Development of Statistical Model for Predicting Prostate Cancer in Patients Requiring Prostate Biopsy.
- Author:
Taek Woo CHO
1
;
Se Hyun KIM
;
Dong PARK
Author Information
1. Department of Urology, College of Medicine, Pochon CHA University, Seongnam, Korea. dsparkmd@cha.ac.kr
- Publication Type:Original Article
- Keywords:
Prostate cancer;
Biopsy;
Statistical mode
- MeSH:
Biopsy*;
Hematuria;
Humans;
Logistic Models;
Models, Statistical*;
Nocturia;
Prostate*;
Prostatic Neoplasms*;
Prostatism;
Prostatitis
- From:Korean Journal of Urology
2004;45(10):1014-1020
- CountryRepublic of Korea
- Language:Korean
-
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.
