Computationally Derived Image Signature of Stromal Morphology Is Prognostic of Prostate Cancer Recurrence Following Prostatectomy in African American Patients

Abstract

Purpose: Between 30%-40% of patients with prostate cancer experience disease recurrence following radical prostatectomy. Existing clinical models for recurrence risk prediction do not account for population-based variation in the tumor phenotype, despite recent evidence suggesting the presence of a unique, more aggressive prostate cancer phenotype in African American (AA) patients. We investigated the capacity of digitally measured, population-specific phenotypes of the intratumoral stroma to create improved models for prediction of recurrence following radical prostatectomy.

Experimental design: This study included 334 radical prostatectomy patients subdivided into training (V_T, n = 127), validation 1 (V₁, n = 62), and validation 2 (V₂, n = 145). Hematoxylin and eosin-stained slides from resected prostates were digitized, and 242 quantitative descriptors of the intratumoral stroma were calculated using a computational algorithm. Machine learning and elastic net Cox regression models were constructed using V_T to predict biochemical recurrence-free survival based on these features. Performance of these models was assessed using V₁ and V₂, both overall and in population-specific cohorts.

Results: An AA-specific, automated stromal signature, AAstro, was prognostic of recurrence risk in both independent validation datasets [V_1,AA: AUC = 0.87, HR = 4.71 (95% confidence interval (CI), 1.65-13.4), P = 0.003; V_2,AA: AUC = 0.77, HR = 5.7 (95% CI, 1.48-21.90), P = 0.01]. AAstro outperformed clinical standard Kattan and CAPRA-S nomograms, and the underlying stromal descriptors were strongly associated with IHC measurements of specific tumor biomarker expression levels.

Conclusions: Our results suggest that considering population-specific information and stromal morphology has the potential to substantially improve accuracy of prognosis and risk stratification in AA patients with prostate cancer.

Figure 1:. Dataset preparation, analysis, and prognostic model construction.

Paraffin-embedded, resected prostate glands were sectioned using a microtome. H&E slides were then prepared and digitally scanned (a). For each slide, a single representative cancerous region was annotated on the digital image by a pathologist (green line in a, magnified view in (b). Stroma and nuclei were then segmented from the region of interest by deep learning models, yielding class probability maps (c) and (d). These probability maps were thresholded and used to compute stromal image features (e-f). Stromal morphology descriptors were used to train prognostic models (g), which estimate biochemical recurrence risk score (h).

Figure 2:. Classifier performance in validation datasets and distributions of features prognostic for AA patients.

Kaplan-Meier survival curve estimates of predicted low versus high BCR risk groups in V_1,AA and V_2,AA for (a) AAstroENC, (b) AAstroML, and (c) Kattan nomogram. Distributions in V_T,AA of QH descriptors of stromal nuclear shape (min / max Fourier descriptor 4 and mean fractal dimension) (d), texture (Haralick mean information measure 1 and mean contrast inverse moment) (e), and nuclear arrangement (COrE mean tensor correlation and sub-graph number of isolated nodes) (f) used by AAstro models.

Figure 3:. Association of stromal morphology descriptors with biomarker expression levels.

Expression levels of selected PCa tumor biomarkers were measured using immunohistochemistry (a). These values were tested for association with stromal image features calculated from H&E-stained images. Selected pairings of biomarkers and stromal image features with significant correlation are shown in (b), with prognostic stromal features highlighted in green. Scatter plot visualizations of correlation between prognostic stromal features and biomarker expression levels are shown in (c)-(e).