Literature DB >> 30082150

Prediction of High-grade Prostate Cancer Following Multiparametric Magnetic Resonance Imaging: Improving the Rotterdam European Randomized Study of Screening for Prostate Cancer Risk Calculators.

Arnout R Alberts1, Monique J Roobol2, Jan F M Verbeek2, Ivo G Schoots3, Peter K Chiu2, Daniël F Osses4, Jasper D Tijsterman5, Harrie P Beerlage6, Christophe K Mannaerts7, Lars Schimmöller8, Peter Albers9, Christian Arsov9.   

Abstract

BACKGROUND: The Rotterdam European Randomized Study of Screening for Prostate Cancer risk calculators (ERSPC-RCs) help to avoid unnecessary transrectal ultrasound-guided systematic biopsies (TRUS-Bx). Multivariable risk stratification could also avoid unnecessary biopsies following multiparametric magnetic resonance imaging (mpMRI).
OBJECTIVE: To construct MRI-ERSPC-RCs for the prediction of any- and high-grade (Gleason score ≥3 + 4) prostate cancer (PCa) in 12-core TRUS-Bx±MRI-targeted biopsy (MRI-TBx) by adding Prostate Imaging Reporting and Data System (PI-RADS) and age as parameters to the ERSPC-RC3 (biopsy-naïve men) and ERSPC-RC4 (previously biopsied men). DESIGN, SETTING, AND PARTICIPANTS: A total of 961 men received mpMRI and 12-core TRUS-Bx±MRI-TBx (in case of PI-RADS ≥3) in five institutions. Data of 504 biopsy-naïve and 457 previously biopsied men were used to adjust the ERSPC-RC3 and ERSPC-RC4. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Logistic regression models were constructed. The areas under the curve (AUCs) of the original ERSPC-RCs and MRI-ERSPC-RCs (including PI-RADS and age) for any- and high-grade PCa were compared. Decision curve analysis was performed to assess the clinical utility of the MRI-ERSPC-RCs. RESULTS AND LIMITATIONS: MRI-ERSPC-RC3 had a significantly higher AUC for high-grade PCa compared with the ERSPC-RC3: 0.84 (95% confidence interval [CI] 0.81-0.88) versus 0.76 (95% CI 0.71-0.80, p<0.01). Similarly, MRI-ERSPC-RC4 had a higher AUC for high-grade PCa compared with the ERSPC-RC4: 0.85 (95% CI 0.81-0.89) versus 0.74 (95% CI 0.69-0.79, p<0.01). Unlike for the MRI-ERSPC-RC3, decision curve analysis showed clear net benefit of the MRI-ERSPC-RC4 at a high-grade PCa risk threshold of ≥5%. Using a ≥10% high-grade PCa risk threshold to biopsy for the MRI-ERSPC-RC4, 36% biopsies are saved, missing low- and high-grade PCa, respectively, in 15% and 4% of men who are not biopsied.
CONCLUSIONS: We adjusted the ERSPC-RCs for the prediction of any- and high-grade PCa in 12-core TRUS-Bx±MRI-TBx. Although the ability of the MRI-ERSPC-RC3 for biopsy-naïve men to avoid biopsies remains questionable, application of the MRI-ERSPC-RC4 in previously biopsied men in our cohort would have avoided 36% of biopsies, missing high-grade PCa in 4% of men who would not have received a biopsy. PATIENT
SUMMARY: We have constructed magnetic resonance imaging-based Rotterdam European Randomized study of Screening for Prostate Cancer (MRI-ERSPC) risk calculators for prostate cancer prediction in transrectal ultrasound-guided biopsy and MRI-targeted biopsy by incorporating age and Prostate Imaging Reporting and Data System score into the original ERSPC risk calculators. The MRI-ERSPC risk calculator for previously biopsied men could be used to avoid one-third of biopsies following MRI.
Copyright © 2018 European Association of Urology. Published by Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Biopsy; Magnetic resonance imaging; Multivariable risk stratification; Prostate cancer; Risk calculator

Mesh:

Substances:

Year:  2018        PMID: 30082150     DOI: 10.1016/j.eururo.2018.07.031

Source DB:  PubMed          Journal:  Eur Urol        ISSN: 0302-2838            Impact factor:   20.096


  45 in total

Review 1.  Role of pre-biopsy multiparametric MRI in prostate cancer diagnosis: Evidence from the literature.

