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

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  • Additional Information
    • Contributors:
      Centre Hospitalier Régional Universitaire CHU Lille (CHRU Lille); Miniaturisation pour la Synthèse, l’Analyse et la Protéomique - UAR 3290 (MSAP); Institut de Chimie - CNRS Chimie (INC-CNRS)-Université de Lille-Centre National de la Recherche Scientifique (CNRS); Thérapies Assistées par Lasers et Immunothérapies pour l'Oncologie - U 1189 (OncoThAI); Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lille-Centre Hospitalier Régional Universitaire CHU Lille (CHRU Lille); Clinique La Croix du Sud; Evaluation des technologies de santé et des pratiques médicales - ULR 2694 (METRICS); Université de Lille-Centre Hospitalier Régional Universitaire CHU Lille (CHRU Lille); Hétérogénéité, Plasticité et Résistance aux Thérapies des Cancers = Cancer Heterogeneity, Plasticity and Resistance to Therapies - UMR 9020 - U 1277 (CANTHER); Institut Pasteur de Lille; Pasteur Network (Réseau International des Instituts Pasteur)-Pasteur Network (Réseau International des Instituts Pasteur)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lille-Centre Hospitalier Régional Universitaire CHU Lille (CHRU Lille)-Centre National de la Recherche Scientifique (CNRS)
    • Publication Information:
      CCSD
      Elsevier
    • Publication Date:
      2022
    • Collection:
      LillOA (HAL Lille Open Archive, Université de Lille)
    • Abstract:
      International audience ; BackgroundThe 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.ObjectiveTo 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 participantsA 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 analysisThe 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 limitationsOverall, 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.ConclusionsWe ...
    • Relation:
      info:eu-repo/semantics/altIdentifier/pmid/35813259; PUBMED: 35813259
    • Accession Number:
      10.1016/j.euros.2022.04.011
    • Online Access:
      https://hal.univ-lille.fr/hal-04533660
      https://hal.univ-lille.fr/hal-04533660v1/document
      https://hal.univ-lille.fr/hal-04533660v1/file/1-s2.0-S2666168322005869-main.pdf
      https://doi.org/10.1016/j.euros.2022.04.011
    • Rights:
      http://creativecommons.org/licenses/by-nc-nd/ ; info:eu-repo/semantics/OpenAccess
    • Accession Number:
      edsbas.77657E9F