A novel predictor of clinical progression in patients on active surveillance for prostate cancer


  • Guan Hee Tan
  • Antonio Finelli
  • Ardalan Ahmad
  • Marian S. Wettstein
  • Thenappan Chandrasekar
  • Alexandre R. Zlotta
  • Neil E. Fleshner
  • Robert J. Hamilton
  • Girish S. Kulkarni
  • Khaled Ajib
  • Gregory Nason
  • Nathan Perlis




prostate cancer, active surveillance


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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How to Cite

Tan, G. H., Finelli, A., Ahmad, A., Wettstein, M. S., Chandrasekar, T. ., Zlotta, A. R., Fleshner, N. E., Hamilton, R. J., Kulkarni, G. S., Ajib, K., Nason, G., & Perlis, N. (2019). A novel predictor of clinical progression in patients on active surveillance for prostate cancer. Canadian Urological Association Journal, 13(8). https://doi.org/10.5489/cuaj.6122



Original Research