Early detection of Prostate Cancer with Prostate Specific Antigen > 4 ng/ml : A Multivariate Logistic Regression

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Published 2015-04-01
Prahara Yuri Sungsang Rochadi

Abstract

Background:Early detection of prostate cancer is a possible means of decreasing the mortality and increasing the quality of life. Objective :To determine whether the prostate specific antigen (PSA), abnormal DRE, family history, age, and prostate volume could increase the specificity and sensitivity of screening for prostate cancer. Methods :We included 92 patients with PSA > 4 ng/ml between January and December 2011 in Sardjito Hospital. Patients received prostate biopsy due to having abnormal serum prostate specific antigen (PSA) level. The relationship between the possibility of prostate cancer and the following variables were evaluated including: age; PSA level, prostate volume, DRE finding and family history. By using chi-square analysis, multiple logistic regression and receiver operating characteristic (ROC) curve were drawn based on the predictive scoring equation to predict the possibility of prostate cancer. All analyses were performed with SPSS, version 18.0. Results:We analyzed 92 patients with PSA > 4 ng/ml. It showed the relationship between the possibility of prostate cancer and the following variables, including : age (p < 0.001), PSA level (p < 0,001), DRE finding (p < 0.001) family history (p < 0,001) except prostate volume (p = 0.398). Using a predictive equation, P = 1/(1-e-X), where X= -3,821 +1.846 (if DRE positive) + 2,488 ( if family history positive ) + 1.718 ( when PSA > 10 ) + 1.414 ( when age > 68), followed by receiver-operating characteristic curve analysis, it showed the sensitivity 90,4% and specificity 85 % in predicting the possibility of prostate cancer. Conclusion:  Age,  DRE  finding,  PSA  and  family  history  are  factors  associated prostate cancer. They can be used as independent predictor to predict prostate cancer. Key words: Logistic regression, early detection,prostate cancer


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Department of Urology, Faculty of Medicine/Airlangga University