Open Journal of Urology, 2011, 1, 37-47
doi:10.4236/oju.2011.13009 Published Online August 2011 (http://www.SciRP.org/journal/oju)
Copyright © 2011 SciRes. OJU
Nomograms as P redictive Tools for Pros t a t e C a ncer
Patients Who Had Radical Prostatectomy
Saadettin Eskicorapci1, Cenk Acar1, Zafer Sinik1, Z afe r Aybek1, Michael Kattanb2
1Department of Ur ol o gy, Pamukkale University School of Medicine, Denizli, Turkey
2Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, USA
E-mail: drsye@yahoo.com
Received March 22, 2011; revised May 9, 2011; accepted May 18, 2011
Abstract
Prostate cancer is the most common solid cancer for men in the developed countries. Radical prostatectomy
is the most preferred treatment modality for localized prostate cancer. Individual decision making is neces-
sary for each patient because of the diversities in the biological characteristics of the prostate cancer. The
prediction of pathologic stage, prognosis and cancer specific mortality after curative therapy and quality of
life issues are essential for counseling and tailoring treatment in possible candidates of radical prostatectomy.
Several studies demonstrated that nomograms are the best predictive tools regarding the other prediction
models. For better understanding the nomograms in radical prostatectomy patients, they should be classified
according to categories for their use. PSA, Gleason grade and clinical stage are seemed to be the most im-
portant prognostic factors in patients who are candidates for radical prostatectomy. Additionally, the patho-
logical parameters are remarkable prognostic criteria. The Partin tables for predicting the radical prostatec-
tomy pathology and Kattan nomograms for predicting the biochemical recurrences free survival rates are the
most frequently used nomograms. Today, these nomograms should not replace the clinical decisions but they
give significant information for the patients’ prognosis, treatment selection and follow up.
Keywords: Nomogram, Prediction, Prostate Cancer, Prognosis, Radical Prostatectomy
1. Introduction
Prostate cancer is the most common solid cancer for men
in the developed countries [1]. Radical prostatectomy is
the most preferred treatment modality for localized pros-
tate cancer. Individual decision making is necessary for
each patient because of the diversities of the biological
characteristics of the prostate cancer. [2]. Selection of
proper treatment for individual patient is crucial to im-
proving the propensity of cure and survival. The predic-
tion of pathologic stage, prognosis and cancer specific
mortality after curative therapy and quality of life issues
are essential for counseling and tailoring treatment in
possible candidates of radical prostatectomy. Research-
ers have developed predictive and prognostic tools that
are based on statistical models for making more accurate
risk estimation. Contemporarily, these tools are nomo-
grams, risk groupings, artificial neural networks (ANN),
probability tables such as “Partin staging tables” and
CART (classification and regression tree) analyses [3-8].
Several studies demonstrated that nomograms are the
best predictive tools regarding the other prediction mod-
els [9,10].
2. What Is a Nomogram?
Statistically, a nomogram is defined as graphical calcu-
lating scale for related mathematical formula. In medical
science, nomograms are the methods for predicting spe-
cific outcome (biochemical recurrences for prostate can-
cer, breast cancer mortality etc.) and prognosis by using
the significantly prognostic parameters of the disease.
For prostate cancer, it is aimed to make an assumption by
using the prostate cancer data (prostate specific antigen
(PSA), digital rectal examination (DRE), Gleason score,
age, race, etc.) (3). Despite the fact that nomograms are
developed for each stage of the prostate cancer, they
have intensively been studied for localized prostate can-
cer in recent years. Commonly used nomograms are
Partin nomogram (tables) for predicting the radical
prostatectomy (RP) pathology and Kattan nomograms
for predicting biochemical recurrences free survival [2].
38 S. ESKICORAPCI ET AL.
The prediction accuracy of nomograms should absolutely
be assessed and validated internally and externally.
However, the application of such nomograms may be
nonsense without understanding relationship between the
parameters. To better understanding the nomograms in
prostate cancer, they should be classified according to
categories for their use (Table 1).
In this review, we discussed the prediction models as-
sociated with radical prostatectomy (RP).
