Development of Statistical Model for Predicting Prostate Cancer in Patients Requiring Prostate Biopsy

Cho, Taek Woo; Kim, Se Hyun; Park, Dong

Abstract

PURPOSE: Patients with an abnormal digital rectal examination(DRE) or elevated serum prostate specific antigen(PSA) level proceed to a transrectal biopsy of the prostate. However, cancer detection is not predictable. There is a need to develop a statistical model for predicting the likelihood of prostate cancer for there to be confidence about the result of a biopsy.
MATERIALS AND METHODS
Patients with prostatism were evaluated based upon the recommendation of the International Consultation on benign prostatic hyperplasia(BPH). Amongst the patients evaluated, 141 revealed an abnormal DRE and/or serum PSA. A transrectal ultrasonography(TRUS) and transrectal biopsy was performed in all the patients. 38 of the above were diagnosed with prostate cancer and 103 with BPH or prostatitis. A logistic regression model was used to identify the variables with the most independent influence on prostate cancer and determine the most parsimonious combination of variables for predicting prostate cancer.
RESULTS
Age, hematuria, nocturia and a combination of urinary symptoms (incomplete emptying, frequency, urgency and nocturia), DRE, PSA and TRUS-hypoechoic lesion were significant variables for separately predicting prostate cancer. Among these, age, DRE, PSA and TRUS-hypoechoic lesion were independent predictors. The probability of prostate cancer(P) =exp(-9.7770+0.0807xage+1.4079xDRE+0.0257xPSA+1.0904xTRUS- hypoechoic lesion)/{(1+exp(-9.7770+0.0807xage+1.4079xDRE+0.0257xPSA+1.0904xTRUS-hypoechoic lesion)}.
CONCLUSIONS
A useful predictive model of prostate cancer has been developed using logistic regression analysis. This model suggests that patients with a high probability(P), but negative biopsy, would require a repeat biopsy. However, a low probability(P), and negative biopsy, would be suggestive of no hidden disease.