UROLOGY - ORIGINAL PAPER

Scoring system for prediction of lymph node metastasis in radicalcystectomy cohort

Miroslav M. Stojadinovic • Rade Prelevic •

Arso Vukicevic

Received: 12 November 2013 / Accepted: 11 January 2014

� Springer Science+Business Media Dordrecht 2014

Abstract

Objectives The objective of the study was to assess

whether pretreatment clinical parameters combined with

computed tomography can improve the prediction of

lymph node metastasis in patients with bladder cancer

treated with radical cystectomy.

Patients and methods In a single-center retrospective

study, demographic and clinicopathological information

(initial transurethral resection [grade, stage, multiplicity of

tumors, lymphovascular invasion], hydronephrosis,

abdominal and pelvic computed tomography) and the

presence of lymph node disease on final pathology of 183

patients with bladder cancer undergoing radical cystectomy

and pelvic lymph node dissection were reviewed. Logistic

regression and bootstrap methods were used to create an

integer score for estimating the risk of positive lymph

nodes. Various measures for predictive ability and clinical

utility were determined.

Results On pathological examination, 59.6 % of patients

had positive lymph nodes. In a multivariable analysis,

status lymph nodes on computed tomography and hydro-

nephrosis were the most strongly associated predictors. The

resultant total possible score ranged from 0 to 10, with a

cut-off value of [4 points. The area under the receiver

operating characteristic curve was 0.806. Relative inte-

grated discrimination improvement was 14.3 %. In the

decision curve analysis, the model provided net benefit

throughout the entire range of threshold probabilities.

However, the final model was roughly equivalent to using

the clinical exam.

Conclusions The pre-cystectomy scoring system

improved the prediction of lymph node status in patients

with bladder cancer. Our model represented a user-friendly

staging aid, but a large multi-center study should be per-

formed before widespread implementation.

Keywords Bladder cancer � Lymph nodes metastasis �Prognostic model � Scoring system

Abbreviations

AUC Area under the receiver operating characteristic

curve

BC Bladder cancer

CI Confidential interval

CT Computed tomography

ePLND Extended pelvic LND

IDI Integrated discrimination improvement

IP The integrated 1-specificity

IS The integrated sensitivity

LN Lymph node

LND Lymph node dissection

LVI Lymphovascular invasion

MRI Magnetic resonance imaging

MI Muscle-invasive

NC Neoadjuvant chemotherapy

NPV Negative predictive value

NRI Net reclassification improvement

ORs Odds ratios

M. M. Stojadinovic (&)

Department of Urology, Clinic of Urology and Nephrology,

Clinical Centre ‘‘Kragujevac’’, Zmaj Jovina 30,

34000 Kragujevac, Serbia

e-mail: midinac@EUnet.rs

R. Prelevic

Clinic of Urology, Military Medical Academy,

Belgrade, Serbia

A. Vukicevic

Faculty of Engineering, University of Kragujevac,

Kragujevac, Serbia

123

Int Urol Nephrol

DOI 10.1007/s11255-014-0645-x

pN? Lymph node metastases

ROC The receiver operating characteristic curve

PPV Positive predictive value

TUR Transurethral resection

TURBT Transurethral resection of bladder tumor

Introduction

Bladder cancer (BC) represents the fourth most common

cancer in men and the eighth most common malignancy in

women worldwide. About 30 % of patients present with a

muscle-invasive (MI) disease at the time of diagnosis [1].

MIBC spread from the bladder in a predictable stepwise

manner to the lymph nodes (LNs) and then to visceral

organs.

Identifying nodal disease in BC patients is critical

because it has profound implications for treatment plan-

ning [2]. The incidence of positive LNs in radical cys-

tectomy (RC) specimens is between 18 and 30 % [3].

