate cancer pathology.
