Computer extracted gland features from H&E predicts prostate cancer recurrence comparably to a genomic companion diagnostic test: a large multi-site study.

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Version: Final published version
License: CC BY 4.0
Serval ID
serval:BIB_E116AF3CB7A8
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Computer extracted gland features from H&E predicts prostate cancer recurrence comparably to a genomic companion diagnostic test: a large multi-site study.
Journal
NPJ precision oncology
Author(s)
Leo P., Janowczyk A., Elliott R., Janaki N., Bera K., Shiradkar R., Farré X., Fu P., El-Fahmawi A., Shahait M., Kim J., Lee D., Yamoah K., Rebbeck T.R., Khani F., Robinson B.D., Eklund L., Jambor I., Merisaari H., Ettala O., Taimen P., Aronen H.J., Boström P.J., Tewari A., Magi-Galluzzi C., Klein E., Purysko A., Nc Shih N., Feldman M., Gupta S., Lal P., Madabhushi A.
ISSN
2397-768X (Print)
ISSN-L
2397-768X
Publication state
Published
Issued date
03/05/2021
Peer-reviewed
Oui
Volume
5
Number
1
Pages
35
Language
english
Notes
Publication types: Journal Article
Publication Status: epublish
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 validated for post-RP BCR prognosis on a separate set of n = 675 patients from five institutions and compared against Decipher on n = 167 patients. Histotyping was prognostic of BCR in the validation set (p < 0.001, univariable hazard ratio [HR] = 2.83, 95% confidence interval [CI]: 2.03-3.93, concordance index [c-index] = 0.68, median years-to-BCR: 1.7). Histotyping was also prognostic in clinically stratified subsets, such as patients with Gleason grade group 3 (HR = 4.09) and negative surgical margins (HR = 3.26). Histotyping was prognostic independent of grade group, margin status, pathological stage, and preoperative prostate-specific antigen (PSA) (multivariable p < 0.001, HR = 2.09, 95% CI: 1.40-3.10, n = 648). The combination of Histotyping, grade group, and preoperative PSA outperformed Decipher (c-index = 0.75 vs. 0.70, n = 167). These results suggest that a prognostic classifier for prostate cancer based on digital images could serve as an alternative or complement to molecular-based companion diagnostic tests.
Pubmed
Web of science
Open Access
Yes
Create date
11/05/2021 13:19
Last modification date
08/08/2024 7:41
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