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A novel predictor of clinical progression in patients on active surveillance for prostate cancer
- Tan, Guan Hee;
- Finelli, Antonio;
- Ahmad, Ardalan;
- Wettstein, Marian S;
- Chandrasekar, Thenappan;
- Zlotta, Alexandre R;
- Fleshner, Neil E;
- Hamilton, Robert J;
- Kulkarni, Girish S;
- Ajib, Khaled;
- Nason, Gregory;
- Perlis, Nathan
Published Web Location
https://doi.org/10.5489/cuaj.6122Abstract
INTRODUCTION: Active surveillance (AS) is standard of care in low-risk prostate cancer (PCa). This study describes a novel total cancer location (TCLo) density metric and aims to determine its performance in predicting clinical progression (CP) and grade progression (GP). METHODS: This was a retrospective study of patients on AS after confirmatory biopsy (CBx). We excluded patients with Gleason ≥7 at CBx and <2 years followup. TCLo was the number of locations with positive cores at diagnosis (DBx) and CBx. TCLo density was TCLo/prostate volume (PV). CP was progression to any active treatment while GP occurred if Gleason ≥7 was identified on repeat biopsy or surgical pathology. Independent predictors of time to CP or GP were estimated with Cox regression. Kaplan-Meier analysis compared progression-free survival (PFS) curves between TCLo density groups. Test characteristics of TCLo density were explored with receiver operating characteristic (ROC) curves. RESULTS: We included 181 patients who had CBx from 2012-2015 and met inclusion criteria. The mean age of patients was 62.58 years (standard deviation [SD] 7.13) and median followup was 60.9 months (interquartile range [IQR] 23.4). A high TCLo density score (>0.05) was independently associated with time to CP (hazard ratio [HR] 4.70; 95% confidence interval [CI] 2.62-8.42; p<0.001) and GP (HR 3.85; 95% CI 1.91-7.73; p<0.001). ROC curves showed TCLo density has greater area under the curve than number of positive cores at CBx in predicting progression. CONCLUSIONS: TCLo density is able to stratify patients on AS for risk of CP and GP. With further validation, it could be added to the decision-making algorithm in AS for low-risk localized PCa.
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