A novel mathematical simulation modelling method to predict the probability of finding cancer in prostate biopsy on an individual basis

Evangelos Spyropoulos, Dimitrios Kotsiris, Katherine Spyropoulos, Aggelos Panagopoulos, Evangelos Chatziplis, Ioannis Galanakis, Stamatios Mavrikos

Abstract


Purpose: To calculate the probability of finding cancer on prostate biopsy, we developed a prostate cancer (PCa) predictive statistical model (PCP-SMART), deriving a novel PCa-predictor (pcrdindex) and a pca-risk mathematical equation.

Subjects and methods: A total of 371 men were included. Since PCa-risk relates to tPSA, age, prostate volume[PV], fPSA, f/tPSA-ratio, PSAD and tPSA≥50ng/ml has 98.5% Positive-Predictive-Value(PPV) for PCa diagnosis, we hypothesized that correlating two variables, each consisting of three ratios/values including patient’s-tPSA, PSA50ng/ml, age, prostate volume, f/tPSAratio, could operate as a “PCa-conditions imitating-simulating model”. Linear regression derived the coefficient-of-determination(R2), termed PCRDindex. Statistics included x2-test, multiple logistic regression analysis, test-performance characteristics and AUC/ROC-curve analysis [SPSS-22(p<0.05)].

Results: Biopsy was PCa(+) in 45.1% and PCa(-) in 44.2%. PCRDindex signed(+) in 89.82% PCa(+) and negative in 91.46% PCa(-) cases (x2-test: p<0.001-RR: 10.52) [Sensitivity: 89.8, specificity: 91.5%, PPV: 91.5%, Negative-Predictive-Value(NPV): 89.8%, Positive-Likelihood-Ratio[LR(+)]: 10.5, Negative-Likelihood-Ratio[LR(-)]: 0.11 Accuracy: 90.6%]. Multiple logistic regression and AUC/ROC analysis revealed PCRDindex as independent PCa-predictor strongly (p<0.001) outperforming other clinically established while, the formulated risk-equation predicted 91% accurately the probability of finding cancer. 

Conclusions: PCRDindex effectively predicted prostate biopsy outcome, identifying correctly 9/10 men who indeed harbored cancer while, correctly ruling out PCa in 9/10 men without disease evidence. It significantly outperformed other established PCa-predictors while, the PCa-risk equation, accurately calculated the individual probability of finding cancer on biopsy.

Keywords


prostate cancer; PSA testing; PCP-SMART model; PCRD-Index; prostate cancer risk mathematical equation

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References


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DOI: http://dx.doi.org/10.19264/hj.v29i1.108