Ge Peng, Zheng Yu-Xin, Yan Zi-Rong, Li Liang, Li Wang, Wang Jun-Qi
Department of Urology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu 221002, China.
Department of Urology, The Second Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu 221006, China.
Zhonghua Nan Ke Xue. 2024 Aug;30(8):701-708.
To evaluate the diagnostic value of hematological parameters for PCa with prostate-specific antigen （PSA） of 4－10 μg/L and construct a risk-stratification model with these parameters.
We retrospectively analyzed the data on the males undergoing the initial prostatic biopsy in the Affiliated Hospital of Xuzhou Medical University with PSA of 4－10 μg/L from March 2010 to April 2021. According to the results of biopsy, we classified the patients into a PCa and a non-PCa group, and compared the hematological parameters between the two groups. We performed univariate and multivariate logistic regression analyses, identified the independent risk factors for PCa, constructed a risk-stratification model for the prediction of PCa and evaluated its efficiency.
A total of 415 cases were included in this study, 107 (25.8%) in the PCa and 308 (74.2%) in the non-PCa group. Compared with the non-PCa males, the PCa patients showed a significantly older age, higher ratios of neutrophil to lymphocyte and platelet to lymphocyte, systemic immune-inflammation index (SII), red blood cell distribution width and cystatin C (CysC) level (all P<0.05), but lower red blood cell count and hemoglobin and free/total PSA (f/tPSA) levels (all P<0.05). Multivariate logistic regression analysis indicated that age, f/tPSA, SII and CysC were independent risk factors for the prediction of PCa (all P<0.05). Five prediction models were constructed based on the above risk factors, and the area under the ROC curve (AUC) of the four-parameter (age+f/tPSA+SII+CysC) model was 0.745 (95% CI: 0.694－0.796), significantly higher than those of the other models (P<0.05). A risk-stratification model (low-, intermediate-, and high-risk) was also constructed based on the total nomogram scores, which showed a comparable performance to that of the Prostate Imaging Reporting and Data System (PI-RADS) for the prediction of PCa (AUC: 0.727 ［95% CI: 0.650－0.804］ vs 0.734 ［95% CI: 0.658－0.811］). However, the prediction rate by the risk-stratification model was evidently higher in the low-risk males than in those with low PI-RADS scores (1－2) (39.4% vs 22.2%).
SII and CysC are independent risk factors for the prediction of PCa in patients with gray-zone PSA levels. The risk-stratification model based on age, SII, CysC and f/tPSA is comparable to PI-RADS in the diagnostic efficiency of PCa, with an even higher prediction rate in low-risk patients than in those with low PI-RADS scores, and contributive to precision screening and reduction of excessive biopsies in the diagnosis of PCa with gray-zone PSA.
评估血液学参数对前列腺特异性抗原（PSA）为4－10 μg/L的前列腺癌（PCa）的诊断价值，并构建基于这些参数的风险分层模型。
回顾性分析2010年3月至2021年4月在徐州医科大学附属医院接受初次前列腺穿刺活检、PSA为4－10 μg/L的男性患者数据。根据活检结果，将患者分为PCa组和非PCa组，比较两组的血液学参数。进行单因素和多因素logistic回归分析，确定PCa的独立危险因素，构建预测PCa的风险分层模型并评估其效能。
本研究共纳入415例患者，PCa组107例（25.8%），非PCa组308例（74.2%）。与非PCa男性相比，PCa患者年龄显著更大，中性粒细胞与淋巴细胞比值、血小板与淋巴细胞比值、全身免疫炎症指数（SII）、红细胞分布宽度和胱抑素C（CysC）水平更高（均P<0.05），但红细胞计数、血红蛋白和游离/总PSA（f/tPSA）水平更低（均P<0.05）。多因素logistic回归分析表明，年龄、f/tPSA、SII和CysC是预测PCa的独立危险因素（均P<0.05）。基于上述危险因素构建了5个预测模型，四参数（年龄+f/tPSA+SII+CysC）模型的ROC曲线下面积（AUC）为0.745（95%CI：0.694－0.796），显著高于其他模型（P<0.05）。还基于总列线图评分构建了一个风险分层模型（低、中、高风险），其在预测PCa方面的表现与前列腺影像报告和数据系统（PI-RADS）相当（AUC：0.727［95%CI：0.650－0.804］vs 0.734［95%CI：0.658－0.811］）。然而，风险分层模型在低风险男性中的预测率明显高于PI-RADS评分低（1－2分）的男性（39.4%对22.2%）。
SII和CysC是预测灰区PSA水平患者PCa的独立危险因素。基于年龄、SII、CysC和f/tPSA的风险分层模型在PCa诊断效能上与PI-RADS相当，在低风险患者中的预测率甚至高于PI-RADS评分低的患者，有助于PCa灰区PSA诊断中的精准筛查和减少过度活检。
