Integrating serum biomarkers into prediction models for biochemical recurrence following radical prostatectomy

Shirin Moghaddam, Amirhossein Jalali, Amanda O’neill, Lisa Murphy, Laura Gorman, Anne Marie Reilly, Áine Heffernan, Thomas Lynch, Richard Power, Kieran J. O’malley, Kristin A. Taskèn, Viktor Berge, Vivi Ann Solhaug, Helmut Klocker, T. Brendan Murphy, R. William Watson

Research output: Contribution to journalArticlepeer-review

Abstract

This study undertook to predict biochemical recurrence (BCR) in prostate cancer patients after radical prostatectomy using serum biomarkers and clinical features. Three radical prostatectomy cohorts were used to build and validate a model of clinical variables and serum biomarkers to predict BCR. The Cox proportional hazard model with stepwise selection technique was used to develop the model. Model evaluation was quantified by the AUC, calibration, and decision curve analysis. Cross-validation techniques were used to prevent overfitting in the Irish training cohort, and the Austrian and Norwegian independent cohorts were used as validation cohorts. The integration of serum biomarkers with the clinical variables (AUC = 0.695) improved significantly the predictive ability of BCR compared to the clinical variables (AUC = 0.604) or biomarkers alone (AUC = 0.573). This model was well calibrated and demonstrated a significant improvement in the predictive ability in the Austrian and Norwegian validation cohorts (AUC of 0.724 and 0.606), compared to the clinical model (AUC of 0.665 and 0.511). This study shows that the pre-operative biomarker PEDF can improve the accuracy of the clinical factors to predict BCR. This model can be employed prior to treatment and could improve clinical decision making, impacting on patients’ outcomes and quality of life.

Original languageEnglish
Article number4162
JournalCancers
Volume13
Issue number16
DOIs
Publication statusPublished - 2 Aug 2021
Externally publishedYes

Keywords

  • Biochemical recurrence
  • Calibration
  • Cox model
  • Cytokine
  • Discrimination
  • Model evaluation
  • Prediction models
  • Prostate cancer

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