AI Article Synopsis
- Developed a '2010 Partin Nomogram' that utilizes total prostate-specific antigen (tPSA) as a continuous biomarker for better risk stratification compared to the previous 2007 model, which categorized tPSA levels into groups.
- Utilized multinomial logistic regression analysis on a large cohort of patients who underwent radical prostatectomy to predict the risk of various pathological outcomes based on tPSA levels, clinical stage, and Gleason score.
- The new nomogram offers improved accuracy in predicting pathological stages and helps determine a patient's risk percentile, influencing treatment decisions regarding radical prostatectomy.
Article Abstract
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.
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|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3082635 | PMC |
| http://dx.doi.org/10.1111/j.1464-410X.2010.09692.x | DOI Listing |
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