Today, pretreatment assessment of lymph node status is

mainly based on imaging techniques like computed

tomography (CT) and magnetic resonance imaging (MRI)

with contrast enhancement, both of which are recom-

mended by current guidelines [1]. The major shortcoming

of this standard routine is the lack of accuracy and the

evident discrepancy between clinical and pathologic

staging. The assessment of metastases to LNs based

simply on size is limited by the inability of both CT and

MRI to identify metastases in normal-sized or minimally

enlarged nodes. The sensitivity for detection of LN

metastases is low, ranging from 48 to 87 %. Specificity is

also low as nodal enlargement may be due to benign

disease [4]. To overcome these deficiencies, several new

techniques are currently being studied. Although encour-

aging initial reports on new techniques, it will take time

to validate the results before these new approaches can be

integrated into clinical routine [1].

Consequently, clinical prediction has evolved from

physician judgment alone and novel imaging modalities to

risk group stratification, prediction models based on mul-

tivariate regression or principal component analysis, to

nomograms and decision tree model [5–10]. Several recent

studies have demonstrated that multivariate models are

more accurate than most informative single predictors [5–

10].

Based on these considerations, the objective of the study

was to assess whether pretreatment clinical parameters in

combination with CT expressed in scoring system could

improve the prediction of LN status in patients with BC

treated with RC and lymph node dissection (LND).

Patients and methods

Patient population

We retrospectively reviewed the medical records of 248

patients who had undergone radical surgery for BC at

Military Medical Academy, from January 2002 through

December 2012. For each patient, comprehensive clinical

and pathologic information were collected as regards pre-

cystectomy assessment. Patients underwent a routine cys-

toscopic and upper tract evaluation, physical examination,

transurethral resection (TUR) of bladder tumor (TURBT)

or biopsy, abdominal and pelvic CT and chest radiography.

No patient received radiotherapy or neoadjuvant chemo-

therapy (NC) before radical cystectomy. Patients with non-

urothelial BC, or incomplete data, or distant metastatic

disease at the time of cystectomy were excluded. The study

included only patients with at least 9 lymph nodes removed

as suggested previously [11] or less, but they were positive.

Thus, 183 patients were included in the present analyses.

Primary RC or secondary RC (after failure of conser-

vative management of superficial BC) has been defined as

described previously [12]. Tumor size and number were

defined according to preoperative radiological examina-

tions. Evaluation for the presence of hydronephrosis, if

any, was performed in all patients and defined as described

previously [13]. Data about TUR stage and grade were

collected and classified according to the seventh edition

TNM classification system and the 1973 WHO grading

system, respectively, as all other surgical specimens [14,

15]. Lymphovascular invasion (LVI) in TURBT or biopsy

specimens was defined as the unequivocal presence of

tumor cells within an endothelium-lined space, with no

underlying muscular walls [16]. The time period between

TURBT and RC was measured as the number of months

from date of TURBT to date of RC. Status of lymph nodes

was evaluated on the basis of size, texture, morphology and

intensity of the signal. Status LNs on CT were categorized

into two main groups. The first group represents patients

with no LNs involvement or the presence of LNs with a

shot axis of\10 mm, ‘‘nonpathologic’’. The second group,

‘‘pathologic’’, was considered when a nodal enlargement

(C10–20 mm in the long axis) was depicted [17].

All patients underwent radical cystectomy, pelvic lym-

phadenectomy and urinary diversion. Bilateral lymphade-

nectomy was performed at least in the obturator fossa and

along the internal and external iliac artery. The indications

for radical cystectomy were tumor invasion into the mus-

cularis propria or prostatic stroma or Ta, T1, or carcinoma

in situ refractory to TUR with intravesical chemotherapy

and/or immunotherapy. The extent of LND was at the

surgeon’s discretion, and extended pelvic LND (ePLND)

was not routinely performed.

Int Urol Nephrol

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Outcome variable

The presence of lymph node metastases (pN?) in surgical

specimen after pathological review was the primary inter-

est of statistical analysis.

