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.

Multiplex Immunofluorescence and the Digital Image Analysis Workflow for Evaluation of the Tumor Immune Environment in Translational Research

“…For example, when grading prostate cancer, some characteristics are associated with tumor aggressiveness, such as cell features, tissue architecture, and image complexity, which are important for determining the Gleason patterns. However, for some challenging cases, these histological features can be influenced by interobserver variability, and although M2 macrophages and regulatory T cells have been suggested as part of the immune-suppressive mechanisms that contribute to cancer progression (51), tumor immune environment characteristics in prostate cancer are still under investigation (52,53). On the other hand, in breast cancer samples and tumor immune environment, has been described the dual role of the immune system in tumor development/progression and inhibition (54)(55)(56).…”

Computational pathology in ovarian cancer

“…Computational analyses of H&E images have been applied for the prediction of patient survival and recurrence. Deep learning-based algorithms have also been reported to predict prostate cancer recurrence comparably to a genomic companion diagnostic test (78). Other studies utilizing deep learning-based classification involve determining histologic subtype in ovarian cancer (79), predicting platinum resistance in ovarian cancer (41,80), predicting survival outcome in patients with mesothelioma (48), predicting metastatic recurrence and death in patients with primary melanoma (81) and colon cancer (46), predicting lung cancer recurrence after surgical resection to identify patients who should receive additional adjuvant therapy (52,82), and predicting response to ipilimumab immunotherapy in patients with malignant melanoma (83).…”

Multiplex Immunofluorescence and the Digital Image Analysis Workflow for Evaluation of the Tumor Immune Environment in Translational Research

“…For example, when grading prostate cancer, some characteristics are associated with tumor aggressiveness, such as cell features, tissue architecture, and image complexity, which are important for determining the Gleason patterns. However, for some challenging cases, these histological features can be influenced www.videleaf.com by interobserver variability, and although M2 macrophages and regulatory T cells have been suggested as part of the immunesuppressive mechanisms that contribute to cancer progression [53], tumor immune environment characteristics in prostate cancer are still under investigation [54,55]. On the other hand, in breast cancer samples and tumor immune environment, has been described the dual role of the immune system in tumor development/progression and inhibition [56][57][58].…”