
THE HAN TABLES
Misop Han, Alan
W. Partin, Marianna Zahurak, Steven Piantadosi, Jonathan
I. Epstein, and Patrick C. Walsh
Introduction
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.
Instruction on which model to use
If you are considering surgery, but have not had surgery
yet, you want to use the
1. Preoperative model.
If you have had surgery already, you want to use the
2. Postoperative model. 
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 

2.
Postoperative model (For men who have had surgery
for prostate cancer)
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 KaplanMeier 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
recurrencefree 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, lognormal
and gamma) were compared to select a model for calculation of predicted
recurrencefree 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, KaplanMeier curves, for these
risk groups were then compared to the model of predicted recurrencefree
survival for each risk group (data not shown). From the chosen
model (proportional hazards), the nomograms were constructed from
biochemical recurrencefree 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 2sided.
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 Splus Design package (Mathsoft Data Analysis
Products Division, Seattle, Washington).
DISCUSSION
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 recurrencefree
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 recurrencefree
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.
CONCLUSIONS
We reviewed a large series of men who underwent radical prostatectomy
for clinically localized prostate cancer to identify indicators of
biochemical recurrence. Using 3 preoperative or postoperative variables,
we developed multivariate proportional hazards models to determine
the 3, 5, 7 and 10year biochemical recurrencefree survival probabilities
among men who undergo radical prostatectomy for clinically localized
prostate cancer. These nomograms were adjusted for the decreasing
relative risk of biochemical recurrence over time. They may be helpful
in guiding treatment decisions for men who are considering or have
undergone radical prostatectomy for clinically localized prostate
cancer in the modern era.
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