2.1. Nomograms for Prediction of Pathological
Parameters after Rp (Table 2)
Radical prostatectomy is frequently preferred treatment
options for organ confined prostate cancer. In order to
predict the pathology of the RP and the most suitable
treatments for particular patients, several nomograms
have been developed [2]. In these nomograms, the Pre-
operative parameters are used such as PSA, Gleason
score, clinical stage, cancer volume in biopsy and PSA
density.
2.1.1. The Predictions for Pathological Stage (Partin
Look-up Tables and Others)
In 1987, Oesterling et al. developed a multiple logistic
regression analysis to predict the pathological stage by
using prostate acid phosphatase (PAP) , clinical stage
and Gleason grade for 275 patients and it was the first
publication in this topic [11]. Later, Narayan et al. set up
the probability graphics by using the clinical stage, PSA,
Gleason grade and transrectal ultrasonography [12]. Sub-
sequently, the “Partin” look-up tables were developed to
predict the pathological stage are the frequently used
models.
The Partin tables are first formulated through the pa-
tients’ data at Johns Hopkins University in 1993 [3]. The
Partin Tables were updated in 1997, 2001 and 2007
[13-15]. The aim of the Partin Tables is to predict the
pathological stage using 3 pre-operative parameters as
clinical stage (TNM classification), Gleason grade and
serum PSA. In clinical practice, it is performed in order
to determine the probabilities for organ-confined dis-
eases, seminal vesicular and lymph node involvement.
The prediction accuracy of any nomogram should be
tested through validation (approval) processes, is con-
ducted by the other data sets or populations. The Partin
tables were validated by Blute et al. at Mayo Clinic in
2000 and by Greafen et al. on European patients in 2003.
They stated that use of Partin tables is appropriate for
both groups [16,17]. Also, Augustin et al. firstly com-
pared the two Partin tables (1997 and 2001) and vali-
dated in 2004 [18]. The validation of 2007 Partin tables
was accomplished by using SEER (Surveillance Epide-
miology and End Results) database of American Na-
tional Cancer Institute in early 2010. They found that the
discrimination power of Partin tables for seminal vesical
and lymph node involvement was high but is limited for
predicting extracapsular extension and localized disease
[19].
Owing to rising PSA screening over the years in the
world, increased number of organ confined cancer was
diagnosed due to early detection of prostate cancer in our
Table 1. Classification of nomograms for prediction of radica l p rostatectomy relate d outc o mes.
1) Nomograms for predicting pathological parameters and stages after RP
The predictions for pathological stage (Partin tables and others)
Nomograms to predict the organ confined diseases and extracapsular involvement
Nomograms for the prediction of SV invasion and lymph node involvement
Nomograms to predict the surgical margin status
Nomograms to predict the Gleason score upgrade
Nomograms to predict the location of the tumor (peripheral zone and transitional zone) and tumor volume
Nomograms to predict clinically insignificant cancers
2) Nomograms to predict PSA recurrence/disease free/general survival after RP
Nomograms with the data prior to RP
Nomograms with the data after RP
3) Nomograms to predict Prostate Cancer Specific Mortality After RP
4) Nomograms to predict the quality of life after RP
RP: Radical Prostatectomy; SV: Seminal Vesicle.
Copyright © 2011 SciRes. OJU
S. ESKICORAPCI ET AL.
39
Table 2. Nomograms for predicting pathological parameters in radical prostatectomy.
