130 Background: The influence of spatial distribution patterns of organ-confined Prostate Cancer (PCa) on the biochemical recurrence (BCR) remains unclear. Therefore, we conducted a study investigating the association between distribution patterns and BCR-free rate in organ-confined PCa. Methods: The anatomical distribution of PCa in 743 men with pT1-pT3N0 and without neoadjuvant therapy was analyzed to determine 20 groups with similar distribution patterns of PCa. Then, 245 men with pT2N0R0 were considered for prognostic evaluation. Spatial distribution patterns of PCa were evaluated using a cMDX-based map model of the prostate. A comparison analysis including 552,049 compare operations was performed to assist the similarity levels of the distribution patterns. K-mean cluster analysis was applied to determine 20 groups with similar distribution patterns. A decision tree-Analysis was performed to divide these groups according to BCR. BCR-free survival was compared. Predictors of progression were investigated using a Cox proportional hazards model. Results: BCR was occurred in 8.2% men with pT2N0R0 PCa. In decision tree analysis, certain PCa distribution patterns revealed no BCR at a median follow-up of 60 mo. (IQR: 42.3-77.0) In univariate and multivariate analysis, the prostate volume, the distribution patterns were an independent predictor for BCR in univariate and multivariate, whereas tumor stage, Gleason score, PSA, relative tumor volume were not. When patients with pT2R0 were stratified according to PCa distribution patterns, the presence of BCR-negative PCa distribution patterns was significantly associated with no risk of BCR by comparison to BCR-associated PCa distribution patterns (P=0.001). Conclusions: PCa distribution patterns provide a prognostic value for BCR. Distribution patterns of PCa can be applied to create more meaningfully predictive pathological T2 sub-divisions than current pT2 prostate cancer sub-stages.
Spatial Distribution Patterns of Malaria Vectors and Sample Size Determination in Spatially Heterogeneous Environments: A Case Study in the West Kenyan Highland
