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

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.

• Authors: 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
• Journal: NPJ precision oncology (NPJ Precis Oncol) Vol. 5 Issue 1 Pg. 35 (May 03 2021) ISSN: 2397-768X [Print] England
• PMID: 33941830 (Publication Type: Journal Article)