Referrence Predictions Parameters Number of
patients Accuracy (%) Validation
Narayan
et al. [12] Pathologic stage Biopsy based Stage, Biopsy Gleason sum, PSA 813 Non-specified NA
Partin
et al. [3] Pathologic stage Clinical stage, Biopsy Gleason sum, PSA 703 Non-specified Externally
and updated
Partin
et al. [14] Pathologic stage Clinical stage, Biopsy Gleason sum, PSA 4133 72 Internally and
Externally
Badalament
et al. [20]
Organ-confined
disease
Clinical stage, PSA, ratio of positive cores,
percentage of positive cores 192 86 NA
Ohori
et al. [21]
Side specific
extracapsular
extension
PSA, Clinical stage, Biopsy Gleason sum (side
specific), percentage positive cores (side specific),
percentage of cancer in cores (side specific)
763 81
Externally
Steuber
et al. [22]
Side specific
extracapsular
extension
PSA, Clinical stage, Biopsy Gleason sum, percentage
positive cores, percentage of cancer in positive cores 1118 84 Internally
Satake
et al. [23]
Side specific
Extracapsular
extension
PSA, Clinical stage, biopsy Gleason sum,maximum
precent of cancer on each side 354 79.9 Internally
Koh
et al. [24]
Seminal vesical
invasion
PSA, Clinical stage, primary ve secondary Gleason
sum, percentage of cancer at base 763 88 Internally
Baccala
et al. [25]
Seminal vesical
invasion Age, PSA, Biopsy Gleason sum, Clinical stage 6740 80 Internally
Gallina
et al. [26]
Seminal vesical
invasion
PSA, Clinical stage, Biopsy Gleason sum, percentage
positive cores 896 79
Internally ve
Externally
Ohori
et al. [27]
Seminal vesical
invasion
PSA, Clinical Stage, Biopsy Gleason score, presence
of cancer at base 466 87 Internally
1) PSA, Clinical stage, Biopsy Gleason sum 76 Internally
Cagiannos
et al. [28]
Lymph node
involvement
(limited) 2) PSA, Clinical stage, Biopsy Gleason sum,
institution
5510
78 Internally
1) PSA, Clinical stage, Biopsy Gleason sum 602 76 Internally
2) PSA, Clinical stage, Biopsy Gleason sum, number
of lymph node 781 79 Internally
Briganti
et al. [29]
Lymph node
involvement
(extended)
3) PSA, Clinical stage, Biopsy Gleason score,
percentage positive cores 278 83 Internally
Choi et al. [30] Pelvic lymph node
involvement
Age, PSA, biopsy Gleason sum, positive cores ratio,
maximum percent of tumor in any core 945 79.9 Internally
Chun et al.
[31] Gleason upgrade PSA, Clinical stage, primary ve secondary Gleason
sum 2982 80 Internally
Chun et al.
[32]
Clinically
significant
Gleason upgrade
PSA, Clinical stage, Biopsy Gleason sum 4789 76 Internally
Stackhouse
et al. [33] Gleason upgrade
Age, biopsy Gleason sum, PSA, prostate weight,
positive-to-total core ratio, maximum percent of
cancer in cores
1701 72.4 Internally
Steuber
et al. [34]
Tumor Location
(TZ vs PZ)
PSA, Biopsy Gleason sum, positive core ratio at
midprostate only, number of positive cores at base,
cumulative percentage biopsy tumor volume
945 77 Internally
Copyright © 2011 SciRes. OJU
S. ESKICORAPCI ET AL.
Copyright © 2011 SciRes. OJU
40
Peller
et al. [35] Tumor volume Biopsy Gleason sum, number positive sextant cores
and PSA 102 Non-specified NA
1) PSA, primary ve secondary Gleason sum 64 Internally
2) PSA, TRUS volume, primary ve secondary
Gleason sum, percentage positive core 74 Internally
Kattan
et al. [36]
Clinically indolent
cancer (tumor
volume < 0.5 cm3,
organ confined
and gleason grad
e < 4)
3) PSA, Clinical stage, TRUS volume, primary ve
secondary Gleason sum, milimeter of the positive
core, milimeter of the negative core
409
79 Internally
country as same. Validation of the Partin nomograms has
been conducted by Eskiçorapçı et al. with the participa-
tion of 1043 patients from 13 different centers in Turkish
population [37]. In conclusion, the urologists should be
keeping in mind that the Partin tables are only beneficial
for predicting the pathological stage but not prognosis or
biochemical recurrences.
2.1.2. Nomograms to Predict the Organ Confined
Diseases and Extracapsular Involvement
Badalament et al. have developed a formula which cal-
culates the probability for organ confined disease by us-
ing Gleason grade, nuclear grade, PSA and tumor in-
volvement rates [20]. Later, the models which was cal-
culating the probability of extracapsular involvement by
using the Gleason grade, age, PSA and tumor involve-
ment rates have been established and some of them were
validated [38]. These models are not widely used be-
cause of complexity of the parameters (nuclear grade,
total tumor involvement rate etc.). Partin tables may pre-
dict the extracapsular involvement but it fails to locate
the effected side. Therefore, Ohori et al. and Steuber et
al. developed the specific prediction nomograms for side
specific extracapsular involvement [21,22].
