biostudies-literatureLeo PBLRD VANCATS NIH HHSNIBIB NIH HHSNCRR NIH HHSNCI NIH HHS35https://www.ebi.ac.uk/biostudies/studies/S-EPMC8093226biostudies-literatureUnknown5(1)Existing tools for post-radical prostatectomy (RP) prostate cancer biochemical recurrence (BCR) prognosis rely on human pathologist-derived parameters such as tumor grade, with the resulting inter-reviewer variability. Genomic companion diagnostic tests such as Decipher tend to be tissue destructive, expensive, and not routinely available in most centers. We present a tissue non-destructive method for automated BCR prognosis, termed "Histotyping", that employs computational image analysis of morphologic patterns of prostate tissue from a single, routinely acquired hematoxylin and eosin slide. Patients from two institutions (n = 214) were used to train Histotyping for identifying high-risk patients based on six features of glandular morphology extracted from RP specimens. Histotyping was vaNPJ precision oncologyComputer extracted gland features from H&E predicts prostate cancer recurrence comparably to a genomic companion diagnostic test: a large multi-site study.PMC8093226I01 BX004121U24 CA199374UL1 TR002548U54 CA254566R43 EB028736C06 RR012463Janaki NEl-Fahmawi AKim JElliott RLee DKhani FFeldman MYamoah KFarre XJambor IJanowczyk ABera KAronen HJMagi-Galluzzi CRebbeck TREttala OPurysko AGupta STewari AEklund LShiradkar RMadabhushi ANc Shih NTaimen PKlein EMerisaari HBostrom PJLeo PShahait MRobinson BDLal PFu PfalseComputer extracted gland features from H&E predicts prostate cancer recurrence comparably to a genomic companion diagnostic test: a large multi-site study.Existing tools for post-radical prostatectomy (RP) prostate cancer biochemical recurrence (BCR) prognosis rely on human pathologist-derived parameters such as tumor grade, with the resulting inter-reviewer variability. Genomic companion diagnostic tests such as Decipher tend to be tissue destructive, expensive, and not routinely available in most centers. We present a tissue non-destructive method for automated BCR prognosis, termed "Histotyping", that employs computational image analysis of morphologic patterns of prostate tissue from a single, routinely acquired hematoxylin and eosin slide. Patients from two institutions (n = 214) were used to train Histotyping for identifying high-risk patients based on six features of glandular morphology extracted from RP specimens. Histotyping was va2021-01-01T00:00:00Z2021 May2026-06-02T18:34:22.08Z2026-04-19T03:09:21.466ZS-EPMC80932263394183010.1038/s41698-021-00174-3