Risk Estimation of Metastatic Recurrence After Prostatectomy: A Model Using Preoperative Magnetic Resonance Imaging and Targeted Biopsy.

Gleason pattern 4/5 Prostate cancer Recurrence risk Tumor volume

Journal

European urology open science
ISSN: 2666-1683
Titre abrégé: Eur Urol Open Sci
Pays: Netherlands
ID NLM: 101771568

Informations de publication

Date de publication:
Jul 2022
Historique:
accepted: 14 04 2022
entrez: 11 7 2022
pubmed: 12 7 2022
medline: 12 7 2022
Statut: epublish

Résumé

The risk of prostate cancer metastatic is correlated with its volume and grade. These parameters are now best estimated preoperatively with magnetic resonance imaging (MRI) and MRI-guided biopsy. To estimate the risk of metastatic recurrence after radical prostatectomy (RP) in our model versus conventional clinical European Association of Urology (EAU) classification. The secondary objective is biochemical recurrence (BCR). A retrospective study was conducted of a cohort of 713 patients having undergone MRI-guided biopsies and RP between 2009 and 2018. The preoperative variables included prostate-specific antigen, cT stage, tumor volume (TV) based on the lesion's largest diameter at MRI, percentage of Gleason pattern 4/5 (%GP4/5) at MRI-guided biopsy, and volume of GP4/5 (VolGP4/5) calculated as TV × %GP4/5. The variables' ability to predict recurrence was determined in univariable and multivariable Fine-and-Gray models, according to the Akaike information criterion (AIC) and Harrell's C-index. Overall, 176 (25%), 430 (60%), and 107 (15%) patients had low, intermediate, and high-risk disease, respectively, according to the EAU classification. During a median follow-up period of 57 mo, metastatic recurrence was observed in 48 patients with a 5-yr probability of 5.6% (95% confidence interval [CI] 3.9-7.7). VolGP4/5 (categories: <0.5, 0.5-1.0, 1.01-3.2, and >3.2 ml) was the parameter with the lowest AIC and the highest C-index for metastatic recurrence of 0.82 (95% CI 0.76-0.88), and for BCR it was 0.73 (95% CI 0.68-0.78). In a multivariable model that included %GP4/5 and TV, C-index values were 0.86 (95% CI 0.79-0.91) for metastatic recurrence and 0.77 (0.72-0.82) for BCR. The same results for EAU classification were 0.74 (0.67-0.80) and 0.67 (0.63-0.72), respectively. Limitations are related to short follow-up and expertise of radiologists and urologists. We developed a preoperative risk tool integrating the VolGP4/5 based on MRI and MRI-guided biopsies to predict metastatic recurrence after RP. Our model showed higher accuracy than conventional clinical risk models. These findings might enable physicians to provide more personalized patient care. Aggressiveness of prostate cancer evaluated before treatment by incorporating magnetic resonance imaging (MRI) and MRI-guided biopsy results gives a better estimate of the risk of metastatic recurrence than previous parameters not based on MRI.

Sections du résumé

Background UNASSIGNED
The risk of prostate cancer metastatic is correlated with its volume and grade. These parameters are now best estimated preoperatively with magnetic resonance imaging (MRI) and MRI-guided biopsy.
Objective UNASSIGNED
To estimate the risk of metastatic recurrence after radical prostatectomy (RP) in our model versus conventional clinical European Association of Urology (EAU) classification. The secondary objective is biochemical recurrence (BCR).
Design setting and participants UNASSIGNED
A retrospective study was conducted of a cohort of 713 patients having undergone MRI-guided biopsies and RP between 2009 and 2018. The preoperative variables included prostate-specific antigen, cT stage, tumor volume (TV) based on the lesion's largest diameter at MRI, percentage of Gleason pattern 4/5 (%GP4/5) at MRI-guided biopsy, and volume of GP4/5 (VolGP4/5) calculated as TV × %GP4/5.
Outcome measurements and statistical analysis UNASSIGNED
The variables' ability to predict recurrence was determined in univariable and multivariable Fine-and-Gray models, according to the Akaike information criterion (AIC) and Harrell's C-index.
Results and limitations UNASSIGNED
Overall, 176 (25%), 430 (60%), and 107 (15%) patients had low, intermediate, and high-risk disease, respectively, according to the EAU classification. During a median follow-up period of 57 mo, metastatic recurrence was observed in 48 patients with a 5-yr probability of 5.6% (95% confidence interval [CI] 3.9-7.7). VolGP4/5 (categories: <0.5, 0.5-1.0, 1.01-3.2, and >3.2 ml) was the parameter with the lowest AIC and the highest C-index for metastatic recurrence of 0.82 (95% CI 0.76-0.88), and for BCR it was 0.73 (95% CI 0.68-0.78). In a multivariable model that included %GP4/5 and TV, C-index values were 0.86 (95% CI 0.79-0.91) for metastatic recurrence and 0.77 (0.72-0.82) for BCR. The same results for EAU classification were 0.74 (0.67-0.80) and 0.67 (0.63-0.72), respectively. Limitations are related to short follow-up and expertise of radiologists and urologists.
Conclusions UNASSIGNED
We developed a preoperative risk tool integrating the VolGP4/5 based on MRI and MRI-guided biopsies to predict metastatic recurrence after RP. Our model showed higher accuracy than conventional clinical risk models. These findings might enable physicians to provide more personalized patient care.
Patient summary UNASSIGNED
Aggressiveness of prostate cancer evaluated before treatment by incorporating magnetic resonance imaging (MRI) and MRI-guided biopsy results gives a better estimate of the risk of metastatic recurrence than previous parameters not based on MRI.

Identifiants

pubmed: 35813259
doi: 10.1016/j.euros.2022.04.011
pii: S2666-1683(22)00586-9
pmc: PMC9257652
doi:

Types de publication

Journal Article

Langues

eng

Pagination

24-34

Informations de copyright

© 2022 The Author(s).

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Auteurs

Thomas Bommelaere (T)

Department of Urology, University of Lille, Lille, France.

Arnauld Villers (A)

Department of Urology, University of Lille, Lille, France.
UMR8161/CNRS-Institut de Biologie de Lille, Lille, France.

Philippe Puech (P)

Department of Radiology, University of Lille, 59000 Lille, France.

Guillaume Ploussard (G)

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

Julien Labreuche (J)

Department of Biostatistics, CHU Lille, Lille, France.
ULR 2694 - METRICS: Évaluation des technologies de santé et des pratiques médicales, CHU Lille, University of Lille, Lille, France.

Elodie Drumez (E)

Department of Biostatistics, CHU Lille, Lille, France.
ULR 2694 - METRICS: Évaluation des technologies de santé et des pratiques médicales, CHU Lille, University of Lille, Lille, France.

Xavier Leroy (X)

Department of Histopathology, University of Lille, Lille, France.

Jonathan Olivier (J)

Department of Urology, University of Lille, Lille, France.
UMR8161/CNRS-Institut de Biologie de Lille, Lille, France.

Classifications MeSH