期刊
EUROPEAN UROLOGY FOCUS
卷 7, 期 3, 页码 546-553出版社
ELSEVIER
DOI: 10.1016/j.euf.2020.05.002
关键词
Biomarker; Magnetic resonance imaging; Prostate cancer; Risk prediction model
资金
- Swiss Cancer League [BIL KLS-4558-08-2018]
- Swedish Cancer Society (Cancerfonden)
- Swedish Research Council (Vetenskapsradet)
- Swedish Research Council for Health, Working Life and Welfare (FORTE)
- Strategic Research Programme on Cancer (StratCan)
- Karolinska Institutet
- Swedish e-Science Research Center (SeRC)
- Swedish Research Council
New generation risk calculators (RCs) incorporating imaging can better discriminate clinically significant prostate cancer, but their predicted probabilities vary in accuracy. The Stockholm3 test achieves performance comparable with the best MRI RC without utilization of imaging.
Background: A new generation of risk calculators (RCs) for prostate cancer (PCa) incorporating magnetic resonance imaging (MRI) data have been introduced. However, these have not been validated externally, and their clinical benefit compared with alternative approaches remains unclear. Objective: To assess previously published PCa RCs incorporating MRI data, and compare their performance with traditional RCs (European Randomized Study of Screening for Prostate Cancer [ERSPC] 3/4 and Prostate Biopsy Collaborative Group [PBCG]) and the blood-based Stockholm3 test. Design, setting, and participants: RCs were tested in a prospective multicenter cohort including 532 men aged 45-74 yr participating in the Stockholm3-MRI study between 2016 and 2017. Outcome measurements and statistical analysis: The probabilities of detection of clini-cally significant PCa (csPCa) defined as Gleason score >3 + 4 were calculated for each patient. For each RC and the Stockholm3 test, discrimination was assessed by area under the curve (AUC), calibration by numerical and graphical summaries, and clinical useful-ness by decision curve analysis (DCA). Results and limitations: The discriminative ability of MRI RCs 1-4 for the detection of csPCa was superior (AUC 0.81-0.87) to the traditional RCs (AUC 0.76-0.80). The observed prevalence of csPCa in the cohort was 37%, but calibration-in-the-large predictions varied from 14% to 63% across models. DCA identified only one model including MRI data as clinically useful at a threshold probability of 10%. The Stockholm3 test achieved equivalent performance for discrimination (AUC 0.86) and DCA, but was underpredicting the actual risk. Conclusions: Although MRI RCs discriminated csPCa better than traditional RCs, their predicted probabilities were variable in accuracy, and DCA identified only one model as clinically useful. Patient summary: Novel risk calculators (RCs) incorporating imaging improved the ability to discriminate clinically significant prostate cancer compared with traditional tools. However, all but one predicted divergent compared with actual risks, suggesting that regional modifications be implemented before usage. The Stockholm3 test achieved performance comparable with the best MRI RC without utilization of imaging. (c) 2020 European Association of Urology. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据