Computationally Derived Image Signature of Stromal Morphology Is Prognostic of Prostate Cancer Recurrence Following Prostatectomy in African American Patients.
retrospective_cohort · Level III
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- Record sourced from PubMed, PMID 32139401.
- Also identified by DOI 10.1158/1078-0432.CCR-19-2659 and PMC identifier 7165025.
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Abstract
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. This study included 334 radical prostatectomy patients subdivided into training (V<sub>T</sub>, <i>n</i> = 127), validation 1 (V<sub>1</sub>, <i>n</i> = 62), and validation 2 (V<sub>2</sub>, <i>n</i> = 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<sub>T</sub> to predict biochemical recurrence-free survival based on these features. Performance of these models was assessed using V<sub>1</sub> and V<sub>2</sub>, both overall and in population-specific cohorts. An AA-specific, automated stromal signature, AAstro, was prognostic of recurrence risk in both independent validation datasets [V<sub>1,AA</sub>: AUC = 0.87, HR = 4.71 (95% confidence interval (CI), 1.65-13.4), <i>P</i> = 0.003; V<sub>2,AA</sub>: AUC = 0.77, HR = 5.7 (95% CI, 1.48-21.90), <i>P</i> = 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. 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.
Medical subject headings
- Black or African American
- Biomarkers, Tumor
- Machine Learning
- Neoplasm Recurrence, Local
- Prostatectomy
- Prostatic Neoplasms
- Stromal Cells