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Bicenter validation of a risk model for the preoperative prediction of extraprostatic extension of localized prostate cancer combining clinical and multiparametric MRI parameters. | LitMetric

AI Article Synopsis

  • This study validated a risk model that integrates clinical and multiparametric MRI parameters to predict extraprostatic extension (EPE) of prostate cancer before radical prostatectomy.
  • The research analyzed data from 205 patients across two German hospitals, using techniques like receiver operating characteristic analysis to assess the model's effectiveness against ESUR criteria.
  • Results showed the risk model had strong predictive performance (AUC = 0.86) for EPE, similar to the ESUR classification (AUC = 0.87), although its applicability may be limited to populations with a high prevalence of EPE.

Article Abstract

Background: This study aimed to validate a previously published risk model (RM) which combines clinical and multiparametric MRI (mpMRI) parameters to predict extraprostatic extension (EPE) of prostate cancer (PC) prior to radical prostatectomy (RP).

Materials And Methods: A previously published RM combining clinical with mpMRI parameters including European Society of Urogenital Radiology (ESUR) classification for EPE was retrospectively evaluated in a cohort of two urological university hospitals in Germany. Consecutive patients (n = 205, January 2015 -June 2021) with available preoperative MRI images, clinical information including PSA, prostate volume, ESUR classification for EPE, histopathological results of MRI-fusion biopsy and RP specimen were included. Validation was performed by receiver operating characteristic analysis and calibration plots. The RM's performance was compared to ESUR criteria.

Results: Histopathological T3 stage was detected in 43% of the patients (n = 89); 45% at Essen and 42% at Düsseldorf. Discrimination performance between pT2 and pT3 of the RM in the entire cohort was AUC = 0.86 (AUC = 0.88 at site 1 and AUC = 0.85 at site 2). Calibration was good over the entire probability range. The discrimination performance of ESUR classification alone was comparable (AUC = 0.87).

Conclusions: The RM showed good discriminative performance to predict EPE for decision-making for RP as a patient-tailored risk stratification. However, when experienced MRI reading is available, standardized MRI reading with ESUR scoring is comparable regarding information outcome. A main limitation is the potentially limited transferability to other populations because of the high prevalence of EPE in our subgroups.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11415414PMC
http://dx.doi.org/10.1007/s00345-024-05232-6DOI Listing

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