2.1.3. Nomograms for the Prediction of SV Invasion
and Lymph Node Involvement
The prediction of seminal vesicle and lymph node in-
volvement is very important because these patients have
generally worse prognosis and the success rate of radical
surgery or radiotherapy is very low. Predictions for these
patients have advantages in order to select the proper
adjuvant treatment. Many researchers have been devel-
oped the models to predict the seminal vesicle and lymph
node involvement [24-26]. However, these models could
not take place in clinical practice due to diagnosing the
diseases at earlier stages and founding more comprehen-
sive prediction models like Partin tables. On the other
hand, Ohori et al. recently developed a nomogram which
predicts seminal involvement by using PSA, clinical
stage, Gleason sum and cancer at the base. It was stated
the accuracy of the nomogram was 87% [27]. The results
of this nomogram are promising.
Eventually, Cagiannos et al. developed a prediction
model for limited lymph node dissection and Briganti et
al. developed for extended lymph node dissection. These
nomograms may be helpful to make a decision for se-
lecting the patients who need lymphadenectomy [28,29].
2.1.4. Nomograms to Predict the Surgical Margin
Status
The positivity of surgical margin is an important prog-
nostic parameter for the prediction of PSA relapse after
RP. However, none of the nomograms predicting the
surgical margin has been validated to date and they are
not widely used [39,40].
2.1.5. Nomograms to Predict the Gleason Score
Upgrade in RP
Gleason grade of RP is generally higher than the biopsy
Gleason grade. D’Amico et al. developed a nomogram to
predict the Gleason score upgrade and has recently been
validated [7,31]. In addition, Chun et al. have developed
a model to predict the high increases in Gleason grade
with their nomograms and was internally validated [32].
Stackhouse et al. conducted a nomogram by using age,
PSA, prostate volume, biopsy Gleason sum, ratio of pos-
itive biopsy core and maximum percentage of cancer in
cores. The accuracy of nomogram was 72.4% [33]. Ca-
pitonio et al. developed their nomograms by using PSA,
clinical stage, primary and secondary Gleason score in
biopsy. The concordance index (c-index) was calculated
as 74.89% [41]. These nomograms may be used espe-
cially for cryotheraphy, HIFU (high intensity focused
ultrasonography) and active surveillance.
2.1.6. Nomograms to Predict the Location of the
Tumor (Peripheral Zone and Transitional
Zone) and Tumor Volume
The fact that organ confined disease rate of the transi-
tional zone prostate cancer is higher despite the high
PSA levels. Steuber et al. have developed a nomogram
for predicting the transitional zone prostate cancer which
c-index was 77% [34]. Peller et al. have developed ano-
ther nomogram to predict the tumor volume in the pros-
tate. However, this nomogram could not be widely used
in view of including small number of patients and data of
sextant biopsy [35].
S. ESKICORAPCI ET AL.
41
2.1.7. Nomograms to Predict Clinically Insignificant
Cancers
The most of prostate cancers are clinically insignificant.
Besides the nomograms predicting the clinically insig-
nificant prostate cancers, the three nomograms were de-
veloped by Kattan et al. is widely used. The nomograms
are based on the criteria of Epstein et al. [36]. These
nomograms may be useful for the elderly patients with
high co-morbidity which require especially conservative
approach.
2.2. Nomograms to Predict Biochemical
Recurrence, Disease F ree and General
Survival after RP (Table 3)
2.2.1. Nomograms with the Data Prior to RP
After the Partin tables which were widely accepted for
predicting the pathological stage, nomograms have been
developed for prediction of survival which is the primary
end point for prostate cancer. The most frequently used
nomogram is the pre-operative Kattan nomogram, was
firstly developed in 1998 [4]. The Kattan nomogram
represents 5 years biochemical recurrence free survival
rates by constituting PSA, clinical stage, Gleason grade.
Kattan nomogram seems to have some advantages such
as easy to apply, predicts progression free survival and
defines the requirement of adjuvant treatments. The ac-
curacy of the nomogram is increased by adding İn- ter-
lökin-6 soluble receptor and transforming growth factor
beta-1 [42]. In 2006, a new version of Kattan nomogram
which predicts 10-years survival was published [43]. In
our country, the validation of Kattan nomogram is re-
cently accomplished by Eskicorapci et al. In this study
the two pre-operative Kattan nomograms (developed in
1998 and 2006) validated and c-index was found as 68%
and 70%, respectively [44].
