RESUMEN
The biopsy Gleason score is an important prognostic marker for prostate cancer patients. It is, however, subject to substantial variability among pathologists. Artificial intelligence (AI)-based algorithms employing deep learning have shown their ability to match pathologists' performance in assigning Gleason scores, with the potential to enhance pathologists' grading accuracy. The performance of Gleason AI algorithms in research is mostly reported on common benchmark data sets or within public challenges. In contrast, many commercial algorithms are evaluated in clinical studies, for which data are not publicly released. As commercial AI vendors typically do not publish performance on public benchmarks, comparison between research and commercial AI is difficult. The aims of this study are to evaluate and compare the performance of top-ranked public and commercial algorithms using real-world data. We curated a diverse data set of whole-slide prostate biopsy images through crowdsourcing containing images with a range of Gleason scores and from diverse sources. Predictions were obtained from 5 top-ranked public algorithms from the Prostate cANcer graDe Assessment (PANDA) challenge and 2 commercial Gleason grading algorithms. Additionally, 10 pathologists (A.C., C.R., J.v.I., K.R.M.L., P.R., P.G.S., R.G., S.F.K.J., T.v.d.K., X.F.) evaluated the data set in a reader study. Overall, the pairwise quadratic weighted kappa among pathologists ranged from 0.777 to 0.916. Both public and commercial algorithms showed high agreement with pathologists, with quadratic kappa ranging from 0.617 to 0.900. Commercial algorithms performed on par or outperformed top public algorithms.
RESUMEN
OBJECTIVE: To explore the programmed death-ligand 1 (PD-L1) expression in varied subtypes of pituitary neuroendocrine tumors with assessment of their clinical behavior at diagnosis and follow-up. METHODS: We conducted a retrospective monocentric study, including all patients operated in the Academic Hospital of Angers (France) for a pituitary neuroendocrine tumor between 2012 and 2018. PDL-1 immunostaining was performed using a European Conformity-In Vitro Diagnostic-labeled anti-PDL1 antibody (clone 22C3). PD-L1 immunostaining was evaluated as the percentage of tumor cells showing positive membrane staining, into four grades: grade 0 = < 1%, grade 1 = 1 to 5%, grade 2 = 6 to 49% and grade 3 = ≥ 50%. PD-L1 expression was compared with tumor features (secretion, proliferation, invasion) and outcome. RESULTS: The study included 139 pituitary neuroendocrine tumors, including 84 (60%) nonfunctioning adenomas. Twenty-five pituitary neuroendocrine tumors were PD-L1 positive (18%), including 3 grade 3, 8 grade 2 and 14 grade 1. PD-L1 expression was not different between functioning and nonfunctioning adenomas (p = 0.26). Among 16 tumors with proliferative markers (Ki-67 ≥ 3% and p53 positive), only one was PD-L1 positive. CONCLUSION: In our series, PD-L1 was expressed in a rather small proportion of PitNET (18%), and this immune marker was not associated with any biological characteristic or behavior of the pituitary tumors. Thus, PD-L1 staining may be necessary before considering PD-L1 blockage in pituitary neuroendocrine tumors, in case of therapeutic impasse.