SelectMDx Publications

Clinical, Analytical, Cost Effectiveness Studies

  1. Haese, A, et al. (2019) Multicenter Optimization and Validation of a 2-Gene mRNA Urine Test for Detection of Clinically Significant Prostate Cancer Prior to Initial Prostate Biopsy. J Uro. doi: 10.1097/JU.0000000000000293
  2. Govers TM, et al. (2018) Cost-Effectiveness of Urinary Biomarker Panel in Prostate Cancer Risk Assessment. J Urol. doi: 10.1016/j.juro.2018.07.034
  3. Govers TM, et al.  Cost-effectiveness of SelectMDx for prostate cancer in four European countries: a comparative modeling study. Prostate Cancer and Prostatic Diseases. doi: 10.1038/s41391-018-0076-3
  4. Trooskens G, et al. (2018) Robust performance of a Urinary Molecular Biomarker–Based Risk Score to detect High-grade Prostate Cancer using optimized cascading models. In: Global Congress on Prostate Cancer; 2018 Jun 28-30; Frankfurt, Germany.
  5. Shore N, et al. (2018) SelectMDx Impacts Prostate Biopsy Decision-making in Routine Clinical Practice. Urology Practice. doi: 10.1016/j.urpr.2018.09.002.
  6. Trooskens G, et al. (2018) Assessment of an established TRUS and a urinary biomarker-based risk score as an inclusion criteria for multiparametric MRI to detect clinically significant prostate cancer.  In: Global Congress on Prostate Cancer; 2018 Jun 28-30; Frankfurt, Germany.
  7. Van Neste L, et al.  (2016) Detection of High-grade Prostate Cancer Using a Urinary Molecular Biomarker-Based Risk Score. Eur Urol, Nov; 70(5): 7 40-7 48.
  8. Hendriks RJ, et al. (2017) A urinary biomarker-based risk score correlates with multiparametric MRI for prostate cancer detection. The Prostate, 77(14):1401-1407. 
  9. Hessels D, et al. (2017) Analytical validation of an mRNA-based urine test to predict the presence of high-grade prostate cancer. Translational Medicine Communications, 2:5. doi: 10.1186/ s41231-017-0014-8.
  10. Dijkstra S, et al. (2017) Cost-effectiveness of a new urinary biomarker-based risk score compared to standard of care in prostate cancer diagnostics - a decision analytical model. BJU Int, 120(5):659-665. doi: 10.1111 /bju.13861. 
  11. Alinezhad S, et al. (2016) Validation of Novel Biomarkers for Prostate Cancer Progression by the Combination of Bioinformatics, Clinical and Functional Studies. PLoS ONE, 11 (5): e0155go1. doi: 10.1371/journal.pone.0155901.  
  12. Leyten GH, et al. (2015) Identification of a Candidate Gene Panel for the Early Diagnosis of Prostate Cancer. Clin Cancer Res, 21 (13):3061-70. 
  13. Vinarskaja A, et al. (2011) DNA Methylation and the HOXC6 Paradox in Prostate Cancer. Cancers, 3:3714-3725. doi: 10.3390/cancers3043714.