At the Cutting Edge
Neuroendocrinology 2010;92:143–157
DOI: 10.1159/000319784
Received: June 29, 2010
Accepted after revision: July 29, 2010
Published online: August 23, 2010
A Nomogram to Assess Small-Intestinal
Neuroendocrine Tumor (‘Carcinoid’)
Survival
Irvin M. Modlin a Bjorn I. Gustafsson a, b Marianne Pavel c Bernhard Svejda a
Benjamin Lawrence a Mark Kidd a
a
Gastrointestinal Pathobiology Research Group, Yale University School of Medicine, New Haven, Conn., USA;
Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology,
Trondheim, Norway; c Medizinische Klinik mit Schwerpunkt Hepatologie und Gastroenterologie, Charité,
Campus-Virchow-Klinikum, Universitätsmedizin Berlin, Berlin, Germany
Key Words
Carcinoid ⴢ Cox model ⴢ Hazard ratio ⴢ Kaplan-Meier
method ⴢ Modlin Score ⴢ National Cancer Institute ⴢ
Neuroendocrine tumor nomogram ⴢ Surveillance,
Epidemiology and End Results database ⴢ Small intestine ⴢ
Survival prediction
Abstract
Neuroendocrine tumors (NETs) are a heterogeneous group
of cancers of which the commonest site is the small intestine
(SI). Most information available to determine tumor behavior
reflects univariate assessment of factors or is anecdotal or
experience based. There currently exists no objective multivariate analysis of indices that defines SI NET prognosis. A
key unmet need is the lack of a rigorous mathematical-based
tool – a nomogram – for the assessment of parameters that
define progress, determine prognosis and can guide therapy. Since prediction of NET behavior is a critical criterion in
determining clinical strategy, we constructed a NET nomogram (Modlin Score) for prognosis prediction, patient group
comparisons and a guide for stratification of treatment and
surveillance. We used hazard ratio (HR), Cox analysis and Kaplan-Meier analysis of published data and the current Sur-
© 2010 S. Karger AG, Basel
0028–3835/10/0923–0143$26.00/0
Fax +41 61 306 12 34
E-Mail karger@karger.ch
www.karger.com
Accessible online at:
www.karger.com/nen
veillance, Epidemiology and End Results (SEER) database
(approx. 20,000 patients) to develop a nomogram from 15
variables demonstrated to provide significant multivariate
HRs. These included age, gender, ethnicity, symptoms, urinary 5-hydroxyindoleacetic acid, plasma chromogranin A,
liver function tests, tumor size, invasion, metastasis, histology, Ki-67 index, carcinoid heart disease and therapy (surgery or long-acting somatostatin analogs). Internal validation was assessed using 33 SI NET patients. A NET nomoscore
(Modlin Score) was developed by HR weighting and stratification into low (!75), medium (75–95) and high risk (195).
This identified significant differences (p ! 0.03, Kaplan-Meier) in survival (15.5 8 4.3, 9.7 8 2.5 and 6.4 8 1.1 years, respectively). The Modlin Score was significantly elevated (p !
0.01) in deceased compared to alive patients. This nomogram represents an optimized construct based upon currently analyzable data, and application will facilitate accurate stratification for comparison in clinical trials. External
validation and amplification by identification of additional
indices, e.g. molecular biomarkers, are necessary. The development of a mathematically validated nomogram provides
a platform for objective assessment of SI NET disease, a finite
basis for precise prognostication and a tool to guide management strategy.
