Implementing the use of nomograms by choosing threshold points in predictive models: 2012 updated Partin Tables vs a European predictive nomogram for organ-confined disease in prostate cancer




  • To implement the use of nomograms in clinical practice showing how to choose thresholds in nomograms’ predictions to select risk groups.
  • To validate and compare the predictive ability and clinical utility of the Hospital Universitario ‘Miguel Servet’ (HUMS) and the updated Partin Tables 2012 (PT-2012) nomograms to predict organ-confined disease (OCD) after radical prostatectomy (RP).

Patients and Methods

  • Cohort of 1285 patients with prostate cancer treated with RP at Instituto Valenciano de Oncología (IVO) between 1986 and 2011.
  • The predictive value of the nomograms was assessed by means of calibration curves, discrimination ability (area under the receiver operating characteristic (ROC) curve (AUC) and probability density functions).
  • The clinical utility was evaluated through Vickers’ decision curves and thresholds were chosen through probability density functions.


  • The calibration curves showed a minimal underestimation in low probabilities (<20%), a minimal overestimation in high probabilities (>50%) in the HUMS nomogram and a regular minimal overestimation in the PT-2012. Their AUC of 0.7285 (95% confidence interval [CI] 0.7010–0.7559) and 0.7288 (95%CI 0.7013–0.7562) respectively, show an adequate discrimination ability for both predictive models in the IVO cohort.
  • The decision curves show similar net benefits for both models.
  • In this study we advocate for a threshold of 53% for the identification of OCD.


  • The HUMS-nomogram and the PT-2012 predictions of OCD confirm their utility in a contemporary cohort of patients.
  • Patients with a probability of OCD >53% should be classified as OCD, helping physicians to better counsel their patients.
  • A selection of adequate thresholds, as presented in this paper, makes nomograms more accessible tools.