A quantitative image analysis model of prostate biopsies for predicting clinical risk in men enrolled in an active surveillance program.

Abstract

111 Background: Quantitative image analysis of the prostate needle biopsy (PNB) has proven to be a robust and predictive platform for prostate cancer (PCa) prognosis. We sought to determine the performance of quantitative metrics in identifying which patients enrolled in an active surveillance (AS) program are most likely to present with significant clinical risk, including subsequent biopsy Gleason grade (GG) upgrading and/or a short (less than 24 month) prostate-specific antigen (PSA) doubling time (PSADT). Methods: One hundred sixty two AS patients (median age 70, 94% cT1-T2a, 85% <=GS6, median PSA 5.9 ng/mL) with overall 8 year median follow up and available diagnostic PNB specimens were analyzed. Computerized image analysis derived quantitative biometric features representing PCa morphology and immunofluorescent (IF) biomarkers from the PNB. Multivariate models predicting either GG upgrading on a subsequent PNB or a PSADT less than 24 months were evaluated. The AUC/concordance index (CI), sensitivity, specificity and hazard ratio (HR) were used to assess performance. Results: Univariate distribution of selective features, notably expression levels of AR and Ki67, were reflective of a low risk cohort.A multivariate model with three quantitative imaging metrics was trained with a CI of 0.77. Men at high risk within 24 months of the PNB were identified with 80% sensitivity and 73% specificity, HR of 5.7. The most important feature measured the relative proportion of tumor epithelial nuclei that were both Androgen Receptor and alpha-methylacyl-CoA racemase positive. The other two features were morphological assessments of epithelial cellular area compared to luminal area. Of note, clinical features such as age, GG and PSA were not selected in competition with the imaging metrics. Conclusions: Quantitative image analysis of morphology and IF biomarker expression in the PNB outperformed standard clinical features in a multivariate model to accurately predict which AS patients are at risk for Gleason upgrading and/or a shortened PSADT. Identifying such patients may prove beneficial in the primary treatment decision process.