{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Leo P"],"funding":["BLRD VA","NCATS NIH HHS","NIBIB NIH HHS","NCRR NIH HHS","NCI NIH HHS"],"pagination":["35"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8093226"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["5(1)"],"pubmed_abstract":["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 va"],"journal":["NPJ precision oncology"],"pubmed_title":["Computer extracted gland features from H&E predicts prostate cancer recurrence comparably to a genomic companion diagnostic test: a large multi-site study."],"pmcid":["PMC8093226"],"funding_grant_id":["I01 BX004121","U24 CA199374","UL1 TR002548","U54 CA254566","R43 EB028736","C06 RR012463"],"pubmed_authors":["Janaki N","El-Fahmawi A","Kim J","Elliott R","Lee D","Khani F","Feldman M","Yamoah K","Farre X","Jambor I","Janowczyk A","Bera K","Aronen HJ","Magi-Galluzzi C","Rebbeck TR","Ettala O","Purysko A","Gupta S","Tewari A","Eklund L","Shiradkar R","Madabhushi A","Nc Shih N","Taimen P","Klein E","Merisaari H","Bostrom PJ","Leo P","Shahait M","Robinson BD","Lal P","Fu P"],"additional_accession":[]},"is_claimable":false,"name":"Computer extracted gland features from H&E predicts prostate cancer recurrence comparably to a genomic companion diagnostic test: a large multi-site study.","description":"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 va","dates":{"release":"2021-01-01T00:00:00Z","publication":"2021 May","modification":"2026-06-02T18:34:22.08Z","creation":"2026-04-19T03:09:21.466Z"},"accession":"S-EPMC8093226","cross_references":{"pubmed":["33941830"],"doi":["10.1038/s41698-021-00174-3"]}}