External validation and comparison of magnetic resonance imaging-based risk prediction models for prostate biopsy stratification.
External validation
MRI
Prostate biopsy
Risk prediction
Journal
World journal of urology
ISSN: 1433-8726
Titre abrégé: World J Urol
Pays: Germany
ID NLM: 8307716
Informations de publication
Date de publication:
12 Jun 2024
12 Jun 2024
Historique:
received:
04
04
2024
accepted:
15
05
2024
medline:
13
6
2024
pubmed:
13
6
2024
entrez:
12
6
2024
Statut:
epublish
Résumé
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). 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. 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. 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.
Identifiants
pubmed: 38866949
doi: 10.1007/s00345-024-05068-0
pii: 10.1007/s00345-024-05068-0
doi:
Types de publication
Journal Article
Validation Study
Comparative Study
Multicenter Study
Langues
eng
Sous-ensembles de citation
IM
Pagination
372Informations de copyright
© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
Références
International Agency for Research on Cancer. Data visualization tools for exploring the global cancer burden in 2020. 2022.
Thompson IM, Pauler DK, Goodman PJ, Tangen CM, Lucia MS, Parnes HL et al (2004) Prevalence of prostate cancer among men with a prostate-specific antigen level ≤4.0 ng per milliliter. N Engl J Med 350:2239–46. https://doi.org/10.1056/NEJMoa031918
doi: 10.1056/NEJMoa031918
pubmed: 15163773
Turkbey B, Rosenkrantz AB, Haider MA, Padhani AR, Villeirs G, Macura KJ et al (2019) Prostate imaging reporting and data system version 2.1: 2019 update of prostate imaging reporting and data system version 2. Eur Urol 76:340–51. https://doi.org/10.1016/j.eururo.2019.02.033
doi: 10.1016/j.eururo.2019.02.033
pubmed: 30898406
Drost F-JH, Osses DF, Nieboer D, Steyerberg EW, Bangma CH, Roobol MJ et al (2019) Prostate MRI, with or without MRI-targeted biopsy, and systematic biopsy for detecting prostate cancer. Cochrane Database Syst Rev. https://doi.org/10.1002/14651858.CD012663.pub2
doi: 10.1002/14651858.CD012663.pub2
pubmed: 31022301
pmcid: 6699659
Loeb S, Vellekoop A, Ahmed HU, Catto J, Emberton M, Nam R et al (2013) Systematic review of complications of prostate biopsy. Eur Urol 64:876–892. https://doi.org/10.1016/j.eururo.2013.05.049
doi: 10.1016/j.eururo.2013.05.049
pubmed: 23787356
Schoots IG, Roobol MJ (2020) Multivariate risk prediction tools including MRI for individualized biopsy decision in prostate cancer diagnosis: current status and future directions. World J Urol 38:517–529. https://doi.org/10.1007/s00345-019-02707-9
doi: 10.1007/s00345-019-02707-9
pubmed: 30868240
Triquell M, Campistol M, Celma A, Regis L, Cuadras M, Planas J et al (2022) Magnetic resonance imaging-based predictive models for clinically significant prostate cancer: a systematic review. Cancers (Basel) 14:4747. https://doi.org/10.3390/cancers14194747
doi: 10.3390/cancers14194747
pubmed: 36230670
Weinreb JC, Barentsz JO, Choyke PL, Cornud F, Haider MA, Macura KJ et al (2016) PI-RADS prostate imaging—reporting and data system: 2015, version 2. Eur Urol 69:16–40. https://doi.org/10.1016/j.eururo.2015.08.052
doi: 10.1016/j.eururo.2015.08.052
pubmed: 26427566
de Rooij M, Israël B, Tummers M, Ahmed HU, Barrett T, Giganti F et al (2020) ESUR/ESUI consensus statements on multi-parametric MRI for the detection of clinically significant prostate cancer: quality requirements for image acquisition, interpretation and radiologists’ training. Eur Radiol 30:5404–5416. https://doi.org/10.1007/s00330-020-06929-z
doi: 10.1007/s00330-020-06929-z
pubmed: 32424596
pmcid: 7476997
Epstein JI, Egevad L, Amin MB, Delahunt B, Srigley JR, Humphrey PA (2015) The 2014 International Society of Urological Pathology (ISUP) Consensus Conference on Gleason Grading of Prostatic Carcinoma. Am J Surg Pathol 40:1. https://doi.org/10.1097/PAS.0000000000000530
doi: 10.1097/PAS.0000000000000530
Wolff RF, Moons KGM, Riley RD, Whiting PF, Westwood M, Collins GS et al (2019) PROBAST: a tool to assess the risk of bias and applicability of prediction model studies. Ann Intern Med 170:51. https://doi.org/10.7326/M18-1376
