Automated Prostate Cancer Identification Facilitates Prognosis Marker Assessment in 11,845 Prostate Cancers Using Artificial Intelligence and BLEACH&STAIN Multiplex Fluorescence Immunohistochemistry

Autor: N C Blessin, J Müller, T Mandelkow, E Bady, M C Lurati, M Lennartz, M Graefen, G Sauter, S Steurer
Rok vydání: 2022
Předmět:
Zdroj: American Journal of Clinical Pathology. 158:S81-S81
ISSN: 1943-7722
0002-9173
DOI: 10.1093/ajcp/aqac126.168
Popis: 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 Conclusion Automated prostate cancer identification enables fully automated prognosis marker assessment in routine clinical practice using deep learning and BLEACH&STAIN mfIHC.
Databáze: OpenAIRE
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