The prediction model was developed by D’Amico et
al. is similar to the Kattan nomogram [45]. This model
predicts 10-years biochemical recurrences free survival
with pre-operative PSA, Gleason grade and tumor stage.
The patients are divided into three groups:
1) Low risk: Stage T1c - T2a, PSA 10 ng/mL and
Gleason grade 6 (10 years progression free survival is
83%)
2) Medium risk: Phase T2b, PSA > 10 ng/mL ve <
20 ng/mL veya Gleason grade = 7 (10 years progression
free survival is 46%)
3) High risk: Phase T2c, PSA 20 ng/mL veya Glea-
son grade 8) (10 years progression free survival is 29%)
Both nomograms (D’Amico and Kattan) predict the
PSA progression but not mortality. The life survival of
most patients is high despite PSA recurrence. These no-
mograms are useful to identify requirement of adjuvant
treatment, predict disease free survival and select the
patients for clinical trial.
2.2.2. Nomograms with the Data after RP
In 1999, Kattan et al. developed a nomogram to predict 5
years survival by comprising PSA, Gleason grade, cap-
sular invasion, surgical margin status, seminal vesicle
invasion and lymph node involvement. (56) These no-
mograms have been validated and widely used [46].
Stephenson et al. represents a new nomogram to predict
10 years progression free survival with additional pa-
rameters [47]. Moul et al. drew up tables predicting
3-5-7 year’s survival without recurrence in 2001 [48].
This table is involved with PSA, race, Gleason score of
RP and pathological stage. The other researchers namely
Han et al., Bauer et al., Blute et al. and D’Amico et al.
conducted similar models [49-52].
Recently, Morieira et al. designed a study to determine
whether the Postoperative nomograms are affected by
race with comparison of 7 nomograms. They stated all
nomograms have similar performance regardless of their
racial characteristics [53]. In addition, a study conducted
to determine the effects of lowered PSA at diagnosis
with rising PSA screening over the years leads to the
clinical stage migration. They found it does not reduce
Postoperative Kattan nomogram prediction accuracy [54].
Furthermore, the nomograms were developed for pre-
dicting early (2 years) and aggressive (9 - 12 months)
recurrence after radical prostatectomy. Walz et al. set up
a nomogram to predict early recurrence with 6 parame-
ters and c-index was found as 82% [55]. Schroeck et al.
developed a nomogram with 8 variables for predicting
aggressive biochemical recurrence and compared with
nine nomograms. They stated their nomogram is superior
for determining aggressive recurrence [56]. Afterwards,
they recalibrated and externally validated their nomo-
gram [57].
2.2.3. Nom o grams to Predic t Prostate Cancer Specific
Mortality af ter RP
Prostate cancer related mortality after RP is another im-
portant issue for prediction models. Stephenson et al. set
up a nomogram to predict 15 years survival and c-index
was found as 82% [58]. Indeed, Porter et al. developed a
nomogram with constituting age, pathological stage, pa-
thological Gleason sum, performing lymph node dissec-
tion and adjuvant radiotherapy data to determine 20-year
disease-free survival after RP and c-index was 76.3%
[59].
2.2.4. Nomograms to Predict the Quality of Life after
RP
A
lthough cancer specific survival has always been the
Copyright © 2011 SciRes. OJU
S. ESKICORAPCI ET AL.
Copyright © 2011 SciRes. OJU
42
Table 3. Nomograms to predict biochemical recurrence, disease free and general survival after RP.
Referrences Pre vs
Postoperative Variables Number
of Patients
Biochemical
recurrneces
(year)
Accuracy(%) Validation
D’Amico
et al. [51] Preoperative PSA, Clinical stage, biopsy Gleason
sum, percentage positive cores 823 4 80
Internally and
Externally
Kattan et al.
