We developed a mathematical “prostate cancer (PCa) conditions simulating” predictive
model (PCP-SMART), from which we derived a novel PCa predictor (prostate cancer risk
determinator [PCRD] index) and a PCa risk equation. We used these to estimate the
probability of finding PCa on prostate biopsy, on an individual basis.
Materials and Methods
A total of 371 men who had undergone transrectal ultrasound-guided prostate biopsy
were enrolled in the present study. Given that PCa risk relates to the total prostate-specific
antigen (tPSA) level, age, prostate volume, free PSA (fPSA), fPSA/tPSA ratio, and
PSA density and that tPSA ≥ 50 ng/mL has a 98.5% positive predictive value for a PCa
diagnosis, we hypothesized that correlating 2 variables composed of 3 ratios (1, tPSA/age;
2, tPSA/prostate volume; and 3, fPSA/tPSA; 1 variable including the patient’s tPSA
and the other, a tPSA value of 50 ng/mL) could operate as a PCa conditions imitating/simulating
model. Linear regression analysis was used to derive the coefficient of determination
(R2), termed the PCRD index. To estimate the PCRD index's predictive validity, we used
the χ2 test, multiple logistic regression analysis with PCa risk equation formation, calculation
of test performance characteristics, and area under the receiver operating characteristic
curve analysis using SPSS, version 22 (P < .05).
The biopsy findings were positive for PCa in 167 patients (45.1%) and negative in
164 (44.2%). The PCRD index was positively signed in 89.82% positive PCa cases and
negative in 91.46% negative PCa cases (χ2 test; P < .001; relative risk, 8.98). The sensitivity was 89.8%, specificity was 91.5%, positive
predictive value was 91.5%, negative predictive value was 89.8%, positive likelihood
ratio was 10.5, negative likelihood ratio was 0.11, and accuracy was 90.6%. Multiple
logistic regression revealed the PCRD index as an independent PCa predictor, and the
formulated risk equation was 91% accurate in predicting the probability of finding
PCa. On the receiver operating characteristic analysis, the PCRD index (area under
the curve, 0.926) significantly (P < .001) outperformed other, established PCa predictors.
The PCRD index effectively predicted the prostate biopsy outcome, correctly identifying
9 of 10 men who were eventually diagnosed with PCa and correctly ruling out PCa for
9 of 10 men who did not have PCa. Its predictive power significantly outperformed
established PCa predictors, and the formulated risk equation accurately calculated
the probability of finding cancer on biopsy, on an individual patient basis.