膀胱镜检查
医学
膀胱癌
泌尿科
尿
内科学
赫拉
癌症
尿细胞学
胃肠病学
肿瘤科
泌尿系统
结直肠癌
克拉斯
作者
Kim E.M. van Kessel,Willemien Beukers,Irene Lurkin,Angelique Ziel‐van der Made,Kirstin A. van der Keur,Joost L. Boormans,Lars Dyrskjøt,Mirari Márquez,Torben F. Ørntoft,Francisco X. Real,Ulrika Segersten,Núria Malats,Per‐Uno Malmström,Wim Van Criekinge,Ellen C. Zwarthoff
出处
期刊:The Journal of Urology
[Ovid Technologies (Wolters Kluwer)]
日期:2017-03-01
卷期号:197 (3 Part 1): 590-595
被引量:118
标识
DOI:10.1016/j.juro.2016.09.118
摘要
Only 3% to 28% of patients referred to the urology clinic for hematuria are diagnosed with bladder cancer. Cystoscopy leads to high diagnostic costs and a high patient burden. Therefore, to improve the selection of patients for cystoscopy and reduce costs and over testing we aimed to validate a recently developed diagnostic urine assay.Included in study were 200 patients from a total of 3 European countries who underwent cystoscopy for hematuria, including 97 with bladder cancer and 103 with nonmalignant findings. Voided urine samples were collected prior to cystoscopy. DNA was extracted and analyzed for mutations in FGFR3, TERT and HRAS, and methylation of OTX1, ONECUT2 and TWIST1. Logistic regression was used to analyze the association between predictor variables and bladder cancer.Combining the methylation and mutation markers with age led to an AUC of 0.96 (95% CI 0.92-0.99) with 93% sensitivity and 86% specificity, and an optimism corrected AUC of 0.95. The AUC was higher for T1 or greater tumors compared to Ta tumors (0.99 vs 0.93). The AUC was also higher for high grade tumors compared to low grade tumors (1.00 vs 0.93). Overall negative predictive value was 99% based on the 5% to 10% prevalence of bladder cancer in patients with hematuria. This would lead to a 77% reduction in diagnostic cystoscopy.Analyzing hematuria patients for the risk of bladder cancer using novel molecular markers may lead to a reduction in diagnostic cystoscopy. Combining methylation analysis (OTX1, ONECUT2 and TWIST1) with mutation analysis (FGFR3, TERT and HRAS) and patient age resulted in a validated accurate prediction model.
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