External Validation of Models for Prediction of Side-specific Extracapsular Extension in Prostate Cancer Patients Undergoing Radical Prostatectomy.

External validation Extracapsular extension Magnetic resonance imaging Prostate cancer Side-specific Targeted biopsy

Journal

European urology focus
ISSN: 2405-4569
Titre abrégé: Eur Urol Focus
Pays: Netherlands
ID NLM: 101665661

Informations de publication

Date de publication:
03 2023
Historique:
received: 22 06 2022
revised: 29 07 2022
accepted: 08 09 2022
medline: 4 4 2023
pubmed: 25 9 2022
entrez: 24 9 2022
Statut: ppublish

Résumé

Predicting the risk of side-specific extracapsular extension (ECE) is essential for planning nerve-sparing radical prostatectomy (RP) in patients with prostate cancer (PCa). To externally validate available models for prediction of ECE. Sixteen models were assessed in a cohort of 737 consecutive PCa patients diagnosed via multiparametric magnetic resonance imaging (MRI)-targeted and systematic biopsies and treated with RP between January 2016 and November 2021 at eight referral centers. Model performance was evaluated in terms of discrimination using area under the receiver operating characteristic curve (AUC), calibration plots, and decision curve analysis (DCA). Overall, ECE was identified in 308/1474 (21%) prostatic lobes. Prostatic lobes with ECE had higher side-specific clinical stage on digital rectal examination and MRI, number of positive biopsy cores, and International Society of Urological Pathology grade group in comparison to those without ECE (all p < 0.0001). Less optimistic performance was observed in comparison to previous published studies, although the models described by Pak, Patel, Martini, and Soeterik achieved the highest accuracy (AUC ranging from 0.73 to 0.77), adequate calibration for a probability threshold <40%, and the highest net benefit for a probability threshold >8% on DCA. Inclusion of MRI-targeted biopsy data and MRI information in models improved patient selection and clinical usefulness. Using model-derived cutoffs suggested by their authors, approximately 15% of positive surgical margins could have been avoided. Some available models were not included because of missing data, which constitutes a limitation of the study. We report an external validation of models predicting ECE and identified the four with the best performance. These models should be applied for preoperative planning and patient counseling. We validated several tools for predicting extension of prostate cancer outside the prostate gland. These tools can improve patient selection for surgery that spares nerves affecting recovery of sexual potency after removal of the prostate. They could potentially reduce the risk of finding cancer cells at the edge of specimens taken for pathology, a finding that suggests that not all of the cancer has been removed.

Sections du résumé

BACKGROUND
Predicting the risk of side-specific extracapsular extension (ECE) is essential for planning nerve-sparing radical prostatectomy (RP) in patients with prostate cancer (PCa).
OBJECTIVE
To externally validate available models for prediction of ECE.
DESIGN, SETTING, AND PARTICIPANTS
Sixteen models were assessed in a cohort of 737 consecutive PCa patients diagnosed via multiparametric magnetic resonance imaging (MRI)-targeted and systematic biopsies and treated with RP between January 2016 and November 2021 at eight referral centers.
OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS
Model performance was evaluated in terms of discrimination using area under the receiver operating characteristic curve (AUC), calibration plots, and decision curve analysis (DCA).
RESULTS AND LIMITATIONS
Overall, ECE was identified in 308/1474 (21%) prostatic lobes. Prostatic lobes with ECE had higher side-specific clinical stage on digital rectal examination and MRI, number of positive biopsy cores, and International Society of Urological Pathology grade group in comparison to those without ECE (all p < 0.0001). Less optimistic performance was observed in comparison to previous published studies, although the models described by Pak, Patel, Martini, and Soeterik achieved the highest accuracy (AUC ranging from 0.73 to 0.77), adequate calibration for a probability threshold <40%, and the highest net benefit for a probability threshold >8% on DCA. Inclusion of MRI-targeted biopsy data and MRI information in models improved patient selection and clinical usefulness. Using model-derived cutoffs suggested by their authors, approximately 15% of positive surgical margins could have been avoided. Some available models were not included because of missing data, which constitutes a limitation of the study.
CONCLUSIONS
We report an external validation of models predicting ECE and identified the four with the best performance. These models should be applied for preoperative planning and patient counseling.
PATIENT SUMMARY
We validated several tools for predicting extension of prostate cancer outside the prostate gland. These tools can improve patient selection for surgery that spares nerves affecting recovery of sexual potency after removal of the prostate. They could potentially reduce the risk of finding cancer cells at the edge of specimens taken for pathology, a finding that suggests that not all of the cancer has been removed.

Identifiants

pubmed: 36153227
pii: S2405-4569(22)00211-5
doi: 10.1016/j.euf.2022.09.006
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

309-316

Informations de copyright

Copyright © 2022 European Association of Urology. Published by Elsevier B.V. All rights reserved.

Auteurs

Romain Diamand (R)

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

Jean-Baptiste Roche (JB)

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

Elena Lievore (E)

Department of Urology, Clinique Saint-Augustin, Bordeaux, France; Department of Urology, IRCCS IEO Istituto Europeo di Oncologia, Milan, Italy.

Vito Lacetera (V)

Department of Urology, Azienda Ospedaliera Ospedali Riuniti Marche Nord, Pesaro, Italy.

Giuseppe Chiacchio (G)

Department of Urology, Azienda Ospedaliera Ospedali Riuniti Marche Nord, Pesaro, Italy.

Valerio Beatrici (V)

Department of Urology, Azienda Ospedaliera Ospedali Riuniti Marche Nord, Pesaro, Italy.

Riccardo Mastroianni (R)

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.

Olivier Windisch (O)

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

Daniel Benamran (D)

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

Martina Martins Favre (MM)

ImageRive, ID Imagerie, Unilabs SA, Geneva, Switzerland.

Alexandre Fourcade (A)

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

Truong An Nguyen (TA)

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

Georges Fournier (G)

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

Gaelle Fiard (G)

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

Guillaume Ploussard (G)

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

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.

Simone Albisinni (S)

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

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