We commend the efforts of Dr. D’Amico in
developing and presenting his stratification schema of prognostic variables for
patients with localized prostate cancer. Clearly, the benefit of such a
stratification scheme is to better identify those who may be eligible for either
more or less aggressive therapy, as predicted by this study. His stratification
schemes have been demonstrated to be effective.
Nevertheless, we have concerns about the scheme that Dr. D’Amico
has devised and applied. The three groups were constructed logically, using
reasonable cutoff points for stage, grade, and prostate-specific antigen (PSA)
level. However, it is not clear whether they represent the optimal grouping for
prognostic discrimination. Shipley et al developed an empiric
risk-stratification scheme by analyzing pretreatment variables and outcome
results in a large multi-institutional cohort of patients treated with
three-dimensional (3D) conformal radiotherapy (CRT). It is not clear that Dr. D’Amico’s
scheme, although easier to remember, is as accurate as Dr. Shipley’s.
Another approach to risk estimation is the use of prognostic
nomograms that compute continuous probabilities of an outcome. Rather than
placing the patient into one of a few risk groups, prognostic nomograms estimate
probabilities, usually of freedom from progression, although other end points,
such as survival or disease-specific survival, can be predicted as well.
Such nomograms have been developed for prostate cancer treated
with surgery, 3D CRT, and brachytherapy (Figures
1, 2, and 3).[2-4] Their advantage over risk-stratification prediction
systems is likely associated with the use of continuous-risk scales for relevant
technique parameters, rather than condensing sections of the risk spectra into
heterogeneous risk groups. Due to improved accuracy, these nomograms may benefit
patient counseling, and more accurate prediction should translate into better
We compared our radiotherapy nomogram to several published risk
stratification schemes and found that the nomogram provided better
discrimination when applied to a validation dataset of 932 patients. To
illustrate the use of this nomogram and compare its prediction with risk
stratification, consider a patient with clinical stage T2c disease, a
pretreatment PSA level of 6 ng/mL, a biopsy Gleason score of 9, and a planned
radiation dose of 66.6 Gy without neoadjuvant hormonal therapy. Using a common
risk-stratification scheme (without the percentage of core biopsy
information), this patient would be classified into the most favorable group,
which has an associated freedom-from-recurrence prediction of 81% at 5 years.
According to the external radiotherapy nomogram, the same
patient’s predicted probability of 5-year freedom from recurrence is 24%.
Using the preoperative surgery nomogram, the predicted probability of 5-year
freedom from recurrence is 68%, and with the brachytherapy nomogram, it is 62%.
Thus, risk stratification may influence this patient to choose radiation therapy
over surgery or brachytherapy, although with the nomogram approach, this
particular patient would seem to have a better chance of achieving freedom from
progression with surgery or brachytherapy rather than low-dose radiation.
Risk Stratification vs Nomogram Predictions
The risk-stratification approach potentially produces
heterogeneous strata with respect to nomogram predictions and, assuming the
nomogram is more accurate, could be misleading when deciding on a treatment
strategy. The latter possibility is illustrated by the number of patients with
predictions of low freedom from recurrence by the nomogram despite
"favorable" risk stratification, and the number of patients with
predictions of high freedom from recurrence by the nomogram despite
"unfavorable" risk stratification (see
Figure 4). Thus, risk stratification may not always offer the patient
the best estimate of treatment efficacy and may, in fact, lead to a poor
In the development of the three nomograms, the pretreatment PSA
value, Gleason grade, and clinical stage have been identified as important risk
variables in patients with prostate cancer. It appears that the percentage of
positive core biopsies provides additional prognostic information, beyond what
is conveyed by the three standard predictor variables as combined by Dr. D’Amico.
But if his stratification scheme is not optimal, the additional benefit of
biopsy information remains uncertain. One would want to demonstrate such benefit
as an addition to an optimal risk assessment, and we believe nomograms offer a
more accurate initial approach. In fact, we recently assessed the prognostic
value of adding biopsy results to nomogram predictions of progression after
radical prostatectomy and showed that adding the percentage of positive cores
provides limited benefit.
Quantitative analysis of biopsy results generates considerable
data in addition to the percentage of positive cores. Other readily available
systematic biopsy features also appear to yield valuable information. We
recently used a database of 1,047 patients treated with permanent brachytherapy
to examine 26 pretreatment factors that included the following:
Clinical stage, biopsy Gleason sum, and pretreatment PSA
Mean percentage of cancer in an involved core
Maximum percentage of cancer
Mean primary Gleason grade
Mean secondary Gleason grade
Maximum Gleason grade (primary or secondary)
Percentage of cancer in the apex, midregion, and base
Percentage of positive cores
Maximum primary Gleason grade in the apex, midregion, and
Maximum secondary Gleason grade in the apex, midregion, and
Maximum percentage of cancer in the apex, midregion, and
Maximum Gleason grade in the apex, midregion, and base
Maximum primary Gleason grade
Maximum secondary Gleason grade
Four modeling strategies were compared. As a base or first
model, we considered the pretreatment PSA level, clinical stage, and biopsy
Gleason sum as predictors. For the second model, we added the percentage of
positive cores. The third modeling strategy was to use stepwise variable
selection of only those factors (from the pool of 26 variables) that were
statistically significant. The fourth strategy was to apply principal components
analysis, which has theoretical advantages over the other strategies. Principal
components analysis creates component scores that account for maximum variance
in the predictors.
An analysis of the four models showed substantial prediction
differences. Ranking each model based on its ability to predict freedom from
recurrence, we determined that the base modelmodel 1, using pretreatment PSA
level, clinical stage, and Gleason sumhad the lowest predictive value. The
next best model added percentage of positive cores. The variable reduction
approach via principal components analysis produced the most accurately
predictive model, which could easily be implemented with standard database
software or a handheld device. Also, principal components analysis is
advantageous over stepwise variable selection because less information content
is lost, and the resulting predictor variables do not have inflated effects.
This novel approach of variable reduction modeling can be
incorporated into the nomogram modeling techniques described. In addition, this
method may provide a useful extension to the important models developed by Dr. D’Amico.
1. Shipley WU, Thames HD, Sandler HM, et al: Radiation therapy
for clinically localized prostate cancer: A multi-institutional pooled analysis.
JAMA 281:1598-1604, 1999.
2. Kattan MW, Eastham JA, Stapleton AM, et al: A
preoperative nomogram for disease recurrence following radical prostatectomy for
prostate cancer. J Natl Cancer Inst 90:766-771, 1998.
3. Kattan MW, Zelefsky M, Kupelian P, et al: Pretreatment
nomogram for predicting the outcome of three-dimensional conformal radiotherapy
in prostate cancer. J Clin Oncol 18(19):3352-3359, 2000.
4. Kattan MW, Potters L, Blasko J, et al: A pretreatment
nomogram for predicting recurrence-free survival following permanent prostate
brachytherapy in cancer. Urology. In press.
5. Graefen M, Ohori M, Karakiewicz PI, et al: Quantification of
total and high-grade cancer in biopsy cores improves the prediction of failure
after radical prostatectomy (RP) (abstract 730). Proc Am Soc Clin Oncol 20:183a,