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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...
Language English Country Germany
Document type Journal Article, Validation Study, Comparative Study, Multicenter Study
NLK
ProQuest Central
from 1997-02-01 to 1 year ago
Medline Complete (EBSCOhost)
from 2000-02-01 to 1 year ago
Health & Medicine (ProQuest)
from 1997-02-01 to 1 year ago
- MeSH
- Risk Assessment methods MeSH
- Middle Aged MeSH
- Humans MeSH
- Magnetic Resonance Imaging * methods MeSH
- Prostatic Neoplasms * pathology diagnostic imaging MeSH
- Predictive Value of Tests MeSH
- Prostate * pathology diagnostic imaging MeSH
- Aged MeSH
- Image-Guided Biopsy methods MeSH
- Check Tag
- Middle Aged MeSH
- Humans MeSH
- Male MeSH
- Aged MeSH
- Publication type
- Journal Article MeSH
- Multicenter Study MeSH
- Comparative Study MeSH
- Validation Study 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
References provided by Crossref.org
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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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