Mp60-15 Pre-biopsy Urine Exosome 3-gene Signature and Pathology Upgrading Risk in Radical Prostatectomy (rp)
Year: 2021
Type: article
Study design: Observational Study
Abstract: of this study was to develop and externally validate a model to predict high risk PCa according to the Decipher score, a validated 22 gene prognostic panel.METHODS: We retrospectively reviewed IRB approved databases at two institutions to develop and externally validate a multivariable model.Patients who underwent RP with Decipher test and preoperative MRI were included in the analysis.Primary outcome was high risk Decipher on RP specimen.Uni-and multivariable analyses were performed to create a model predicting High Risk Decipher score.Area under the curve (AUC) and calibration were used to assess the accuracy of the model before and after leave one out cross validation (LOOCV) in the development and validation cohort.Finally, Decision curve analysis (DCA) was performed to assess the clinical benefit of the model.RESULTS: The development and validation cohort included 622 and 185 patients from two different institutions with 283 (35%) and 80 (43%) of those with High risk Decipher score.The multivariate model with the best performance included PSA density, biopsy Gleason Grade Group, percentage of positive cores and MRI ECE.AUC was 0.73 after LOOCV and the calibration was excellent.DCA showed a clinical benefit in a range of probabilities between 15 and 60%.In the external validation cohort, AUC was 0.70 but the model slightly underestimated the actual probability of the outcome.CONCLUSIONS: We developed and externally validated a model to predict High risk Decipher score at radical prostatectomy.This model could be helpful to improve risk stratification of patients with PCa and to assess the need for additional testing and treatments.
Source: The Journal of Urology
Authors: Gordon Brown, Jason Alter, Michael Donovan, Sonia Kumar, Vasisht Tadigotla, Johan Skog, Mikkel Noerholm, Christian Fischer, Grannum Sant
Language: English
Subfield: Urology
Field: Medicine
Domain: Health Sciences
Open Access status: bronze
OpenAlex ID: W3191156098