Can Tumour Volume Percentage in Radical Prostatectomy Predict Cancer Biochemical Recurrence? Determining a Cut-off Point and Composite Risk Factors Approach.

Background: Incidence of biochemical recurrence (BCR) after radical prostatectomy is relatively high and overall survival can be poor. Debate exists whether tumour volume predicts BCR and when treatments should be administered. In this study, we aimed to i) assess the impact of tumour volume percentage (TVP) as a predictor for BCR, ii) determine TVP cut-off point for BCR and iii) evaluate single and composite predictors of BCR.

Methods: From March 2000 to December 2013, 1777 patients underwent laparoscopic radical prostatectomy for localized prostate cancer. None received neoadjuvant or adjuvant therapy. One hundred and forty-six patients experienced BCR (range 3 months-10 years). Using D'Amico classification, 146 matched controls without BCR were compared. Liu cut-point analysis was used to identify TVP with optimal sensitivity and specificity. Single and composite BCR risk predictors were analyzed using Cox hazards regression in cases and controls.

Results: Median TVP was 10% (range 1-90%). Most of BCR peaked after 3 years of follow-up. TVP ≥8% was an independent predictor of BCR with HR 1.6 (p= 0.001, 95% CI= 1.11-2.48). TVP of 8% was associated with the highest accuracy: sensitivity 74% and specificity 53% (ROC curve= 0.7). At TVP ≥8%, pathological stage pT3 was associated with 1.7-fold higher risk of BCR compared to T2. Lymph node invasion was associated with 1.4-fold higher risk of BCR compared to no invasion. Combining TVP ≥8%, pT3 and lymph node invasion, HR jumped to 3.73 (p< 0.001, 95% CI= 2.27-6.14), whereas combining TVP ≥8%, positive surgical margin and lymph node invasion, HR was 2.68 (p= 001, 95% CI= 1.50-4.77).

Conclusion: TVP can be used as an independent predictor of BCR after radical prostatectomy for prostate cancer. TVP cut-point of ≥8% allows the best discrimination. TVP should be considered in combination with other clinico-pathological factors to improve prediction of long-term oncological outcomes and to stratify BCR risk.