Copyright © 2010 S. Karger AG, Basel
Irvin M. Modlin
Department of Gastroenterological Surgery, Yale University School of Medicine
333 Cedar Street, PO Box 208062
New Haven, CN 06520-8062 (USA)
Tel. +1 203 785 5429, Fax +1 203 737 4067, E-Mail imodlin @ optonline.net
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b
Neuroendocrine tumors (NETs) represent both a relatively rare neoplastic process as well as a diverse group of
tumors segregated within a group previously considered
as ‘carcinoids’ [1, 2]. The tumors occur in numerous sites
but are especially common within the gastrointestinal
tract and the bronchopulmonary system reflecting the
wide distribution of their neuroendocrine cells of origin
(e.g. enterochromaffin cells, gastrin cells, -cells) which
are sensory and regulatory in function [2, 3]. Relatively
little is known of the etiology of the disease, and each
NET, depending on its anatomical site, arises from a different neuroendocrine cell and exhibits a different functionality as well as significantly different biological and
malignant behavior [2, 4]. Thus, prediction of prognosis
and outcome of an individual NET is difficult and, in
many instances, inaccurate. This reflects the absence of
any organized assessment system to provide a mathematical basis for objective appraisal of disease stratification
and risk analysis. The development of a nomogram that
can be utilized to provide a prognostic model to predict
disease-specific death for patients with a NET remains a
major unmet need in the discipline of neuroendocrine
oncology.
Patients with a NET have an unpredictable survival
even when there is successful resection of the primary
tumor and/or its metastases, which is due to the biological heterogeneity of the tumors. Since these cancers have
differing genetic, cellular and behavioral characteristics,
their survival is not uniform. Patient prognosis is currently estimated on the basis of a number of different systems proposed by the American Joint Committee on
Cancer (AJCC), World Health Organization (WHO) and
European Neuroendocrine Society (ENETS) [5–9]. These
are variably based upon prognostic determinants such
as histological differentiation (well-differentiated NET/
neuroendocrine carcinoma and poorly differentiated
neuroendocrine carcinoma and biological/pathomorphological signs of malignancy, or more recently on the
TNM staging system) [2, 5–7, 9, 10]. By integrating additional significant prognostic factors, a nomogram can be
developed to not only better assess an individual patient’s
disease-specific survival, but also provide information
that may be helpful in defining treatment options or comparing treatment groups.
In the USA, the incidence (2003–2007) of the disease
based upon the 2007 National Cancer Institute’s (NCI)
Surveillance, Epidemiology and End Results (SEER) database was 5.76/100,000, and the prevalence in 2004 was
144
Neuroendocrinology 2010;92:143–157
estimated to be approximately 35/100,000 [11, 12]. The
incidence is equivalent to esophageal cancer (4.5/100,000),
testicular cancer (5.4/100,000) and myeloma (5.4/100,000).
The prevalence renders gastroenteropancreatic NETs
(GEP NETs) the second most common gastrointestinal
cancer after colon cancer, and more prevalent than pancreatic, gastric, esophageal or hepatic cancer or any two
of these combined [13].
Approximately 18,000 cases and 8,200 deaths attributable to this disease are predicted for 2011 in the USA
based on the NCI SEER data [11]. Given the wide range of
the 5-year survival rate of 41–87% depending on disease
extent, grade and tumor site [11], patients with a NET require an accurate prognosis. With accurate prediction,
patients at low risk for disease-specific death can be safely reassured, whereas patients at high risk can be considered for appropriate surgery and systemic therapy [2].
Several studies have identified prognostic factors in individual NETs but a rigorous and robust assimilation of the
different indices used to define outcome is lacking [12,
14–23]. Although assessment of a variety of parameters
and knowledge thereof has utility in clinicopathological
research and clinical trial design, patient-specific counseling and therapeutic strategy require formalized integration of diverse prognostic factors to establish a single
patient-specific prognosis. In addition, the ability of such
a tool, a nomogram, to generate individualized predictions, will facilitate the identification and stratification of
patients in clinical trials [24].
It is evident that a simplistic enumeration of the risk
factors of an individual is insufficient to objectively integrate or adequately weight the information available for
prediction. A standardized inventory of risk factors erroneously assumes that each factor has equal or equivalent weight, and if a continuous variable, such as patient
age at diagnosis, is categorized for counting, the information quotient may be diminished [25]. In order to best
construct a nomogram, the range of variables ideally considered should be determined based on data availability
and clinical evidence as well as on statistical significance
(using modeling programs, e.g. Cox proportional hazards model). Prognostic parameters therefore need to be
identified based on both research and clinical rationale,
and then weighted according to the size of the prognostic
effect ideally allowing for the differing variance in each
sample. As such, multivariate hazard ratios (HRs) can be
readily compared across separate studies and can translate directly into weightings for each prognostic variable.
