Misop Han, Alan W. Partin, Marianna Zahurak, Steven Piantadosi, Jonathan I. Epstein, and Patrick C. Walsh
The "Han tables" were developed by urologists, Misop Han, M.D., Alan W. Partin, M.D., Ph.D., and Patrick C. Walsh, M.D., based on accumulated data from thousands of patients who had been treated for prostate cancer at the James Buchanan Brady Urological Institute, Johns Hopkins Hospital. After using The Partin Tables to predict the definitive pathological stage (the extent of cancer spread), men and their doctors may want to know the probability of recurrence following surgery (radical prostatectomy). The Han tables were designed to predict the probability of the first evidence of recurrence (detectable PSA level) following surgery.
Similar to The Partin Tables, the Han Tables correlate the three common factors known about a man’s prostate cancer, PSA level, Gleason score, and clinical stage (or pathological stage). While The Partin Tables are used to predict pathological stage, the Han Tables are used to predict the probability of prostate cancer recurrence up to 10 years following surgery. Based on the result of the probability of recurrence, men and their doctors can decide the best course of treatment after surgery.
1. Preoperative model (For men who are considering surgery for prostate cancer, but have not had surgery yet)
Prediction of recurrence probability following surgery using the available information BEFORE the surgery (PSA level, biopsy Gleason score, and clinical stage)
Based upon PSA, Gleason Score, and Clinical Stage, recurrence probability is calculated at 3, 5, 7, and 10 years following surgery
Prediction of recurrence probability following surgery using the available information BEFORE AND AFTER the surgery (PSA level, surgical Gleason score, and pathological stage)
Based upon PSA, Surgical Gleason Score, and Pathological Stage, recurrence probability is calculated at 3, 5, 7, and 10 years following surgery
Knowing the probability of recurrence following surgery would help patients rationally choose appropriate treatment options, either primary and/or adjuvant therapy, for prostate cancer. The tables are going to be updated soon to combine the information of prostate cancer patients from multiple institutions.
Statistical methods to build biochemical recurrence prediction models.
Available preoperative parameters for multivariate analysis were age, clinical TNM stage, Gleason score from biopsy specimen (categorical), PSA (categorically divided into 4.0 or less, 4.1 to 10.0, 10.1 to 20.0 and greater than 20.0 ng./ml.) and acid phosphatase level. Available postoperative variables were organ confinement, focal extraprostatic extension, extensive extraprostatic extension, lymph node involvement, seminal vesicle invasion, surgical margin status and Gleason score from surgical specimen.
Actuarial analysis was performed comparing freedom from biochemical recurrence after radical retropubic prostatectomy (PSA 0.2 ng./ml. or greater). Patients were censored if they were lost to followup or there was no recurrence. Event time distributions for the time to recurrence end point were estimated with the Kaplan-Meier method and compared using the log rank statistic or the proportional hazards regression model. The simultaneous effect of 2 or more factors was studied using the multivariate proportional hazards model. Covariates and interactions marginally significant (p <0.19) in univariate analyses were entered into the multivariate regression model and insignificant effects were removed in a stepwise fashion. The first model was developed using preoperative variables only and the second model using all available variables.
Early detection programs encouraged in the later years of this study produced significant shifts towards early stage disease over time. Interactions of PSA, Gleason score and TNM stage with calendar time were tested to determine if the risks attributable to these variables were also functions of time. For all analyses PSA, Gleason score and TNM stage were categorized into discrete levels and entered in regression models in unfactored form. When calendar year of surgery was entered into the proportional hazards model, a restricted cubic spline transformation was used to allow nonlinearity.
In addition to the shift toward early stage disease, the relative risk of biochemical recurrence following surgery decreased significantly over time. Most importantly, when the relative risk of biochemical recurrence was adjusted for clinical TNM stage, preoperative PSA and Gleason score, there was still a significant decrease in relative risk of biochemical recurrence over time.
Observed and predicted recurrence-free survival curves for 2 proportional hazards models (1 stratified for year of surgery and 1 including year of surgery as predictor) and 3 parametric survival models (Weibull, log-normal and gamma) were compared to select a model for calculation of predicted recurrence-free probabilities and confidence intervals. For each model coefficients from the multivariate regression were used to generate a prognostic factor score for each individual. This score is the weighted average of prognostic factor values with weights determined by the estimated coefficients from the model. The proportional hazards regression model is the patient’s hazard relative to a patient with the most favorable level of all prognostic factors. These scores were then ranked and categorized to form 4 risk groups. Plots of the observed survival, Kaplan-Meier curves, for these risk groups were then compared to the model of predicted recurrence-free survival for each risk group (data not shown). From the chosen model (proportional hazards), the nomograms were constructed from biochemical recurrence-free survival probability with corresponding 95% confidence intervals at 3, 5, 7 and 10 years following radical retropubic prostatectomy, adjusting for the latest year in which surgical data were available (1999). All p values are 2-sided. All statistical analyses were performed using the Intercooled Stata 6.0 statistics and graphics data management system (Stata Corporation, College Station, Texas), SAS system (SAS Institute, Inc., Cary, North Carolina), EGRET (Cytel Statistical Software, Cambridge, Massachusetts) or the S-plus Design package (Mathsoft Data Analysis Products Division, Seattle, Washington).
Our models are based on the followup information from a large group of men who underwent radical retropubic prostatectomy. All 5 variables integrated in the final model are commonly used and readily reproducible for men who are considering or have already undergone radical retropubic prostatectomy. We excluded from analysis men with clinical stage T1a/b or T3a disease or Gleason score less than 5 since the percentage of men with those diseases has decreased significantly in the contemporary patient population. Our nomograms provide biochemical recurrence-free survival probability, which is easier to explain to patients.
The most important distinction of our models is that we integrated a significant downward stage migration and an improved surgical outcome over time into the models. We have previously demonstrated the decreasing relative risk of biochemical recurrence following surgery in the modern era. That change may reflect the benefits of early detection, better preoperative selection of patients for surgery as well as lead time bias. In the current study we attempted to delineate whether downward stage migration alone could account for the improved therapeutic outcome over time. When the relative risk of biochemical recurrence was adjusted for clinical TNM stage, preoperative PSA and Gleason score, there was still a significant decrease in relative risk of biochemical recurrence over time. Since patients have a decreasing relative risk of biochemical recurrence over time, our nomograms were generated for men who underwent radical retropubic prostatectomy at the latest year of followup (1999). Therefore, these nomograms can be applied to men with clinically localized prostate cancer who underwent or plan to undergo surgery in the modern era.
A man with newly diagnosed prostate cancer has to make important decisions regarding treatment. The Partin tables enabled physicians and patients to make more informed treatment decisions based on the probability of pathological stage for clinically localized prostate cancer. When a patient experiences biochemical recurrence following radical retropubic prostatectomy, the study by Pound et al can be informative as well as comforting regarding the interval from PSA detection to evidence of metastasis. They demonstrated that disease progression from an isolated PSA increase to metastasis and cancer specific mortality is generally a protracted process. The algorithm in their study provided the risk for developing metastatic cancer so that patients and physicians could decide on the need for and timing of the most appropriate adjuvant therapy following postoperative biochemical recurrence.
In addition to the Partin tables and the Pound algorithm, the tables in the present study can help patients rationally decide on the best treatment options depending on the probability of recurrence-free survival following radical prostatectomy according to disease characteristics in the modern era. In addition, these tables can guide physicians caring for men with prostate cancer in determining how often and what type of monitoring tests should be performed following surgery. Finally, the nomograms can help physicians determine whether adjuvant therapy may be beneficial or which patients would benefit from novel adjuvant therapy.
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