Gerald Niedobitek, G. Schmitz, Manuel Fella, André Oliveira, John Theunissen, Manuela Vecsler, Jens Köllermann
The diagnosis of prostate cancer rests on the histopathological evaluation of prostate needle core biopsy specimens (NCBS) supplemented by immunohistochemistry as required. Because of the well-known interobserver variability in diagnosis and grading of prostate cancer, we have studied the capability of a commercially available artificial intelligence (AI) solution to aid in the diagnosis of PCa in a non-expert setting. For this, 2828 H&E stained NCBS slides from 249 cases were digitised to produce whole slide images (WSI) and subjected to second-read analysis using the Ibex prostate AI solution. Ground truth was established by a combination of primary pathologists' diagnoses supplemented by immunohistochemistry and external expert revision. For cancer detection, the AI solution achieved a false negative rate of 0.2%, significantly lower than that of the reporting pathologists (1.5%). False positive rates were similar for reporting pathologists (2.3%) and prostate AI solution (3.1%). A Gleason pattern 4 alert was raised by the AI solution in 2 of 29 cases (7%) of cases originally diagnosed as Gleason score 3 + 3, both of which were upheld after expert review. Because of the therapeutic consequences, the triggering of a "higher than 3 + 3 Gleason score" alert represents a useful safety feature. In conclusion, our findings support the growing evidence that by enhancing diagnostic accuracy AI-based diagnostic solutions can play a pivotal role in alleviating the increasing workload faced by pathologists due to the rising global incidence of prostate cancer.