Statistical analyses

Univariate and multivariate regression was used to identify

and quantify the independent predictors of pN?. The

results of regressions were expressed in odds ratios (ORs)

with 95 % confidential interval (CIs). To examine the

stability of the model’s effect estimates and check for over-

fitting, we used the bootstrap method to generate 1,000

samples. The medians of the resultant beta coefficients for

each variable were then reported and used to develop an

integer-based weighted point system. Then the resultant

beta coefficients were multiplied by 15 and rounded off to

the nearest integer. Individual scores were assigned to each

patient-discharge record by summing the individual risk

factor points. The cut-off points for predicting pN? were

identified. Sensitivity, specificity, positive predictive value

(PPV), negative predictive value (NPV) and accuracy for

scoring systems were determined. Different aspect of per-

formance and utility risk prediction model was assessed

using the Hosmer–Lemeshow statistic, calibration and

Nagelkerke’s R2 for model fit, the area under the receiver

operating characteristic (ROC) curve (AUC) and integrated

discrimination improvement (IDI) for predictive ability,

and decision curve analyses for clinical utility [18–20].

Assumption was made that the identification of pN? would

lead to treatment with NC. The interpretation of a decision

curve was that the model with the highest net benefit at a

particular threshold probability should be chosen.

Statistical significance was set at p \ 0.05. All analyses

were performed using SPSS version 10.0 (SPSS Inc.,

Chicago, IL), MATLAB 2010a software (MathWorks,

Natick, MA), and R-statistics (the R foundation for Sta-

tistical Computing, version 2.15.1).

Results

Patients’ characteristics

The mean ± standard deviation (range) patient age at the

time of cystectomy was 63.4 ± 9.0 (42–86) years and 166

(90.7 %) patients were male. Of all the patients, 77

(42.1 %) presented with no, 61 (33.3 %) unilateral and 45

(24.6 %) bilateral hydronephrosis. TUR was performed in

149 (81.4 %) patients, median (IQR; range) 2 (1; 1–12)

months before RC. The pathological staging after RC of

the entire cohort was distributed as follows: 9 (4.9 %)

patients had T1, 52 (28.4 %) had T2, 73 (39.9 %) had T3,

and 49 (26.8 %) had T4 disease. On CT 76 (41.5 %) of

patients had LN status defined as group one, 107 (58.5 %)

had status defined as group two or ‘‘pathologic’’. The

median (IQR; range) number of removed LNs was 14 (8;

2–25). Overall, 109 (59.6 %) patients had LN metastases,

median 3 (IQR; range) (3; 1–11). Pearson’s r for rela-

tionship between pathological stages with lymph node

metastases was 0.565, p = 0.000. After histological eval-

uation, the absence of tumor involvement was correctly

diagnosed in 53 of 76 patients (69.7 %) in group one, and

in 21 of 107 ones (19.6 %) in group with ‘‘pathologic’’ LNs

by CT (Pearson v2 = 46.329, p = 0.000). The clinico-

pathological characteristics of the patient cohorts (pN- or

pN?) are shown in Table 1.

Table 1 Baseline patients’ clinicopathological characteristics in lymph node-negative and positive bladder cancer (n = 183)

Characteristics LN negative LN positive p value

Age, years 62.7 ± 10.1 63.9 ± 8.2 0.364

Gender, female/male [n (%)] 6/68 (8.1/91.9) 11/98 (10.1/89.9) 0.797

Primary/secondary [n (%)] 41/33 (55.4/44.6) 58/51 (53.2/46.8) 0.880

Size of tumors (cm)a 3.3 5.3 0.000

Number of tumors, 1, 2 or C3 [n (%)] 13/12/49 (17.6/16.2/66.2) 15/16/78 (13.8/14.7/68.5) 0.717

Initial tumor grade 2 or 3 [n (%)] 14/60 (18.9/81.1) 8/101 (7.3/92.7) 0.017

Initial tumor TUR stage Ta/T1/T2 [n (%)] 3/23/48 (4.1/31.1/64.9) 3/16/90 (2.8/14.7/82.6) 0.022

Lymphovascular invasion no/yes [n (%)] 29/45 (39.2/60.8) 22/87 (20.2/79.2) 0.004

Hydronephrosis no/unilateral/bill [n (%)] 45/25/4 (60.8/33.8/5.4) 32/36/41 (29.4/33/37.6) 0.000

Status LN on CT [n (%)] 53/21 (71.6/28.4) 23/86 (21.1/78.9) 0.000

Pathological stage Ta/T1, T2, T3, T4 [n (%)] 9/41/17/7 (12.2/55.4/23/90.5) 0/11/56/42 (0/10.1/51.4/38.5) 0.000

LN lymph node, CT computed tomographya Median, interquartile range

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Prediction and scoring system for pN?

