检测前列腺癌及临床显著性前列腺癌不同模型的构建与比较
Construction and Comparison of Different Models in Detecting Prostate Cancer and Clinically Significant Prostate Cancer.
作者信息
Zhou Yongheng, Qi Wenqiang, Cui Jianfeng, Zhong Minglei, Lv Guangda, Qu Sifeng, Chen Shouzhen, Li Rongyang, Shi Benkang, Zhu Yaofeng
机构信息
Department of Urology, Qilu Hospital of Shandong University, Jinan, China.
Department of Thoracic Surgery, Qilu Hospital of Shandong University, Jinan, China.
出版信息
Front Oncol. 2022 Jul 12;12:911725. doi: 10.3389/fonc.2022.911725. eCollection 2022.
BACKGROUND
With the widespread adoption of prostatic-specific antigen (PSA) screening, the detection rates of prostate cancer (PCa) have increased. Due to the low specificity and high false-positive rate of serum PSA levels, it was difficult to diagnose PCa accurately. To improve the diagnosis of PCa and clinically significant prostate cancer (CSPCa), we established novel models on the basis of the prostate health index (PHI) and multiparametric magnetic resonance imaging (mpMRI) in the Asian population.
METHODS
We retrospectively collected the clinical indicators of patients with TPSA at 4-20 ng/ml. Furthermore, mpMRI was performed using a 3.0-T scanner and reported in the Prostate Imaging Reporting and Data System version 2.1 (PI-RADS). Univariable and multivariable logistic analyses were performed to construct the models. The performance of different models based on PSA derivatives, PHI derivatives, PI-RADS, and a combination of PHI derivatives and PI-RADS was evaluated.
RESULTS
Among the 128 patients, 47 (36.72%) patients were diagnosed with CSPCa and 81 (63.28%) patients were diagnosed with non-CSPCa. Of the 81 (63.28%) patients, 8 (6.25%) patients were diagnosed with Gleason Grade 1 PCa and 73 (57.03%) patients were diagnosed with non-PCa. In the analysis of the receiver operator characteristic (ROC) curves in TPSA 4-20 ng/ml, the multivariable model for PCa was significantly larger than that for the model based on the PI-RADS ( = 0.004) and that for the model based on the PHI derivatives ( = 0.031) in diagnostic accuracy. The multivariable model for CSPCa was significantly larger than that for the model based on the PI-RADS ( = 0.003) and was non-significantly larger than that for the model based on the PHI derivatives ( = 0.061) in diagnostic accuracy. For PCa in TPSA 4-20 ng/ml, a multivariable model achieved the optimal diagnostic value at four levels of predictive variables. For CSPCa in TPSA 4-20 ng/ml, the multivariable model achieved the optimal diagnostic value at a sensitivity close to 90% and 80%.
CONCLUSIONS
The models combining PHI derivatives and PI-RADS performed better in detecting PCa and CSPCa than the models based on either PHI or PI-RADS.
背景
随着前列腺特异性抗原（PSA）筛查的广泛应用，前列腺癌（PCa）的检出率有所增加。由于血清PSA水平的特异性低且假阳性率高，准确诊断PCa存在困难。为了改善PCa和临床显著性前列腺癌（CSPCa）的诊断，我们在亚洲人群中基于前列腺健康指数（PHI）和多参数磁共振成像（mpMRI）建立了新模型。
方法
我们回顾性收集了总前列腺特异抗原（TPSA）在4 - 20 ng/ml的患者的临床指标。此外，使用3.0-T扫描仪进行mpMRI检查，并按照前列腺影像报告和数据系统第2.1版（PI-RADS）进行报告。进行单变量和多变量逻辑分析以构建模型。评估了基于PSA衍生物、PHI衍生物、PI-RADS以及PHI衍生物与PI-RADS组合的不同模型的性能。
结果
在128例患者中，47例（36.72%）被诊断为CSPCa，81例（63.28%）被诊断为非CSPCa。在这81例（63.28%）患者中，8例（6.25%）被诊断为Gleason 1级PCa，73例（57.03%）被诊断为非PCa。在TPSA 4 - 20 ng/ml的受试者工作特征（ROC）曲线分析中，PCa的多变量模型在诊断准确性方面显著大于基于PI-RADS的模型（P = 0.004）以及基于PHI衍生物的模型（P = 0.031）。CSPCa的多变量模型在诊断准确性方面显著大于基于PI-RADS的模型（P = 0.003），且与基于PHI衍生物的模型相比虽无显著差异但稍大（P = 0.061）。对于TPSA 4 - 20 ng/ml的PCa，多变量模型在四个水平的预测变量时达到了最佳诊断价值。对于TPSA 4 - 20 ng/ml的CSPCa，多变量模型在灵敏度接近90%和80%时达到了最佳诊断价值。
结论
与基于PHI或PI-RADS的模型相比，结合PHI衍生物和PI-RADS的模型在检测PCa和CSPCa方面表现更好。
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