Comparison of patients' characteristics between two stages.

<p>*This table showed most patients' basic information at their first visits (patients with watchful waiting) or latest visits before biopsies (patients with biopsies).</p>#<p>In stage two, positive rate was discussed in patients with biopsies only.</p>a<p>. Student's t-test for age, PSA, fPSA, PV, PSAD, f/t and PCaR distributions between two stages.</p>b<p>. Two-sided χ2-test or Fish's exact test for DRE findings, Hypoechoic, No. of subjects with biopsies, Positive cases and Gleason score between two stages.</p><p>Comparison of patients' characteristics between two stages.</p

ROC of our new model, PSA, PSAD and f/t. The AUC of these predictors were 0.789, 0.566, 0.664 and 0.654 respectively.

<p>ROC of our new model, PSA, PSAD and f/t. The AUC of these predictors were 0.789, 0.566, 0.664 and 0.654 respectively.</p

Multivariate analysis of the predictors of prostate cancer.

#<p>Age, PSA, fPSA, PV, PSAD, f/t, hypoechoic, DRE findings and microcalcification were included in our logistic analysis with a backward elimination scheme. Six predictors showed significant difference (p<0.05) and were included into an equation for prostate cancer risk (PCaR).</p><p>Multivariate analysis of the predictors of prostate cancer.</p

Nomogram for predicting a positive prostate biopsy.

<p>Locate patient values on each axis, and compare to the ‘Point’ axis to determine how many points are attributed to each variable. Then, locate the sum of the points for all variables on the ‘Total Points’ line to determine the individual probability of prostate cancer on the ‘PCaR’ line.</p

Management for patients with PSA from 4 to 10 ng/ml based on our new model.

<p>Management for patients with PSA from 4 to 10 ng/ml based on our new model.</p

Validation of the predictive accuracy (78.9%).

<p>Validation of the predictive accuracy (78.9%).</p

Comparison between our model and other earlier models.

<p>Comparison between our model and other earlier models.</p

Analysis between combined risk alleles and ccRCC Susceptibility.

<p>*Two-sided χ² test for either genotype distributions or allele frequencies between the cases and controls.</p><p>△Adjusted for age, gender, body mass index, smoking status, drinking status, hypertension and diabetes in logistic regression model; 95% CI: 95% confidence interval.</p><p>Analysis between combined risk alleles and ccRCC Susceptibility.</p

The basic information of the genotyped polymorphisms in nine SNPs in the <i>RhoA/ROCK1</i> and <i>Cav-1</i> associated with the ccRCC risk.

<p>a:Two-sided χ2 test for either genotype distributions or allele frequencies between the cases and controls.</p><p>b: Bonferroni FDR</p><p>c:Adjusted for age, BMI, gender, smoking status, drinking status and history of hypertension and diabetes in logistic regression model; 95% CI: 95% confidence interval</p><p>The basic information of the genotyped polymorphisms in nine SNPs in the <i>RhoA/ROCK1</i> and <i>Cav-1</i> associated with the ccRCC risk.</p

The characteristics of the 9 tSNPs in <i>Cav-1</i> and <i>RhoA/ROCK1</i>.

<p>*χ² test was used to assess Hardy–Weinberg equilibrium (HWE) in controls</p><p>The characteristics of the 9 tSNPs in <i>Cav-1</i> and <i>RhoA/ROCK1</i>.</p