The purpose of this study was to develop a prognostic
model, or nomogram, that predicts disease-specific death
Modlin /Gustafsson /Pavel /Svejda /
Lawrence /Kidd
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Introduction
Materials and Methods
Detection of variables that predict survival required HRs
which were obtained from the literature or calculated de novo
from the latest iteration of the NCI SEER database (2007).
Literature Assessment to Identify Appropriate Prognostic
Indices
A retrospective survey of the current status of risk (HRs) associated with gastrointestinal NETs (and specifically SI ‘carcinoids’ and NETs) was undertaken using the PubMed database
(available at URL: http://www.pubmed.gov, accessed June 6,
2010). Key words used in the literature search included carcinoid,
gastrointestinal, hazard ratio (HR), Kaplan-Meier, multivariate
analysis, neuroendocrine tumor, small intestine and survival (table 1). Research articles were evaluated for survival information,
particularly with reference to univariate (table 2) and multivariate
(table 3) HRs. All relevant articles (n = 19 articles, 12,412 patients)
were published from 1997 to 2010.
Analysis of SEER Database (1977–2007) for Indices
The patient population is derived from the NCI SEER database. A search of the database identified 24,850 patients with gastrointestinal NETs (stomach, small bowel, colon, rectum and appendix) between 1977 and 2007. Of these patients, 7,445 had SI
NETs (table 4). Patients with a diagnosis of Meckel’s diverticulum
carcinoids (n = 133) were included. Patients with other causes of
death were excluded. Clinicopathological factors were analyzed
to determine the effect on overall survival using the log rank test
(Mantel-Cox). Multivariate analysis was performed using the Cox
proportional hazards model (SPSS 16.0, IBM). Variables were
added to the multivariate model based on a stepwise forward
(Wald statistic model) selection procedure where the entry criterion for each variable was based on p ! 0.05. Variables evaluated
included gender, ethnicity (race: White, Black and other – including Hispanic, Latino and unknown), SEER stage or extent (local,
regional or distant disease) and degree of differentiation/grade.
The latter included well-differentiated (grade I), moderately differentiated (grade II), poorly differentiated (grade III), undifferentiated (anaplastic: grade IV) and unknown. Different classification systems are used in the USA and Europe so a combination of
both the WHO and TNM were examined. Survival curves were
generated from Kaplan-Meier analysis (fig. 1), and statistically
significant variables were included in table 4 and used to generate
the nomogram (fig. 2).
Nomogram to Assess Small-Intestinal
Neuroendocrine Tumor Survival
Table 1. Potentially assessable prognostic indices
Factor
Data
available
Patient characteristics
Age
yes
Gender
yes
yes
Ethnicity/race1
Symptoms
yes
Bioactive amine/peptide-related symptoms2
Biochemistry, urine
5-HIAA levels3
yes
Biochemistry (blood)
Elevated CgA blood levels4
yes
Liver function tests
Abnormal liver function tests5
yes
Tumor morphology, extent and immunohistochemistry
Histological grade
yes
CgA staining (‘well differentiated’)
yes
Tumor size
yes
Ki-67 index
yes
no
Mitoses6
SSTR2 expression
no
Disease topography
Local disease only
yes
Regional disease
(involving regional lymph nodes)
yes
Metastasis (liver/elsewhere)
yes
yes
Presence of carcinoid heart disease7
Therapeutic intervention
Surgery – with curative intent
yes
Surgery – primary resectable/residual disease
yes
Tumor debulking (hepatic/mesenteric)
yes
Response to systemic therapy
yes
Performance status
Karnofsky score
yes
5-HIAA = 5-Hydroxyindoleacetic acid; CgA = chromogranin
A; SSTR = somatostatin receptor.
