Automated Prostate Cancer Identification Facilitates Prognosis Marker Assessment in 11, 845 Prostate Cancers Using Artificial Intelligence and BLEACH&STAIN Multiplex Fluorescence Immunohistochemistry. (9th November 2022)

• Record Type: Journal Article
• Authors: Blessin, N C; Müller, J; Mandelkow, T; Bady, E; Lurati, M C; Lennartz, M; Graefen, M; Sauter, G; Steurer, S
• Is Part Of: American journal of clinical pathology. Volume 158(2022)Supplement 1
• Page Start: S81
• Page End: S81
• Publication Date: 2022-11-09
• Subjects: Diagnosis, Laboratory -- Periodicals; Pathology -- Periodicals; 616.07
• DOI: 10.1093/ajcp/aqac126.168
• Languages: English
• ISSNs: 0002-9173

Abstract

Abstract: Introduction/Objective: Although most prostate cancers behave in an indolent manner, a small proportion is highly aggressive. To evaluate the patient's risk, several prognosis parameters, that can be accompanied by a high interobserver variability has been established. A reproducible prognostic evaluation is lacking. Methods/Case Report: To enable automated prognosis marker quantification, we have developed and validated a framework for automated prostate cancer detection that comprises three different artificial intelligence analysis steps and an algorithm for cell-distance analysis of BLEACH&STAIN multiplex fluorescence immunohistochemistry (mfIHC). We have used the analysis framework to measure PSA, PSMA, INSM1, AR, Ki-67, CD56, Chromogranin A, Synaptophysin, CD8 in a cohort of 11, 845 prostate cancers. Results (if a Case Study enter NA): The Ki-67 labeling index provided the strongest prognostic information among all analyzed prognosis marker in 11, 845 successfully analyzed prostate cancers (p<0.001 each). The combined analysis of the Ki67-LI and Gleason grades obtained on identical tissue spots showed that the Ki67-LI added significant additional prognostic information in case of classical ISUP grades (AUC:0.82 [p=0.002]) and quantitative Gleason grades (AUC:0.83 [p=0.018]). Several combinations of these 8 prognosis markers were combined to prognosis scores and used for unsupervised clustering to identify a proportion of prostate cancers with a particularly poor prognosis (p<0.001 each). Conclusion: Automated prostate cancer identification enables fully automated prognosis marker assessment in routine clinical practice using deep learning and BLEACH&STAIN mfIHC.