Authors:  David Ka-Wai Leung; Peter Ka-Fung Chiu; Chi-Fai Ng; Jeremy Yuen-Chun Teoh
Journal:  Turk J Urol       Date:  2020-10-01

Review 2.  PI-RADS Steering Committee: The PI-RADS Multiparametric MRI and MRI-directed Biopsy Pathway.

Authors:  Anwar R Padhani; Jelle Barentsz; Geert Villeirs; Andrew B Rosenkrantz; Daniel J Margolis; Baris Turkbey; Harriet C Thoeny; François Cornud; Masoom A Haider; Katarzyna J Macura; Clare M Tempany; Sadhna Verma; Jeffrey C Weinreb
Journal:  Radiology       Date:  2019-06-11       Impact factor: 11.105

3.  Prostate MRI, with or without MRI-targeted biopsy, and systematic biopsy for detecting prostate cancer.

Authors:  Frank-Jan H Drost; Daniël F Osses; Daan Nieboer; Ewout W Steyerberg; Chris H Bangma; Monique J Roobol; Ivo G Schoots
Journal:  Cochrane Database Syst Rev       Date:  2019-04-25

4.  MRI for clinically suspected prostate cancer-the disparity between private and public sectors.

Authors:  Lee Chien Yap; Thomas Hugh Lynch; Rustom P Manecksha
Journal:  Ir J Med Sci       Date:  2019-10-22       Impact factor: 1.568

Review 5.  [Intelligent early prostate cancer detection in 2021: more benefit than harm].

Authors:  N Westhoff; J von Hardenberg; M-S Michel
Journal:  Urologe A       Date:  2021-04-21       Impact factor: 0.639

Review 6.  Serum PSA-based early detection of prostate cancer in Europe and globally: past, present and future.

Authors:  Hendrik Van Poppel; Tit Albreht; Partha Basu; Renée Hogenhout; Sarah Collen; Monique Roobol
Journal:  Nat Rev Urol       Date:  2022-08-16       Impact factor: 16.430

7.  How to implement magnetic resonance imaging before prostate biopsy in clinical practice: nomograms for saving biopsies.

Authors:  Ángel Borque-Fernando; Luis Mariano Esteban; Ana Celma; Sarai Roche; Jacques Planas; Lucas Regis; Inés de Torres; Maria Eugenia Semidey; Enrique Trilla; Juan Morote
Journal:  World J Urol       Date:  2019-09-10       Impact factor: 4.226

8.  Multiparametric MRI Versus SelectMDx Accuracy in the Diagnosis of Clinically Significant PCa in Men Enrolled in Active Surveillance.

Authors:  Pietro Pepe; Giuseppe Dibenedetto; Ludovica Pepe; Michele Pennisi
Journal:  In Vivo       Date:  2020 Jan-Feb       Impact factor: 2.155

9.  A predictive model based on biparametric magnetic resonance imaging and clinical parameters for improved risk assessment and selection of biopsy-naïve men for prostate biopsies.

Authors:  Lars Boesen; Frederik B Thomsen; Nis Nørgaard; Vibeke Løgager; Ingegerd Balslev; Rasmus Bisbjerg; Henrik S Thomsen; Henrik Jakobsen
Journal:  Prostate Cancer Prostatic Dis       Date:  2019-04-15       Impact factor: 5.554

10.  Development and head-to-head comparison of machine-learning models to identify patients requiring prostate biopsy.

Authors:  Shuanbao Yu; Jin Tao; Biao Dong; Yafeng Fan; Haopeng Du; Haotian Deng; Jinshan Cui; Guodong Hong; Xuepei Zhang
Journal:  BMC Urol       Date:  2021-05-16       Impact factor: 2.264

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