1 Ethnicity/race as defined by NCI SEER database (White,
Black, other). 2 Symptoms of ‘carcinoid syndrome’. 3 Elevation
above normal range. 4 >6 times the upper limit of normal. 5 Elevation greater than the upper normal limit of bilirubin, alkaline
phosphatase or ␥-glutamyltransferase. 6 Mitoses per 10 high-powered field on histological examination. 7 Echocardiogram.
Development of the NET Nomogram
Statistically significant variables identified either in the literature review or from the multivariate analysis of the most recent
NCI SEER database are shown in table 5. Patient age, gender, ethnicity, presence of specific symptoms at diagnosis (flushing, diarrhea), elevated urinary 5-hydroxyindoleacetic acid (5-HIAA; 1300
mol/24 h), elevated plasma chromogranin A (1 6! upper limit of
normal), abnormal liver function studies, tumor size, SEER staging, histology (grade), Ki-67 index, the presence of carcinoid heart
disease, liver metastases (detected by any conventional modality,
Neuroendocrinology 2010;92:143–157
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for patients with NET disease of the small intestine (SI;
‘carcinoid’). We used information acquired from current
literature resources (12,412 patients) and 7,445 SI NET
patients identified within the 1977–2007 NCI SEER database to create multivariate HRs for each prognostic factor. Multiple statistical modeling strategies were compared for their ability to combine the established prognostic factors into a model that predicts accurately, and
this final model was internally validated.
Table 2. Literature compilation of univariate variables predictive of survival in SI NETs
Factor
HR
Patient characteristics
Age
>50 years
>64 years
>65 years
Ethnicity: Black
Gender: male
Symptoms at diagnosis
Biochemistry: urine
Raised urinary 5-HIAA1
Raised urinary 5-HIAA (ratio >3.7)
Elevated urinary 5-HIAA
Elevated urinary 5-HIAA (>300 mol/24 h)
Biochemistry: blood
Raised CgA (ratio >6.2)
Raised CgA (>5,000 g/l)
Liver function tests
Raised ␥-glutamyltransferase1
Raised alkaline phosphatase1
Raised alkaline phosphatase
Altered liver function tests
Tumor size
1–2 cm
>2 cm
>3 cm
Tumor invasion
Beyond muscularis propria
Lymph node involvement
Tumor histology
Solid growth pattern
Moderately differentiated
Poorly differentiated
Poorly differentiated
Undifferentiated
Undifferentiated
Undifferentiated
Tumor immunostain
Ki-67 index (>1%)
Ki-67 index (>2%)
Ki-67 index (>5%)
COX-2 immunostaining score
Distant disease
Carcinoid heart disease
Carcinoid heart disease with tricuspid involvement
Metastatic disease
Distant metastases
Liver metastases
Liver involvement >10%
>5 liver metastases
Treatment
Resection of primary
Hepatic surgery
Somatostatin analog use
p value
Patients
Reference
1.043
2.33
2.78
2.51
0.684
1.66
2.9
0.003
0.035
<0.0001
<0.0001
0.006
0.01
<0.01
76
156
258
3,136
3,136
258
301
23
21
14
26
26
14
19
1.004
2.35
1.87
1.8
0.003
<0.0001
0.025
<0.05
79
258
429
301
23
14
17
19
2.47
4.5
<0.0001
<0.05
258
301
14
19
1.009
1.006
2.4
2.08
0.026
0.063
0.003
0.013
79
79
137
429
23
23
15
17
1.958
3.93
4.26
0.013
<0.0001
0.0009
3,136
3,136
156
26
26
21
5.87
2.178
<0.0001
<0.0001
3,136
3,136
26
26
2.9
1.958
3.36
3.37
4.6
7.7
10.51
<0.01
0.046
0.006
0.003
NA
0.0001
<0.0001
56
3,136
3,136
429
7,693
156
3,136
16
26
26
17
18
21
26
81
156
258
37
16
21
14
27
258
52
3,136
258
79
85
256
14
28
26
14
23
29
19
79
31
92
23
30
31
5.4
3.84
2.24
1.53
<0.01
0.01
0.03
0.09
1.76
2.52
5.38
2.7
1.79
2.81
3
0.02
<0.001
<0.0001
<0.0001
0.104
0.009
<0.05
0.606
0.25
2.46
0.087
<0.001
0.021
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5-HIAA = 5-Hydroxyindoleacetic acid; CgA = chromogranin A; COX = cyclooxygenase. 1 HR per year.
