Department of Pathology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands.
Department of Urology, Haga Hospital, The Hague, The Netherlands.
J Urol. 2020 Jul;204(1):50-57. doi: 10.1097/JU.0000000000000786. Epub 2020 Jan 27.
Current clinical guidelines recommend cystoscopy in patients who present with hematuria to rule out a bladder tumor. We evaluated whether our previously developed urine assay was able to triage patients with hematuria for cystoscopy in a large prospective cohort.
A urine sample was collected before cystoscopy and mutation/methylation status of 6 genes was determined on cellular DNA. The existing diagnostic model was validated on this cohort. Logistic regression was applied to investigate other potential variables. The primary end point was the model performance as indicated by the AUC. Secondary end points were sensitivity, specificity and negative predictive value. Clinical usefulness was determined by the net benefit approach.
In 838 patients biomarker status could be determined for all genes. Urothelial cancer was observed in 112 patients (98 of 457 in the gross and 14 of 381 in the microscopic hematuria group). Validation of the existing model resulted in an AUC of 0.93. Logistic regression analysis identified type of hematuria as a significant additional variable. Adding type of hematuria resulted in an AUC of 0.95 (96% sensitivity, 73% specificity, 99% negative predictive value). The assay identified all upper tract tumors not visible by cystoscopy (in 6). Net benefit analysis showed that the urine assay should be preferred over current clinical practice. Implementing the urine assay as a triage tool could lead to a 53% reduction in cystoscopies.
The urine assay detected urothelial cancer with a very high accuracy and can be used to triage patients presenting with hematuria for cystoscopy.
目前的临床指南建议血尿患者行膀胱镜检查以排除膀胱肿瘤。我们评估了我们之前开发的尿液检测方法是否能够在大型前瞻性队列中对血尿患者进行膀胱镜检查的分诊。
在膀胱镜检查前收集尿液样本，并在细胞 DNA 上测定 6 个基因的突变/甲基化状态。该队列验证了现有的诊断模型。应用逻辑回归分析其他潜在变量。主要终点是 AUC 所示的模型性能。次要终点是敏感性、特异性和阴性预测值。通过净收益方法确定临床实用性。
在 838 名患者中，所有基因的生物标志物状态均可确定。在 112 名患者中观察到尿路上皮癌（457 例肉眼血尿患者中有 98 例，381 例镜下血尿患者中有 14 例）。验证现有的模型得出 AUC 为 0.93。逻辑回归分析确定血尿类型是一个重要的附加变量。添加血尿类型后，AUC 为 0.95（96%的敏感性，73%的特异性，99%的阴性预测值）。该检测方法可识别所有肉眼不可见的上尿路肿瘤（6 例）。净收益分析表明，尿液检测优于当前的临床实践。将尿液检测作为一种分诊工具，可以将膀胱镜检查减少 53%。
该尿液检测方法对尿路上皮癌的检测具有很高的准确性，可以用于对血尿患者进行膀胱镜检查的分诊。
