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External validation and comparison of magnetic resonance imaging-based risk prediction models for prostate biopsy stratification
R. Diamand, K. Guenzel, T. Jabbour, A. Baudewyns, HA. Bourgeno, Y. Lefebvre, M. Ferriero, G. Simone, A. Fourcade, G. Fournier, AP. Bui, F. Taha, M. Oderda, P. Gontero, K. Rysankova, A. Bernal-Gomez, A. Mastrorosa, JB. Roche, G. Fiard, R. Abou...
Jazyk angličtina Země Německo
Typ dokumentu časopisecké články, validační studie, srovnávací studie, multicentrická studie
- MeSH
- hodnocení rizik metody MeSH
- lidé středního věku MeSH
- lidé MeSH
- magnetická rezonanční tomografie * metody MeSH
- nádory prostaty * patologie diagnostické zobrazování MeSH
- prediktivní hodnota testů MeSH
- prostata * patologie diagnostické zobrazování MeSH
- senioři MeSH
- ultrazvukem navigovaná biopsie metody MeSH
- Check Tag
- lidé středního věku MeSH
- lidé MeSH
- mužské pohlaví MeSH
- senioři MeSH
- Publikační typ
- časopisecké články MeSH
- multicentrická studie MeSH
- srovnávací studie MeSH
- validační studie MeSH
PURPOSE: Magnetic resonance imaging (MRI) is a promising tool for risk assessment, potentially reducing the burden of unnecessary prostate biopsies. Risk prediction models that incorporate MRI data have gained attention, but their external validation and comparison are essential for guiding clinical practice. The aim is to externally validate and compare risk prediction models for the diagnosis of clinically significant prostate cancer (csPCa). METHODS: A cohort of 4606 patients across fifteen European tertiary referral centers were identified from a prospective maintained database between January 2016 and April 2023. Transrectal or transperineal image-fusion MRI-targeted and systematic biopsies for PI-RADS score of ≥ 3 or ≥ 2 depending on patient characteristics and physician preferences. Probabilities for csPCa, defined as International Society of Urological Pathology (ISUP) grade ≥ 2, were calculated for each patients using eight models. Performance was characterized by area under the receiver operating characteristic curve (AUC), calibration, and net benefit. Subgroup analyses were performed across various clinically relevant subgroups. RESULTS: Overall, csPCa was detected in 2154 (47%) patients. The models exhibited satisfactory performance, demonstrating good discrimination (AUC ranging from 0.75 to 0.78, p < 0.001), adequate calibration, and high net benefit. The model described by Alberts showed the highest clinical utility for threshold probabilities between 10 and 20%. Subgroup analyses highlighted variations in models' performance, particularly when stratified according to PSA level, biopsy technique and PI-RADS version. CONCLUSIONS: We report a comprehensive external validation of risk prediction models for csPCa diagnosis in patients who underwent MRI-targeted and systematic biopsies. The model by Alberts demonstrated superior clinical utility and should be favored when determining the need for a prostate biopsy.
Departement of Urology Hôpital Cochin Paris France
Department of Urology Centre Hospitalier Universitaire de Reims Reims France
Department of Urology Città Della Salute E Della Scienza Di Torino University of Turin Turin Italy
Department of Urology Clinique Saint Augustin Bordeaux France
Department of Urology Cliniques de L'Europe Saint Elisabeth Brussels Belgium
Department of Urology Hôpital Cavale Blanche CHRU Brest Brest France
Department of Urology Hôpital Européen Georges Pompidou Université de Paris Paris France
Department of Urology Hôpitaux Universitaires de Genève Geneva Switzerland
Department of Urology IRCCS Regina Elena National Cancer Institute Rome Italy
Department of Urology La Croix du Sud Hospital Quint Fonsegrives France
Department of Urology Private Medical Center Klinika Wisniowa Zielona Góra Poland
Department of Urology Vivantes Klinikum Am Urban Berlin Germany
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- $a Diamand, Romain $u Department of Urology, Jules Bordet Institute-Erasme Hospital, Hôpital Universitaire de Bruxelles, Université Libre de Bruxelles, Jules Bordet Institute, HUB, Rue Meylemeersch 90, 1070, Brussels, Belgium. romain.diamand@hubruxelles.be $1 https://orcid.org/0000000220168543
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- $a External validation and comparison of magnetic resonance imaging-based risk prediction models for prostate biopsy stratification / $c R. Diamand, K. Guenzel, T. Jabbour, A. Baudewyns, HA. Bourgeno, Y. Lefebvre, M. Ferriero, G. Simone, A. Fourcade, G. Fournier, AP. Bui, F. Taha, M. Oderda, P. Gontero, K. Rysankova, A. Bernal-Gomez, A. Mastrorosa, JB. Roche, G. Fiard, R. Abou Zahr, G. Ploussard, O. Windisch, Q. Novello, D. Benamran, G. Delavar, J. Anract, N. Barry Delongchamps, A. Halinski, C. Dariane, L. Vlahopoulos, G. Assenmacher, T. Roumeguère, A. Peltier
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- $a PURPOSE: Magnetic resonance imaging (MRI) is a promising tool for risk assessment, potentially reducing the burden of unnecessary prostate biopsies. Risk prediction models that incorporate MRI data have gained attention, but their external validation and comparison are essential for guiding clinical practice. The aim is to externally validate and compare risk prediction models for the diagnosis of clinically significant prostate cancer (csPCa). METHODS: A cohort of 4606 patients across fifteen European tertiary referral centers were identified from a prospective maintained database between January 2016 and April 2023. Transrectal or transperineal image-fusion MRI-targeted and systematic biopsies for PI-RADS score of ≥ 3 or ≥ 2 depending on patient characteristics and physician preferences. Probabilities for csPCa, defined as International Society of Urological Pathology (ISUP) grade ≥ 2, were calculated for each patients using eight models. Performance was characterized by area under the receiver operating characteristic curve (AUC), calibration, and net benefit. Subgroup analyses were performed across various clinically relevant subgroups. RESULTS: Overall, csPCa was detected in 2154 (47%) patients. The models exhibited satisfactory performance, demonstrating good discrimination (AUC ranging from 0.75 to 0.78, p < 0.001), adequate calibration, and high net benefit. The model described by Alberts showed the highest clinical utility for threshold probabilities between 10 and 20%. Subgroup analyses highlighted variations in models' performance, particularly when stratified according to PSA level, biopsy technique and PI-RADS version. CONCLUSIONS: We report a comprehensive external validation of risk prediction models for csPCa diagnosis in patients who underwent MRI-targeted and systematic biopsies. The model by Alberts demonstrated superior clinical utility and should be favored when determining the need for a prostate biopsy.
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