Refining clinically relevant cut-offs of prostate specific antigen density for risk stratification in patients with PI-RADS 3 lesions.


Journal

Prostate cancer and prostatic diseases
ISSN: 1476-5608
Titre abrégé: Prostate Cancer Prostatic Dis
Pays: England
ID NLM: 9815755

Informations de publication

Date de publication:
24 Jul 2024
Historique:
received: 19 05 2024
accepted: 08 07 2024
revised: 30 06 2024
medline: 26 7 2024
pubmed: 26 7 2024
entrez: 24 7 2024
Statut: aheadofprint

Résumé

Prostate Imaging Reporting and Data System (PI-RADS) 3 lesions, identified through multiparametric magnetic resonance imaging (mpMRI), present a clinical challenge due to their equivocal nature in predicting clinically significant prostate cancer (csPCa). Aim of the study is to improve risk stratification of patients with PI-RADS 3 lesions and candidates for prostate biopsy. A cohort of 4841 consecutive patients who underwent MRI and subsequent MRI-targeted and systematic biopsies between January 2016 and April 2023 were retrospectively identified from independent prospectively maintained database. Only patients who have PI-RADS 3 lesions were included in the final analysis. A multivariable logistic regression analysis was performed to identify covariables associated with csPCa defined as International Society of Urological Pathology (ISUP) grade group ≥2. Performance of the model was evaluated using the area under the receiver operating characteristic curve (AUC), calibration, and net benefit. Significant predictors were then selected for further exploration using a Chi-squared Automatic Interaction Detection (CHAID) analysis. Overall, 790 patients had PI-RADS 3 lesions and 151 (19%) had csPCa. Significant associations were observed for age (OR: 1.1 [1.0-1.1]; p = 0.01) and PSA density (OR: 1643 [2717-41,997]; p < 0.01). The CHAID analysis identified PSAd as the sole significant factor influencing the decision tree. Cut-offs for PSAd were 0.13 ng/ml/cc (csPCa detection rate of 1% vs. 18%) for the two-nodes model and 0.09 ng/ml/cc and 0.16 ng/ml/cc for the three-nodes model (csPCa detection rate of 0.5% vs. 2% vs. 17%). For individuals with PI-RADS 3 lesions on prostate mpMRI and a PSAd below 0.13, especially below 0.09, prostate biopsy can be omitted, in order to avoid unnecessary biopsy and overdiagnosis of non-csPCa.

Sections du résumé

BACKGROUND BACKGROUND
Prostate Imaging Reporting and Data System (PI-RADS) 3 lesions, identified through multiparametric magnetic resonance imaging (mpMRI), present a clinical challenge due to their equivocal nature in predicting clinically significant prostate cancer (csPCa). Aim of the study is to improve risk stratification of patients with PI-RADS 3 lesions and candidates for prostate biopsy.
METHODS METHODS
A cohort of 4841 consecutive patients who underwent MRI and subsequent MRI-targeted and systematic biopsies between January 2016 and April 2023 were retrospectively identified from independent prospectively maintained database. Only patients who have PI-RADS 3 lesions were included in the final analysis. A multivariable logistic regression analysis was performed to identify covariables associated with csPCa defined as International Society of Urological Pathology (ISUP) grade group ≥2. Performance of the model was evaluated using the area under the receiver operating characteristic curve (AUC), calibration, and net benefit. Significant predictors were then selected for further exploration using a Chi-squared Automatic Interaction Detection (CHAID) analysis.
RESULTS RESULTS
Overall, 790 patients had PI-RADS 3 lesions and 151 (19%) had csPCa. Significant associations were observed for age (OR: 1.1 [1.0-1.1]; p = 0.01) and PSA density (OR: 1643 [2717-41,997]; p < 0.01). The CHAID analysis identified PSAd as the sole significant factor influencing the decision tree. Cut-offs for PSAd were 0.13 ng/ml/cc (csPCa detection rate of 1% vs. 18%) for the two-nodes model and 0.09 ng/ml/cc and 0.16 ng/ml/cc for the three-nodes model (csPCa detection rate of 0.5% vs. 2% vs. 17%).
CONCLUSIONS CONCLUSIONS
For individuals with PI-RADS 3 lesions on prostate mpMRI and a PSAd below 0.13, especially below 0.09, prostate biopsy can be omitted, in order to avoid unnecessary biopsy and overdiagnosis of non-csPCa.

