External Validation of Models Predicting the Probability of Lymph Node Involvement in Prostate Cancer Patients

Tom A. Hueting*, Erik B. Cornel, Diederik M. Somford, Hanneke Jansen, Jean Paul A. van Basten, Rick G. Pleijhuis, Ruben A. Korthorst, Job A.M. van der Palen, Hendrik Koffijberg

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

10 Citations (Scopus)

Abstract

Background: Multiple statistical models predicting lymph node involvement (LNI) in prostate cancer (PCa) exist to support clinical decision-making regarding extended pelvic lymph node dissection (ePLND). Objective: To validate models predicting LNI in Dutch PCa patients. Design, setting, and participants: Sixteen prediction models were validated using a patient cohort of 1001 men who underwent ePLND. Patient characteristics included serum prostate specific antigen (PSA), cT stage, primary and secondary Gleason scores, number of biopsy cores taken, and number of positive biopsy cores. Outcome measurements and statistical analysis: Model performance was assessed using the area under the receiver operating characteristic curve (AUC). Calibration plots were used to visualize over- or underestimation by the models. Results and limitations: LNI was identified in 276 patients (28%). Patients with LNI had higher PSA, higher primary Gleason pattern, higher Gleason score, higher number of nodes harvested, higher number of positive biopsy cores, and higher cT stage compared to patients without LNI. Predictions generated by the 2012 Briganti nomogram (AUC 0.76) and the Memorial Sloan Kettering Cancer Center (MSKCC) web calculator (AUC 0.75) were the most accurate. Calibration had a decisive role in selecting the most accurate models because of overlapping confidence intervals for the AUCs. Underestimation of LNI probability in patients had a predicted probability of <20%. The omission of model updating was a limitation of the study. Conclusions: Models predicting LNI in PCa patients were externally validated in a Dutch patient cohort. The 2012 Briganti and MSKCC nomograms were identified as the most accurate prediction models available. Patient summary: In this report we looked at how well models were able to predict the risk of prostate cancer spreading to the pelvic lymph nodes. We found that two models performed similarly in predicting the most accurate probabilities. Nomograms developed by Briganti et al and the Memorial Sloan Kettering Cancer Center were best at predicting lymph node involvement in prostate cancer patients. These models support clinical decision-making on whether to perform pelvic lymph node dissection.

Original languageEnglish
Pages (from-to)411-417
Number of pages7
JournalEuropean urology oncology
Volume1
Issue number5
Early online date28 Jun 2018
DOIs
Publication statusPublished - 1 Oct 2018

Keywords

  • External validation
  • Lymph node involvement
  • Nomograms
  • Pelvic lymph node dissection
  • Prediction models
  • Prostate cancer

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