학술논문
Computer extracted gland features from H&E predicts prostate cancer recurrence comparably to a genomic companion diagnostic test: a large multi-site study
Document Type
article
Author
Patrick Leo; Andrew Janowczyk; Robin Elliott; Nafiseh Janaki; Kaustav Bera; Rakesh Shiradkar; Xavier Farré; Pingfu Fu; Ayah El-Fahmawi; Mohammed Shahait; Jessica Kim; David Lee; Kosj Yamoah; Timothy R. Rebbeck; Francesca Khani; Brian D. Robinson; Lauri Eklund; Ivan Jambor; Harri Merisaari; Otto Ettala; Pekka Taimen; Hannu J. Aronen; Peter J. Boström; Ashutosh Tewari; Cristina Magi-Galluzzi; Eric Klein; Andrei Purysko; Natalie NC Shih; Michael Feldman; Sanjay Gupta; Priti Lal; Anant Madabhushi
Source
npj Precision Oncology, Vol 5, Iss 1, Pp 1-11 (2021)
Subject
Language
English
ISSN
2397-768X
Abstract
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