In a univariate analysis, 6 risk factors displayed significant

correlation with pN? (Table 2). During multivariable

analysis, two sustained their prognostic significance

(Table 2). The analysis demonstrated the status of LNs on

CT and hydronephrosis have strong prognostic value of

pN? (Table 2). All variables maintained significance in the

bootstrap model (Table 2).

Next, a total score was calculated by summing the points

from each variable for each patient. The resultant total

possible score ranged from 0 to 10, with a cut-off value of

[4 points. AUC for the scoring system was 0.806 (95 %

CI 0.743–0.870), showing the model to have good dis-

criminatory ability and significantly better (difference

between areas 0.0539, p = 0.0008) compared when model

include only prediction of status LNs on CT (AUC 95 %

CI 0.753, 0.678–0.827) (Fig. 1). The sensitivity and spec-

ificity of the scoring system in the data set were 0.826

(95 % CI 0.741–0.892) and 0.676 (95 % CI 0.557–0.780),

respectively. The calculated PPV, NPV and accuracy were

0.789 (95 % CI 0.703–0.860), 0.725 (95 % CI

0.604–0.825) and 0.765 (95 % CI 0.697–0.824), respec-

tively. Graphical assessments of score calibration are pre-

sented in Fig. 2. The scoring system was well calibrated

because of the Hosmer and Lemeshow goodness of fit test

statistic was v2 = 6.537, p = 0.163, thereby demonstrating

good fit. The Brier score for a model was 0.171. The

Nagelkerke’s R2 value in the model was 0.363.

IDI and summary statistics are shown in Table 3. Rel-

ative improvement with new model was 14.3 %. The risk

assessment plot is shown in Fig. 3. Only the IDIevents was

small with a 95 % CI that straddled zero. The model

overall similarly identifies those who do not have events

(the nonevents curve, integrated 1-specificity [IPnew] =

0.359) and those who do (the events curve, integrated

sensitivity [ISnew] = 0.657). The addition of hydrone-

phrosis to the model makes very little difference. Fur-

thermore, reference and new model curve partly overlaps.

The IS and IP have positive and negative components.

Positive between a risk of 0.30–0.58 and between 0.80 and

0.90, and negative from 0.23 to 0.30 and between 0.66 and

0.80. Nevertheless, the resulting net for IS and IP were

0.0146, p = 0.014, and 0.0221, p = 0.001, respectively.

In the decision curve analysis (Fig. 4), both model and

clinical exam predicting pN? provided net benefit

throughout the entire range of threshold probabilities above

30 % as compared to the strategy of treating all patients

with NC, or alternatively, treating no one. The graph shows

that the final model (dotted black line) is roughly equiva-

lent to using the clinical exam (dotted red line) alone

between 30 and 80 % threshold probabilities.

Discussion

Pretreatment identifying lymph node metastasis could

improve decision-making for NC that is associated with a

potential benefit for this group of patients, extended lym-

phadenectomy, or conservative management. In the current

study, we have taken a unique approach for prediction LNs

status before RC using clinicopathological features

obtained before radical surgery. Our scoring system was

able to achieve an accuracy of 76.5 %. The tool showed

satisfactory discrimination, calibration, and clinical use-

fulness in the internal validation.

To date, several clinicopathological factors are reportedly

associated with post-surgical pathological stage and were

included in the existing models such as TUR parameters of

stage and grade [5–7, 9, 10], LVI [5, 6, 8], hydronephrosis [6,

8–10], age [6, 7, 10], female gender [7, 21], CIS [7], histo-

logical variants [21], tumor size [9], tumor growth pattern

[10], multiplicity of tumors [8, 10], palpable mass [8], number

of intravesical treatments [8], NC [7], primary versus sec-

ondary RC [12], oncofetal markers [9], preoperative plasma

soluble E-cadherin [22], expression of vascular endothelial

growth factor-C [23], or gene expression model on primary

tumor tissue [24]. In line with previous studies, several of

those factors have reached statistical significance in the uni-

variate or multivariate analysis in our model. However, many

Table 2 The analysis of possible and independent predictors for lymph nodes metastasis in bladder cancer patients and point value

