Yu Shuanbao, Hong Guodong, Tao Jin, Shen Yan, Liu Junxiao, Dong Biao, Fan Yafeng, Li Ziyao, Zhu Ali, Zhang Xuepei
Department of Urology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Department of Nosocomial Infection Management, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Front Oncol. 2020 Nov 11;10:575261. doi: 10.3389/fonc.2020.575261. eCollection 2020.
We sought to develop diagnostic models incorporating mpMRI examination to identify PCa (Gleason score≥3+3) and CSPCa (Gleason score≥3+4) to reduce overdiagnosis and overtreatment.
We retrospectively identified 784 patients according to inclusion criteria between 2016 and 2020. The cohort was split into a training cohort of 548 (70%) patients and a validation cohort of 236 (30%) patients. Age, PSA derivatives, prostate volume, and mpMRI parameters were assessed as predictors for PCa and CSPCa. The multivariable models based on clinical parameters were evaluated using area under the curve (AUC), calibration plots, and decision curve analysis (DCA).
Univariate analysis showed that age, tPSA, PSAD, prostate volume, MRI-PCa, MRI-seminal vesicle invasion, and MRI-lymph node invasion were significant predictors for both PCa and CSPCa (each ≤0.001). PSAD has the highest diagnostic accuracy in predicting PCa (AUC=0.79) and CSPCa (AUC=0.79). The multivariable models for PCa (AUC=0.92, 95% CI: 0.88-0.96) and CSPCa (AUC=0.95, 95% CI: 0.92-0.97) were significantly higher than the combination of derivatives for PSA (=0.041 and 0.009 for PCa and CSPCa, respectively) or mpMRI (each <0.001) in diagnostic accuracy. And the multivariable models for PCa and CSPCa illustrated better calibration and substantial improvement in DCA at threshold above 10%, compared with PSA or mpMRI derivatives. The PCa model with a 30% cutoff or CSPCa model with a 20% cutoff could spare the number of biopsies by 53%, and avoid the number of benign biopsies over 80%, while keeping a 95% sensitivity for detecting CSPCa.
Our multivariable models could reduce unnecessary biopsy without comprising the ability to diagnose CSPCa. Further prospective validation is required.
我们试图开发结合多参数磁共振成像（mpMRI）检查的诊断模型，以识别前列腺癌（Gleason评分≥3+3）和临床显著前列腺癌（CSPCa，Gleason评分≥3+4），从而减少过度诊断和过度治疗。
我们根据纳入标准回顾性地确定了2016年至2020年间的784例患者。该队列被分为一个由548例（70%）患者组成的训练队列和一个由236例（30%）患者组成的验证队列。评估年龄、前列腺特异性抗原（PSA）衍生物、前列腺体积和mpMRI参数作为前列腺癌和CSPCa的预测指标。基于临床参数的多变量模型使用曲线下面积（AUC）、校准图和决策曲线分析（DCA）进行评估。
单变量分析显示，年龄、总PSA（tPSA）、PSA密度（PSAD）、前列腺体积、MRI-前列腺癌、MRI-精囊侵犯和MRI-淋巴结侵犯是前列腺癌和CSPCa的显著预测指标（均≤0.001）。PSAD在预测前列腺癌（AUC=0.79）和CSPCa（AUC=0.79）方面具有最高的诊断准确性。前列腺癌（AUC=0.92，95%可信区间：0.88-0.96）和CSPCa（AUC=0.95，95%可信区间：0.92-0.97）的多变量模型在诊断准确性上显著高于PSA衍生物组合（前列腺癌和CSPCa分别为0.041和0.009）或mpMRI（均<0.001）。与PSA或mpMRI衍生物相比，前列腺癌和CSPCa的多变量模型在阈值高于10%时显示出更好的校准和DCA的实质性改善。前列腺癌模型以30%的截断值或CSPCa模型以20%的截断值可减少53%的活检数量，并避免超过80%的良性活检数量，同时保持检测CSPCa的95%敏感性。
我们的多变量模型可以减少不必要的活检，同时不影响诊断CSPCa的能力。需要进一步的前瞻性验证。