Identifiants

pubmed: 39048664
doi: 10.1038/s41391-024-00872-6
pii: 10.1038/s41391-024-00872-6
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

© 2024. The Author(s), under exclusive licence to Springer Nature Limited.

Références

Ahmed HU, El-Shater Bosaily A, Brown LC, Gabe R, Kaplan R, Parmar MK, et al. Diagnostic accuracy of multi-parametric MRI and TRUS biopsy in prostate cancer (PROMIS): a paired validating confirmatory study. Lancet. 2017;389:815–22. https://doi.org/10.1016/S0140-6736(16)32401-1
doi: 10.1016/S0140-6736(16)32401-1 pubmed: 28110982
Zhang Y, Zeng N, Zhang F, Huang Y, Tian Y. How to make clinical decisions to avoid unnecessary prostate screening in biopsy-naïve men with PI-RADs v2 score ≤ 3? Int J Clin Oncol. 2020;25:175–86. https://doi.org/10.1007/s10147-019-01524-9
doi: 10.1007/s10147-019-01524-9 pubmed: 31473884
Wysock JS, Mendhiratta N, Zattoni F, Meng X, Bjurlin M, Huang WC, et al. Predictive value of negative 3T multiparametric magnetic resonance imaging of the prostate on 12-core biopsy results. BJU Int. 2016;118:515–20. https://doi.org/10.1111/bju.13427
doi: 10.1111/bju.13427 pubmed: 26800439
Haffner J, Lemaitre L, Puech P, Haber G-P, Leroy X, Jones JS, et al. Role of magnetic resonance imaging before initial biopsy: comparison of magnetic resonance imaging-targeted and systematic biopsy for significant prostate cancer detection. BJU Int. 2011;108:E171–8. https://doi.org/10.1111/j.1464-410X.2011.10112.x
doi: 10.1111/j.1464-410X.2011.10112.x pubmed: 21426475
Hamoen EHJ, de Rooij M, Witjes JA, Barentsz JO, Rovers MM. Use of the prostate imaging reporting and data system (PI-RADS) for prostate cancer detection with multiparametric magnetic resonance imaging: a diagnostic meta-analysis. Eur Urol. 2015;67:1112–21. https://doi.org/10.1016/j.eururo.2014.10.033
doi: 10.1016/j.eururo.2014.10.033 pubmed: 25466942
EAU Guidelines on Prostate Cancer - Uroweb. Uroweb - Eur Assoc Urol n.d. https://uroweb.org/guidelines/prostate-cancer#4 . Accessed 21 May 2023.
Purysko AS, Rosenkrantz AB, Turkbey IB, Macura KJ. RadioGraphics update: PI-RADS Version 2.1—a pictorial update. RadioGraphics. 2020;40:E33–7. https://doi.org/10.1148/rg.2020190207
doi: 10.1148/rg.2020190207 pubmed: 33136475
Oerther B, Engel H, Bamberg F, Sigle A, Gratzke C, Benndorf M. Cancer detection rates of the PI-RADSv2.1 assessment categories: systematic review and meta-analysis on lesion level and patient level. Prostate Cancer Prostatic Dis. 2022;25:256–63. https://doi.org/10.1038/s41391-021-00417-1
doi: 10.1038/s41391-021-00417-1 pubmed: 34230616
Schoots IG. MRI in early prostate cancer detection: how to manage indeterminate or equivocal PI-RADS 3 lesions? Transl Androl Urol. 2018;7:70–82. https://doi.org/10.21037/tau.2017.12.31
doi: 10.21037/tau.2017.12.31 pubmed: 29594022 pmcid: 5861283