[4] Preoperative PSA, Clinical stage, primary ve
secondary biopsy Gleason grade 983 5 74
Internally and
Externally
Stephenson
et al. [43] Preoperative
PSA, Clinical stage, biopsy Gleason
sum, year of surgery, number of
positive ve negative cores
1978 and
1545 10 76 and 79 Internally and
Externally
Cooperberg
et al. [60] Preoperative
Age, DRE, number of previous negative
biopsy, history of HGPIN ve ASAP,
PSA, PSA velocity, family history, time
to first biopsy, time to previous biopsy
1439 3 and 5 66 Internally and
Externally
Graefen et al.
[61] Postoperative Pathologic stage, volume Gleason
grade 4/5, 2393 3,5 76 NA
McAleer
et al. [62] Postoperative Gleason grade, stage, surgical margin,
PSA 2417 7 Non-specified Internally
Kattan et al.
[5] Postoperative
PSA, Gleason sum, extracapsular
extension, seminal vesical invasion,
lymph node invasion, surgical margin
status
996 5 88
Internally and
Externally
Stephenson
et al. [47] Postoperative
PSA, Gleason sum, extracapsular
extension, seminal vesical invasion,
lymph node invasion, surgical magrin
status
1881,
1782 and
1357
10 78 - 86 Internally and
Externally
Stephenson
et al. [63] Postoperative
Age, PSA, pathological Gleason score,
pathological stage, year of surgery,
surgical margin status
7160 7 85 Internally
Walz et al.
[55]
Postoperative
Early Recurrence
(<2 years)
Age, PSA, pathological Gleason sum,
surgical magrin, extracapsular
extension, seminal vesical invasion
and lymph node invasion
2911 Non-specifie
d 82 Internally and
Externally
Schroeck
et al. [57]
Postoperative
Agressive
Recurrence
(<9 months)
PSA, surgical margin status, seminal
vesical invasion, extracapsular extention,
Gleason score, prostate weight, African
American, year of surgery
2599 5 83
Internally,
Externally and
Recalibrated
first end point, quality of life should also have an impor-
tant place after curative treatments. The study which in-
cludes Cancer of the Prostate Strategic Urologic Re-
search Endeavor (CaPSURE) data was produced to pre-
dict continence, erection status with physical and mental
outcomes in the first year after RP [64]. This nomogram
predicts characteristics of the preoperative tumors (clini-
cal stage, PSA and Gleason grade) as well as the quality
of life prior to surgery. Meanwhile, age and income sta-
tus as well as co-morbidity were observed independent
prognostic factors for prediction of the life quality. In
addition, the good physical conditions without co-mor-
bidity and healthy moods may induce rapid recovery to
the pre-operative condition.
3. What Are the Limitations of Nomograms?
Most of the series constituted the nomograms with
pre-operative parameters are developed by retrospective
RP data. However, the prediction accuracy of nomo-
grams may be affected by altering the population char-
acteristics over the years. In PSA era, newly diagnosed
prostate cancer patients have better stage and grade than
before. Therefore, the nomograms should be updated and
validated periodically. On the other hand, benefits from
diagnosis and treatment of prostate cancer are not ho-
mogenous when considering the long clinical course and
low mortality. Determining the weight of prognostic
factors on prostate cancer outcomes should be defined
S. ESKICORAPCI ET AL.
43
individually and in prediction model at the same time.
For this purpose, Kattan nomograms and Albertsen ta-
bles are widely used [65,66].
To date, any model has perfect prediction performance.
Additionally, some risk factors affecting the prognosis
are not included in several nomograms. However, the
models cannot achieve 100% accuracy even if all factors
add into the nomograms. To increase the accuracy of
nomograms, new biomarkers and imaging techniques
have been investigated [42,67].
4. Conclusions
Predicting the clinical course of cancer is challenging for
all patients. The urologists are willing to predict the pa-
thological stages and possible scenarios after curative
interventions. Therefore, the prognostic factors and no-
mograms are the frequently applied sources. PSA, Glea-
son grade and clinical stage are considered to be the most
important prognostic factors. In addition, the pathologi-
cal parameters are remarkable prognostic criteria. The
Partin tables for predicting the radical prostatectomy
pathology and Kattan nomograms for predicting the bio-
chemical recurrences free survival rates are the most
frequently used nomograms. Today, these nomograms
should not replace the clinical decisions but they give
significant information for the patients’ prognosis, treat-
ment selection and follow up.
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