Factor Univariate analysis Multivariable analysis Bootstrap Point value

OR (95 % CI) p value OR (95 % CI) p value B B

Size of tumors 1.393 (1.176–1.651) 0.000

Initial tumor grade 2.946 (1.167–7.433) 0.022

Initial tumor stage 2.033 (1.134–3.644) 0.017

Lymphovascular invasion 2.548 (1.316–4.935) 0.006

Hydronephrosis 3.138 (2.006–4.909) 0.000 2.113 (1.298–3.440) 0.003 0.748 0.133 2

Status LN on CT 9.437 (4.764–18.691) 0.000 6.448 (3.131–13.281) 0.000 1.864 0.409 6

LN lymph node, CT computed tomography

Int Urol Nephrol

123

of these parameters did not retain their independent predictive

value, even those with previously proved significance [25].

Small sample size in our study could, at least partially, explain

the observation. Nevertheless, we found that unilateral and

bilateral hydronephrosis was a strong independent predictor of

pN?. These findings support those of previous investigators

such as Stimson et al. [26], who reported that preoperative

hydronephrosis was independently associated with extrave-

sical and node-positive disease at the time of cystectomy,

directly affecting cancer-specific survival and predicted the

side of nodal involvement.

In our study,[59 % of patients harbored LN metastasis

because in our cohort, locally advanced BC dominated

(66.7 %), contrary to other study were organ-confined BC

dominated. In accordance with previous results, the pro-

portion of having a positive LNs increased proportionally

with advancing pathologic T stage [25]. Our study support

findings that currently available standard techniques based

simply on size is still not accurate enough to predict

pathologic node-negative disease in patients with MIBC

[1], because small lymph nodes can contain tumor (about

30 % in our study) and enlarged nodes may be reactive but

may not contain tumor (about a fifth in our study). More-

over, metastases from bladder and other pelvic cancers

frequently cause very little nodal enlargement, and the

round shape of the LNs are more likely to be metastatic

than are ovoid nodes [2].

In 2006, Karakiewicz published the first nomograms for

predicting pT3-4 and pN? disease in BC [7]. Their model

indicated an accuracy of 63.1 % for pN? disease. How-

ever, a recent validation study in European patients dem-

onstrated a notable decrease in model performance (the

AUC was 54.5 % for pN? disease) which remains similar

after refitting the model (64.7 % for pN? disease) [27].

Our scoring system resulted in an AUC of 80.6 %, which is

statistically better than the model that only included vari-

ables proposed by Karakiewicz’s report and similar other

reports (67–85 %) [5, 6, 8, 9, 24].