Weinreb JC, Barentsz JO, Choyke PL, Cornud F, Haider MA, Macura KJ, et al. PI-RADS prostate imaging - reporting and data system: 2015, version 2. Eur Urol. 2016;69:16–40. https://doi.org/10.1016/j.eururo.2015.08.052
doi: 10.1016/j.eururo.2015.08.052 pubmed: 26427566
Turkbey B, Rosenkrantz AB, Haider MA, Padhani AR, Villeirs G, Macura KJ, et al. Prostate imaging reporting and data system version 2.1: 2019 update of prostate imaging reporting and data system version 2. Eur Urol. 2019;76:340–51. https://doi.org/10.1016/j.eururo.2019.02.033
doi: 10.1016/j.eururo.2019.02.033 pubmed: 30898406
Epstein JI, Egevad L, Amin MB, Delahunt B, Srigley JR, Humphrey PA, et al. The 2014 International Society of Urological Pathology (ISUP) consensus conference on gleason grading of prostatic carcinoma: definition of grading patterns and proposal for a new grading system. Am J Surg Pathol. 2016;40:244–52. https://doi.org/10.1097/PAS.0000000000000530
doi: 10.1097/PAS.0000000000000530 pubmed: 26492179
Riley RD, Ensor J, Snell KIE, Harrell FE, Martin GP, Reitsma JB, et al. Calculating the sample size required for developing a clinical prediction model. BMJ. 2020;368:m441. https://doi.org/10.1136/bmj.m441
doi: 10.1136/bmj.m441. pubmed: 32188600
Zeng J, Cheng Q, Zhang D, Fan M, Shi C, Luo L. Diagnostic ability of dynamic contrast-enhanced magnetic resonance imaging for prostate cancer and clinically significant prostate cancer in equivocal lesions: a systematic review and meta-analysis. Front Oncol. 2021;11:620628.
doi: 10.3389/fonc.2021.620628 pubmed: 33680965 pmcid: 7933498
Hong SK, Song SH, Kim HJ, Lee HS, Nam JH, Lee SB. Temporal changes of PIRADS scoring by radiologists and correlation to radical prostatectomy pathological outcomes. Prostate Int. 2022;10:188–93. https://doi.org/10.1016/j.prnil.2022.07.001
doi: 10.1016/j.prnil.2022.07.001 pubmed: 36570646 pmcid: 9747593
Cornford P, van den Bergh RCN, Briers E, Van den Broeck T, Brunckhorst O, Darraugh J, et al. EAU-EANM-ESTRO-ESUR-ISUP-SIOG guidelines on prostate cancer-2024 update. Part I: screening, diagnosis, and local treatment with curative intent. Eur Urol. 2024:S0302-2838(24)02254-1. https://doi.org/10.1016/j.eururo.2024.03.027
Boschheidgen M, Schimmöller L, Doerfler S, Al-Monajjed R, Morawitz J, Ziayee F, et al. Single center analysis of an advisable control interval for follow-up of patients with PI-RADS category 3 in multiparametric MRI of the prostate. Sci Rep. 2022;12:6746 https://doi.org/10.1038/s41598-022-10859-9
doi: 10.1038/s41598-022-10859-9 pubmed: 35469056 pmcid: 9038748
Venderink W, van Luijtelaar A, Bomers JGR, van der Leest M, Hulsbergen-van de Kaa C, Barentsz JO, et al. Results of targeted biopsy in men with magnetic resonance imaging lesions classified equivocal, likely or highly likely to be clinically significant prostate cancer. Eur Urol. 2018;73:353–60. https://doi.org/10.1016/j.eururo.2017.02.021
