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A Device for Predicting Prostate Cancer Risk: A Logistic Regressi
Reproductive System & Sexual Disorders: Current Research

Reproductive System & Sexual Disorders: Current Research
Open Access

ISSN: 2161-038X

+44 1300 500008

Research Article - (2016) Volume 0, Issue 0

A Device for Predicting Prostate Cancer Risk: A Logistic Regression

Abstract

Background: Early detection of prostate cancer is a possible means of decreasing the mortality and increasing the quality of life.

Methods: We included 92 patients retrospectively in Sardjito Hospital. Patients received prostate biopsy due to having abnormal serum prostate specific antigen (PSA) level (>4 ng/ml) and DRE. 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 regressions, receiver operating characteristic (ROC) curve were drawn based on the predictive scoring equation to predict the possibility of prostate cancer. Using the predictive equation, we design a normogram for predicting prostate cancer risk called prostate cancer risk calculator. 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) and prostate volume (p=0.04). Using a predictive equation, we design a calculator for predicting prostate cancer 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, prostate volume, PSA, DRE finding and family history are factors associated prostate cancer. They can be used as independent predictor to predict prostate cancer.

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Keywords: Early detection; Logistic regression; Prostate cancer risk calculator



Copyright: This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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