doi: 10.7326/M18-1376
pubmed: 30596875
Collins GS, Reitsma JB, Altman DG, Moons KGM (2015) Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. Eur Urol 67:1142–1151. https://doi.org/10.1016/j.eururo.2014.11.025
doi: 10.1016/j.eururo.2014.11.025
pubmed: 25572824
Riley RD, Debray TPA, Collins GS, Archer L, Ensor J, van Smeden M et al (2021) Minimum sample size for external validation of a clinical prediction model with a binary outcome. Stat Med 40:4230–4251. https://doi.org/10.1002/sim.9025
doi: 10.1002/sim.9025
pubmed: 34031906
Alberts AR, Roobol MJ, Verbeek JFM, Schoots IG, Chiu PK, Osses DF et al (2019) Prediction of high-grade prostate cancer following multiparametric magnetic resonance imaging: improving the rotterdam european randomized study of screening for prostate cancer risk calculators. Eur Urol 75:310–318. https://doi.org/10.1016/j.eururo.2018.07.031
doi: 10.1016/j.eururo.2018.07.031
pubmed: 30082150
Mottet N, van den Bergh RCN, Briers E, den Broeck T, Cumberbatch MG, De Santis M et al (2021) EAU-EANM-ESTRO-ESUR-SIOG guidelines on prostate cancer—2020 update. Part 1: screening, diagnosis, and local treatment with curative intent. Eur Urol 79:243–62. https://doi.org/10.1016/j.eururo.2020.09.042
doi: 10.1016/j.eururo.2020.09.042
pubmed: 33172724
Bhayana R, O’Shea A, Anderson MA, Bradley WR, Gottumukkala RV, Mojtahed A et al (2021) PI-RADS versions 2 and 2.1: interobserver agreement and diagnostic performance in peripheral and transition zone lesions among six radiologists. AJR Am J Roentgenol 217:141–51. https://doi.org/10.2214/AJR.20.24199
doi: 10.2214/AJR.20.24199
pubmed: 32903060
Yilmaz EC, Lin Y, Belue MJ, Harmon SA, Phelps TE, Merriman KM et al (2023) PI-RADS version 2.0 versus version 2.1: comparison of prostate cancer gleason grade upgrade and downgrade rates from MRI-targeted biopsy to radical prostatectomy. Am J Roentgenol. https://doi.org/10.2214/AJR.23.29964
doi: 10.2214/AJR.23.29964
Touzani A, Fiard G, Barret E, Renard-Penna R, Salin A, Pradère B et al (2022) Clinical trial protocol for perfect: a randomised controlled trial comparing the efficiency and tolerance of transperineal fusion versus transrectal imaging-targeted prostate biopsies (CCAFU-PR1 Study). Eur Urol Open Sci 45:76–80. https://doi.org/10.1016/j.euros.2022.09.007
doi: 10.1016/j.euros.2022.09.007
pubmed: 36217451
pmcid: 9547228
Bryant RJ, Yamamoto H, Eddy B, Kommu S, Narahari K, Omer A et al (2023) Protocol for the TRANSLATE prospective, multicentre, randomised clinical trial of prostate biopsy technique. BJU Int 131:694–704. https://doi.org/10.1111/bju.15978
doi: 10.1111/bju.15978
pubmed: 36695816
Vickers AJ (2011) Prediction models in cancer care. CA Cancer J Clin. https://doi.org/10.3322/caac.20118
doi: 10.3322/caac.20118
pubmed: 21732332
pmcid: 3189416
Wynants L, van Smeden M, McLernon DJ, Timmerman D, Steyerberg EW, Van Calster B (2019) Three myths about risk thresholds for prediction models. BMC Med 17:192. https://doi.org/10.1186/s12916-019-1425-3
doi: 10.1186/s12916-019-1425-3
pubmed: 31651317
pmcid: 6814132
Park KJ, Choi SH, Lee JS, Kim JK, Kim M (2020) Interreader agreement with prostate imaging reporting and data system version 2 for prostate cancer detection: a systematic review and meta-analysis. J Urol 204:661–670. https://doi.org/10.1097/JU.0000000000001200
doi: 10.1097/JU.0000000000001200
pubmed: 32552474
Cornud F, Roumiguié M, Barry de Longchamps N, Ploussard G, Bruguière E, Portalez D et al (2018) Precision matters in MR imaging–targeted prostate biopsies: evidence from a prospective study of cognitive and elastic fusion registration transrectal biopsies. Radiology. 287:534–42. https://doi.org/10.1148/radiol.2017162916
doi: 10.1148/radiol.2017162916
pubmed: 29361246
Martin R, Belahsen Y, Noujeim J-P, Lefebvre Y, Lemort M, Deforche M et al (2023) Optimizing multiparametric magnetic resonance imaging-targeted biopsy and detection of clinically significant prostate cancer: the role of core number and location. World J Urol. https://doi.org/10.1007/s00345-023-04386-z
doi: 10.1007/s00345-023-04386-z
pubmed: 37924333