Neutrophil-to-lymphocyte and platelet-to-lymphocyte ratios alone or combined with prostate-specific antigen for the diagnosis of prostate cancer and clinically significant prostate cancer.

OBJECTIVE

We evaluated whether the blood parameters before prostate biopsy can diagnose prostate cancer (PCa) and clinically significant PCa (Gleason score [GS] ≥7) in our hospital.

METHODS

This study included patients with increased prostate-specific antigen (PSA) up to 20 ng/mL. The associations of neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) alone or with PSA with PCa and clinically significant PCa were analyzed.

RESULTS

We included 365 patients, of whom 52.9% (193) had PCa including 66.8% (129) with GS of ≥7. PSA density (PSAD) and PSA had better the area under the curve (AUC) of 0.722 and 0.585, respectively with =0.001 for detecting PCa compared with other blood parameters. PSA combined with PLR (PsPLR) and PSA with NLR (PsNLR) had better AUC of 0.608 and 0.610, respectively with <0.05, for diagnosing GS≥7 population, compared with PSA, free/total PSA, NLR, PLR, and PsNPLR (PSA combined with NLR and PLR). NLR and PLR did not predict PCa on multivariate analysis. For GS≥7 cancer detection, in the multivariate analysis, separate models with PSA and NLR (Model 1: PsNLR+baseline parameters) or PSA and PLR (Moder 2: PsPLR+baseline parameters) were made. Baseline parameters comprised age, digital rectal exam-positive lesions, PSA density, free/total PSA, and magnetic resonance imaging. Model 2 containing PsPLR was statistically significant (odds ratio: 2.862, 95% confidence interval: 1.174-6.975, =0.021) in finding aggressive PCa. The predictive accuracy of Model 2 was increased (AUC: 0.734, <0.001) than that when only baseline parameters were used (AUC: 0.693, <0.001).

CONCLUSION

NLR or PLR, either alone or combined with PSA, did not detect PCa. However, the combined use of PSA with PLR could find the differences between clinically significant and insignificant PCa in our retrospective study limited by the small number of samples.