doi: 10.1016/j.eururo.2017.02.021 pubmed: 28258784
Szempliński S, Kamecki H, Dębowska M, Zagożdżon B, Mokrzyś M, Zawadzki M, et al. Predictors of clinically significant prostate cancer in patients with PIRADS categories 3–5 undergoing magnetic resonance imaging-ultrasound fusion biopsy of the prostate. J Clin Med. 2023;12:156 https://doi.org/10.3390/jcm12010156
doi: 10.3390/jcm12010156
Felker ER, Raman SS, Margolis DJ, Lu DSK, Shaheen N, Natarajan S, et al. Risk stratification among men with prostate imaging reporting and data system version 2 category 3 transition zone lesions: is biopsy always necessary? Am J Roentgenol. 2017;209:1272–7. https://doi.org/10.2214/AJR.17.18008
doi: 10.2214/AJR.17.18008
Sheridan AD, Nath SK, Syed JS, Aneja S, Sprenkle PC, Weinreb JC, et al. Risk of clinically significant prostate cancer associated with prostate imaging reporting and data system category 3 (equivocal) lesions identified on multiparametric prostate MRI. Am J Roentgenol. 2018;210:347–57. https://doi.org/10.2214/AJR.17.18516
doi: 10.2214/AJR.17.18516
Schoots IG, Padhani AR. Risk-adapted biopsy decision based on prostate magnetic resonance imaging and prostate-specific antigen density for enhanced biopsy avoidance in first prostate cancer diagnostic evaluation. BJU Int. 2021;127:175–8. https://doi.org/10.1111/bju.15277
doi: 10.1111/bju.15277 pubmed: 33089586
Kim TJ, Lee MS, Hwang SI, Lee HJ, Hong SK. Outcomes of magnetic resonance imaging fusion-targeted biopsy of prostate imaging reporting and data system 3 lesions. World J Urol. 2019;37:1581–6. https://doi.org/10.1007/s00345-018-2565-3
doi: 10.1007/s00345-018-2565-3 pubmed: 30460594
Aussavavirojekul P, Hoonlor A, Srinualnad S. Optimization of clinical risk-factor interpretation and radiological findings with machine learning for PIRADS category 3 patients. Prostate. 2022;82:235–44. https://doi.org/10.1002/pros.24266
doi: 10.1002/pros.24266 pubmed: 34783053
Ferro M, Crocetto F, La Civita E, Fiorenza M, Jannuzzi G, Carbone G, et al. Serum (-2)proPSA/freePSAratio, (-2)proPSA/freePSA density, prostate health index, and prostate health index density as clues to reveal postoperative clinically significant prostate cancer in men with prostate-specific antigen 2-10ng/mL. Prostate. 2024. https://doi.org/10.1002/pros.24752
Ferro M, Crocetto F, Bruzzese D, Imbriaco M, Fusco F, Longo N, et al. Prostate health index and multiparametric MRI: partners in crime fighting overdiagnosis and overtreatment in prostate cancer. Cancers. 2021;13:4723. https://doi.org/10.3390/cancers13184723
doi: 10.3390/cancers13184723 pubmed: 34572950 pmcid: 8466029
Wetterauer C, Matthias M, Pueschel H, Deckart A, Bubendorf L, Mortezavi A, et al. Opportunistic prostate cancer screening with biparametric magnetic resonance imaging (VISIONING). Eur Urol Focus. 2024:S2405-4569(24)00023-3. https://doi.org/10.1016/j.euf.2024.02.006

