Head-to-head Comparison of Conventional, and Image- and Biomarker-based Prostate Cancer Risk Calculators

被引:21
作者
Mortezavi, Ashkan [1 ,2 ]
Palsdottir, Thorgerdur [1 ]
Eklund, Martin [1 ]
Chellappa, Venkatesh [1 ]
Murugan, Sarath Kumar [1 ]
Saba, Karim [3 ]
Ankerst, Donna P. [4 ]
Haug, Erik S. [5 ,6 ]
Nordstrom, Tobias
Tilki, Derya [1 ,7 ]
机构
[1] Karolinska Inst, Dept Med Epidemiol & Biostat, Nobels Vag 12A, S-17177 Stockholm, Sweden
[2] Univ Hosp Zurich, Dept Urol, Zurich, Switzerland
[3] Cantonal Hosp Grisons, Dept Urol, Chur, Switzerland
[4] Tech Univ Munich, Dept Math & Life Sci, Munich, Germany
[5] Vestfold Hosp Trust, Sect Urol, Tonsberg, Norway
[6] Oslo Univ Hosp, Inst Canc Genom & Informat, Oslo, Norway
[7] Danderyd Hosp, Dept Clin Sci, Stockholm, Sweden
基金
瑞典研究理事会;
关键词
Biomarker; Magnetic resonance imaging; Prostate cancer; Risk prediction model; ANTIGEN; STHLM3; MEN;
D O I
10.1016/j.euf.2020.05.002
中图分类号
R5 [内科学]; R69 [泌尿科学(泌尿生殖系疾病)];
学科分类号
1002 ; 100201 ;
摘要
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/).
引用
收藏
页码:546 / 553
页数:8
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