血清前列腺特异性抗原联合Gleason评分对初诊前列腺癌转移风险的预测价值

目的 探讨血清前列腺特异性抗原(PSA)联合活体组织穿刺的Gleason评分对初诊前列腺癌患者转移风险的预测价值.方法 回顾性分析2019年1月至12月经前列腺活体组织穿刺病理学初次确诊、于海军军医大学(第二军医大学)长海医院行以68镓(68Ga)标记的靶向前列腺特异性膜抗原(PSMA)分子探针PSMA-11为示踪剂的PET-CT(68Ga-PSMA-11 PET-CT)检查的85例未经治疗的初诊前列腺癌患者的影像学及临床资料.85例患者中未发生肿瘤转移的有46例(无转移组)、发生肿瘤转移的有39例(转移组).以68Ga-PSMA-11 PET-CT是否发现转移病灶为二分类因变量,以PSAX(Gleason评分-5)为连续自变量,建立转移风险的logistic回归预测模型,并通过ROC曲线分析其诊断效能;用外部验证数据(n=20)对该模型进行检验,评估回归方程预测肿瘤转移的准确性.结果 转移组患者PSA水平、Gleason评分均高于无转移组75.0(17.7～533.9)ng/mL vs 13.8(3.8～62.0)ng/mL,P＜0.01;8(6～10)分vs 8(6～10)分,P=0.042].以PSAX(Gleason评分-5)作为连续自变量时,预测转移风险的 ROC AUC 为0.857(95％CI 0.772～0.942,P＜0.01);PSA×(Gleason 评分-5)最佳界值为 130.62,对应的灵敏度为71.8％,特异度为95.7％,约登指数为0.675.回归方程logit(P)=0.019×PSA×(Gleason评分-5)-2.3对转移风险的预测准确度达81.2％(69/85).外部验证预测的PSA水平(Z=-1.616,P=0.106)和Gleason 评分(Z=-1.391,P=0.164)与构建模型数据相比差异无统计学意义,说明外部验证数据符合检验条件,其检验回归方程的准确度为85.0％(17/20).结论 PSA联合Gleason评分对初诊前列腺癌患者转移风险有较好的预测价值.

Role of serum prostate-specific antigen combined with Gleason score in predicting the metastatic risk in patients with newly diagnosed prostate cancer

Objective To explore the role of prostate-specific antigen (PSA) combined with the biopsy Gleason score (GS) in predicting the metastatic risk in patients with newly diagnosed prostate cancer. Methods The imaging and clinical data of 85 untreated prostate cancer patients, who were newly diagnosed by prostate biopsy and underwent ⁶⁸gallium-labelled prostate-specific membrane antigen (PSMA) ligand PSMA-11 (⁶⁸Ga-PSMA-11) positron emission tomography-computed tomography (PET-CT) in Changhai Hospital of Naval Medical University (Second Military Medical University) from Jan. to Dec. 2019, were analyzed retrospectively. There were 46 cases without metastasis (non-metastasis group) and 39 cases with metastasis (metastasis group). A logistic regression model for predicting the metastatic risk was established using the occurrence of ⁶⁸Ga-PSMA-11 PET-CT positive metastasis as a binary dependent variable and PSA×(GS-5) as a continuous independent variable, and the diagnostic efficacy was analyzed by receiver operating characteristic (ROC) curve; the model was tested with external validation data (n=20) to evaluate the accuracy of regression equation in predicting tumor metastasis. Results The median PSA (75.07.7-533.9]ng/mL vs 13.833.8-62.0]ng/mL, P<0.01) and GS (86-10]vs 86-10], P=0.042) in the metastasis group were significantly higher than those in the non-metastasis group. When PSA×(GS-5) was used as a continuous independent variable, the area under the ROC curve of predictive value was 0.857 (95% confidence intervalCI] 0.772-0.942, P<0.01). The best cut-off value of PSA×(GS-5) was 130.62, with a sensitivity of 71.8%, a specificity of 95.7%, and a Yoden index of 0.675. The regression equation logit (P)=0.019×PSA×(GS-5)-2.3 had an accuracy of 81.2% (69/85) for predicting the metastatic risk. There was no significant difference between the external validation data and the modeling data in the PSA (Z=-1.616, P=0.106) or GS (Z=-1.391, P=0.164). The external validation data met the test conditions, and the accuracy of the test regression equation was 85.0% (17/20). Conclusion PSA combined with GS has good performance in predicting the metastatic risk of patients with newly diagnosed prostate cancer.