Nepal Sat Prasad, Nakasato Takehiko, Fukagai Takashi, Ogawa Yoshio, Nakagami Yoshihiro, Shichijo Takeshi, Morita Jun, Maeda Yoshiko, Oshinomi Kazuhiko, Unoki Tsutomu, Noguchi Tetsuo, Inoue Tatsuki, Kato Ryosuke, Amano Satoshi, Mizunuma Moyuru, Kurokawa Masahiro, Tsunokawa Yoshiki, Yasuda Sou
Department of Urology, Department of Medicine, Showa University School of Medicine, 1-5-8 Hatanodai, Shinagawa-ku, Tokyo, Japan.
Asian J Urol. 2023 Apr;10(2):158-165. doi: 10.1016/j.ajur.2022.02.007. Epub 2022 Mar 5.
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.
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.
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).
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.
我们评估了在我院前列腺活检前的血液参数是否能够诊断前列腺癌（PCa）及临床显著性前列腺癌（Gleason评分[GS]≥7）。
本研究纳入前列腺特异性抗原（PSA）升高至20 ng/mL的患者。分析中性粒细胞与淋巴细胞比值（NLR）和血小板与淋巴细胞比值（PLR）单独或与PSA联合用于诊断PCa及临床显著性PCa的相关性。
我们纳入了365例患者，其中52.9%（193例）患有PCa，包括66.8%（129例）GS≥7的患者。PSA密度（PSAD）和PSA检测PCa的曲线下面积（AUC）分别为0.722和0.585，与其他血液参数相比，P = 0.001。与PSA、游离/总PSA、NLR、PLR和PsNPLR（PSA与NLR和PLR联合）相比，PSA与PLR联合（PsPLR）和PSA与NLR联合（PsNLR）诊断GS≥7人群的AUC分别为0.608和0.610，P < 0.05。多因素分析中，NLR和PLR不能预测PCa。对于GS≥7的癌症检测，多因素分析中，构建了分别包含PSA和NLR（模型1：PsNLR +基线参数）或PSA和PLR（模型2：PsPLR +基线参数）的模型。基线参数包括年龄、直肠指检阳性病变、PSA密度、游离/总PSA和磁共振成像。包含PsPLR的模型2在发现侵袭性PCa方面具有统计学意义（比值比：2.862，95%置信区间：1.174 - 6.975，P = 0.021）。模型2的预测准确性（AUC：0.734，P < 0.001）高于仅使用基线参数时（AUC：0.693，P < 0.001）。
NLR或PLR单独或与PSA联合均不能检测出PCa。然而，在我们这项受样本量限制的回顾性研究中，PSA与PLR联合使用能够发现临床显著性和非显著性PCa之间的差异。