Integration of risk factors into decision tools is neces-

sary to provide patients and physicians with individualized

need to implement NC. However, in bladder cancer, there

are many unresolved issues regarding the NC, and thus, the

threshold probability of clinical application remains an

open question. First of all, NC prior to cystectomy has not

been widely adopted, since most patients do not benefit,

Fig. 1 ROC curves analysis of scoring system and status lymph

nodes on computed tomography for predicted lymph node-positive

bladder cancer

Fig. 2 Observed versus predicted probability for lymph node-

positive bladder cancer by score

Table 3 IDI and summary

statistics

IDI the integrated

discrimination improvement, IS

the integrated sensitivity, IP the

integrated 1-specificity, events

lymph nodes positive, nonevents

lymph nodes negative, ref

reference model, new new

model

Metrics of IDI Mean (95 % CI) p value

IDIevents 0.0146 (-0.0044 to 0.0337) 0.131

IDInonevents 0.0216 (0.0003 to 0.0428) 0.047

IDI Absolute 0.0367 (0.0238 to 0.0496) 0.000

ISref (reference) 0.6971 ((0.6657 to 0.7285) \0.0001

ISnew (reference ? hydronephrosis) 0.7117 (0.6848 to 0.7385) \0.0001

IS 0.0146 (0.0030 to 0.0261) 0.014

IPref (reference) 0.445 (0.4115 to 0.4785) \0.0001

IPnew (reference ? hydronephrosis) 0.4229 (0.392 to 0.4537) \0.0001

IP 0.0221 (0.0088 to 0.0355) 0.001

IDI relative 14.3 %

Int Urol Nephrol

123

and we are currently unable to predict those that do. Then,

there is a concern for delay of surgery and risk of disease

progression [26]. Furthermore, a strict statistical correla-

tion between clinical tumor stage and effectiveness of NC

was not described. Available studies suggest a much more

substantial benefit for patients with high-risk disease (cT3,

cT4a) and/or lymph node-positive disease (cN1) [28]. One

simple way to test the clinical consequences of using pre-

diction tools is to use decision curve analyses for clinical

utility. Based on these considerations, we considered the

relevant range of threshold probabilities to be about

20–50 %. In this range, decision curve analysis of our

model including both status LNs on CT and hydronephrosis

offers the highest net benefit both for clinicians hesitant to

use NC and for those who liberally recommend for its use.

However, it remains an open question selection of those

most likely to benefit from NC, while avoiding overtreat-

ment and delay.

The current study has several limitations worth noting.

First, enrolled patients were retrospectively collected in a

single tertiary center with a relatively small patient cohort

who may influence the results by the selection bias. Sec-

ond, pN? is a useful diagnostic marker but predicting

disease outcome, surveillance or response to therapy is

more clinically significant. A third limitation of the study is

the exclusion of other possible risk factors for advanced

disease, such as biomarkers [9], bimanual palpations (BP)

[29]. These data were not available in our cohort. In our

study, the extent of LND was at the surgeon’s discretion,

and ePLND was not routinely performed. Therefore, we

used the threshold of 9 LNs removed, as the minimum

number required during LND [11]. Furthermore, it was

found that there was a weak correlation between the

number of retrieved nodes and number of positive nodes

[25, 30]. However, the number of LNs removed is not only

a factor of the extent of LND, but is also dependent on the

pathological evaluation, inherent differences among

patients, the location of LNs from an area with a high

likelihood of malignancy [11], recently established clinical

nodal staging score [31], and the pathological nodal staging

score [32]. We used the scoring system approach as we are

very familiar with it in both routine work and in the

research. However, other prognostic tools are also possible

(e.g., nomograms) according to the individual preferences

and study goals. Furthermore, our models are not appli-

cable to patients who were pretreated with radiotherapy or

to those harboring pathologies other than transitional cell

carcinoma. Finally, we used a bootstrap method internal

validation and did not use an external cohort to validate our

scoring system. Nevertheless, to our knowledge, this is the

first study that prognostic model expressed in scoring

system, a user-friendly staging aid, to predict pathological

node status before surgery. The prediction model repre-

sents another step toward accurately estimating individu-

alized risk of LNs metastases in a patient population

lacking optimal staging procedures.

Conclusions

We developed a scoring system to predict the pathological

node status in BC patients treated with RC by evaluating

Fig. 3 The risk assessment plot. The reference risk models (dashed

lines) and new risk models (solid lines) were obtained by addition of

hydronephrosis for prediction lymph node-positive bladder cancer.

Red lines represent 1-specificity versus the calculated risk for those

with the event; black lines are sensitivity versus the calculated risk for

those without events

Fig. 4 Decision curve analysis of the effect of prediction models on

the detection of lymph node-positive status. Net benefit is compared

with ‘‘NC for all’’ strategy and ‘‘NC for none’’. Model 1 is a final

model including status lymph nodes on CT and hydronephrosis

(dotted black line). Model 2 is a model including only status lymph

nodes on CT (dotted red line)

Int Urol Nephrol

123

the status LNs on CT and the presence of hydronephrosis.

The newly devised formula has an accuracy of 76.5 % and

was internally validated. Adoption of such a tool into daily

clinical decision-making may lead to more appropriate

integration of perioperative NC, thereby potentially

improving survival in patients with BC. The clinical value

of this model needs to be further assessed in external multi-

institutional validation cohorts.

Acknowledgments The authors were financially supported through

a research grant N0175014 of the Ministry of Science and Techno-

logical Development of Serbia. The authors thank the Ministry for

this support.

Conflict of interest None.

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