Auteurs

Georges Mjaess (G)

Department of Urology, Jules Bordet Institute-Erasme Hospital, Hôpital Universitaire de Bruxelles, Université Libre de Bruxelles, Brussels, Belgium.

Laura Haddad (L)

Department of Urology, Jules Bordet Institute-Erasme Hospital, Hôpital Universitaire de Bruxelles, Université Libre de Bruxelles, Brussels, Belgium.

Teddy Jabbour (T)

Department of Urology, Jules Bordet Institute-Erasme Hospital, Hôpital Universitaire de Bruxelles, Université Libre de Bruxelles, Brussels, Belgium.

Arthur Baudewyns (A)

Department of Urology, Jules Bordet Institute-Erasme Hospital, Hôpital Universitaire de Bruxelles, Université Libre de Bruxelles, Brussels, Belgium.

Henri-Alexandre Bourgeno (HA)

Department of Urology, Jules Bordet Institute-Erasme Hospital, Hôpital Universitaire de Bruxelles, Université Libre de Bruxelles, Brussels, Belgium.

Yolène Lefebvre (Y)

Department of Radiology, Jules Bordet Institute-Erasme Hospital, Hôpital Universitaire de Bruxelles, Université Libre de Bruxelles, Brussels, Belgium.

Mariaconsiglia Ferriero (M)

Department of Urology, IRCCS "Regina Elena" National Cancer Institute, Rome, Italy.

Giuseppe Simone (G)

Department of Urology, IRCCS "Regina Elena" National Cancer Institute, Rome, Italy.

Alexandre Fourcade (A)

Department of Urology, Hôpital Cavale Blanche, CHRU Brest, Brest, France.

Georges Fournier (G)

Department of Urology, Hôpital Cavale Blanche, CHRU Brest, Brest, France.

Marco Oderda (M)

Department of Urology, Città della Salute e della Scienza di Torino, University of Turin, Turin, Italy.

Paolo Gontero (P)

Department of Urology, Città della Salute e della Scienza di Torino, University of Turin, Turin, Italy.

Adrian Bernal-Gomez (A)

Department of Urology, Clinique Saint-Augustin, Bordeaux, France.

Alessandro Mastrorosa (A)

Department of Urology, Clinique Saint-Augustin, Bordeaux, France.

Jean-Baptiste Roche (JB)

Department of Urology, Clinique Saint-Augustin, Bordeaux, France.

Rawad Abou Zahr (R)

Department of Urology, La Croix du Sud Hospital, Quint Fonsegrives, France.

Guillaume Ploussard (G)

Department of Urology, La Croix du Sud Hospital, Quint Fonsegrives, France.

Gaelle Fiard (G)

Department of Urology, Grenoble Alpes University Hospital, Université Grenoble Alpes, CNRS, Grenoble INP, TIMC, Grenoble, France.

Adam Halinski (A)

Department of Urology, Private Medical Center "Klinika Wisniowa", Zielona Góra, Poland.

Katerina Rysankova (K)

Department of Urology, University Hospital Ostrava, Ostrava, Czech Republic.
Department of Surgical Studies, Faculty of Medicine, Ostrava University, Ostrava, Czech Republic.

Charles Dariane (C)

Department of Urology, Hôpital Européen Georges-Pompidou, Université de Paris, Paris, France.

Gina Delavar (G)

Departement of Urology, Hôpital Cochin, Paris, France.

Julien Anract (J)

Departement of Urology, Hôpital Cochin, Paris, France.

Nicolas Barry Delongchamps (N)

Departement of Urology, Hôpital Cochin, Paris, France.

Alexandre Patrick Bui (AP)

Department of Urology, Centre Hospitalier Universitaire de Reims, Reims, France.

Fayek Taha (F)

Department of Urology, Centre Hospitalier Universitaire de Reims, Reims, France.

Olivier Windisch (O)

Department of Urology, Hôpitaux Universitaires de Genève, Geneva, Switzerland.

Daniel Benamran (D)

Department of Urology, Hôpitaux Universitaires de Genève, Geneva, Switzerland.

Gregoire Assenmacher (G)

Department of Urology, Cliniques de l'Europe-Saint Elisabeth, Brussels, Belgium.

Jan Benijts (J)

Department of Urology, Cliniques de l'Europe-Saint Elisabeth, Brussels, Belgium.

Karsten Guenzel (K)

Department of Urology, Vivantes Klinikum am Urban, Berlin, Germany.

Thierry Roumeguère (T)

Department of Urology, Jules Bordet Institute-Erasme Hospital, Hôpital Universitaire de Bruxelles, Université Libre de Bruxelles, Brussels, Belgium.

Alexandre Peltier (A)

Department of Urology, Jules Bordet Institute-Erasme Hospital, Hôpital Universitaire de Bruxelles, Université Libre de Bruxelles, Brussels, Belgium.

Romain Diamand (R)

Department of Urology, Jules Bordet Institute-Erasme Hospital, Hôpital Universitaire de Bruxelles, Université Libre de Bruxelles, Brussels, Belgium. romain.diamand@hubruxelles.be.

Classifications MeSH