Prediction of patient-specific risk and percentile cohort risk of pathological stage outcome using continuous prostate-specific antigen measurement, clinical stage and biopsy Gleason score

Study Type – Therapy (case series) Level of Evidence 4 What's known on the subject? and What does the study add? This international collaboration started in 2008 based upon the possible application of the ‘predictiveness curves’ (multinomial logistic regression method) developed at the Fred Hutchinson Cancer Research Center (FHCRC) to the internationally recognized Partin Tables for staging prostate cancer. Dr. Ying Huang, a biostatistician at the FHCRC, applied the ‘predictiveness curve’ statistical modeling concept to the Partin Tables and then created a new Partin Nomogram using total PSA (tPSA) as a continuous variable. The new ‘2010 Partin Nomogram’ stage risk prediction capacity among the total cohort and the individual patients is based on the ‘predictiveness curves’ using the method developed in Huang et al.[16]. For each pathological stage, we calculated ‘the risk’ for each subject in the cohort based on the risk model and made a quantile plot based on the estimated risks. If one considers a point on the ‘predictiveness curve’ with an x-coordinate of value v, then the value of its y-coordinate, which we name R(v), is the 100 × vth percentile of risk in the study cohort. On the other hand, for a particular point on the curve with y-coordinate p, the value of its x-coordinate, which we name R−1(p), corresponds to the percentage of subjects in the study cohort with risk ≤p[i.e. the cumulative distribution function (CDF) of risk at p]. It is likely that this CDF of risk will be useful for clinicians and patients (see Fig. 2 in the article). Dr. Huang has also written an operational R-program to calculate patient's risk and next we intend to develop a user friendly computer program based upon this program to allow the easy use by patients and physicians of the 2010 Partin Nomogram and the corresponding predictiveness curves for patient-specific pathological stage outcome prediction. OBJECTIVES • To develop a ‘2010 Partin Nomogram’ with total prostate-specific antigen (tPSA) as a continuous biomarker, in light of the fact that the current 2007 Partin Tables restrict the application of tPSA as a non-continuous biomarker by creating ‘groups’ for risk stratification with tPSA levels (ng/mL) of 0–2.5, 2.6–4.0, 4.1–6.0, 6.1–10.0 and >10.0. • To use a ‘predictiveness curve’ to calculate the percentile risk of a patient among the cohort. PATIENTS AND METHODS • In all, 5730 and 1646 patients were treated with radical prostatectomy (without neoadjuvant therapy) between 2000 and 2005 at the Johns Hopkins Hospital (JHH) and University Clinic Hamburg-Eppendorf (UCHE), respectively. • Multinomial logistic regression analysis was performed to create a model for predicting the risk of the four non-ordered pathological stages, i.e. organ-confined disease (OC), extraprostatic extension (EPE), and seminal vesicle (SV+) and lymph node (LN+) involvement. • Patient-specific risk was modelled as a function of the B-spline basis of tPSA (with knots at the first, second and third quartiles), clinical stage (T1c, T2a, and T2b/T2c) and biopsy Gleason score (5–6, 3 + 4 = 7, 4 + 3 = 7, 8–10). RESULTS • The ‘2010 Partin Nomogram’ calculates patient-specific absolute risk for all four pathological outcomes (OC, EPE, SV+, LN+) given a patient’s preoperative clinical stage, tPSA and biopsy Gleason score. • While having similar performance in terms of calibration and discriminatory power, this new model provides a more accurate prediction of patients’ pathological stage than the 2007 Partin Tables model. • The use of ‘predictiveness curves’ has also made it possible to obtain the percentile risk of a patient among the cohort and to gauge the impact of risk thresholds for making decisions regarding radical prostatectomy. CONCLUSION • The ‘2010 Partin Nomogram’ using tPSA as a continuous biomarker together with the corresponding ‘predictiveness curve’ will help clinicians and patients to make improved treatment decisions.