Table 3. Compilation of multivariate variables predictive of survival in SI NETs from the literature and current (1973–2007) SEER
analysis
Factor
Patient characteristics
Age, per year
Age, per year
55–74 years
55–74 years
>62 years
>64 years
>65 years
>65 years
>75 years
Gender: female
Ethnicity
Symptoms at diagnosis
Biochemistry: urine
Raised urinary 5-HIAA
Elevated urinary 5-HIAA
Elevated urinary 5-HIAA
Biochemistry: blood
Raised CgA (ratio >6.2)
Raised CgA (>5,000 g/l)
Liver function tests
Raised ␥-glutamyltransferase
Altered liver function tests
Tumor size
Primary tumor >2 cm
Primary tumor >2.5 cm
Tumor invasion
Increasing SEER staging
Beyond muscularis propria
Tumor histology
Increasing histological grade
Poorly differentiated
Poorly differentiated
Tumor immunohistochemistry
Ki-67 index (>5%)
Ki-67 index (>10%)
Distant disease
Carcinoid heart disease
Carcinoid heart disease with tricuspid involvement
Distant metastases
Liver metastases
Liver involvement >10%
Treatment
Resection of primary
Surgery
Hepatic surgery
Somatostatin analog use
HR
p value
1.052
1.02
1.9
2.26
3.4
3.12
3.372
1.91
3.58
0.8
1.1
8.2
0.001
0.006
<0.0001
NA
0.0001
<0.001
<0.0001
<0.001
NA
<0.0001
0.006
0.04
1.003
2.36
1.11
0.03
0.006
0.02
1.90
4.4
0.02
<0.01
Patients
Reference
76
200
3,231
7,693
154
258
3,136
3,175
7,693
3,231
7,445
399
23
32
20
18
33
14
26
20
18
20
SEER 2007
34
79
429
200
3
17
32
258
301
14
19
79
429
23
17
1.009
2.21
0.002
0.02
2.83
4.44
<0.0001
<0.001
3,136
399
26
34
1.21
2.97
<0.0001
0.009
7,445
3,136
SEER 2007
26
1.07
2.99
4.02
<0.0001
0.034
0.02
7,445
156
429
SEER 2007
21
17
3.99
24.8
0.01
<0.001
399
399
34
34
2.04
2.55
1.98
2.3
2.63
0.001
<0.001
0.04
0.003
0.002
200
52
258
95
85
32
28
14
33
29
0.581
0.21
0.31
0.27
0.097
<0.001
0.003
<0.001
79
399
31
85
23
34
30
29
Nomogram to Assess Small-Intestinal
Neuroendocrine Tumor Survival
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5-HIAA = 5-Hydroxyindoleacetic acid; CgA = chromogranin A; NA = not available.
Table 4. Clinicopathological characteristics and univariate analy-
Table 5. Prognostic score criteria and point allocation for NET
sis of SI NETs from the current SEER database (1973–2007): patient demographics and survival (n = 7,445)
nomogram development
Gender
Female
Male
Ethnicity
White
Black
Other
Grade
WD, I
MD, II
PD, III
UD/A, IV
Unknown
Extent
Localized
Regional
Distant
Unknown
Number
Mean
survival
months
Standard 2
error
Indices
Points
Calculated
estimated nomoscore
from HR points
Age >62 years
Gender
3
0
1
0
1
2
0
8
0
1
0
2
0
2
0
2
3
0
1
3
1
2
3
4
0
4
25
0
2
0
2
2
0
3
0
p
value
3,519
3,926
126.1
130.5
3.1
3.1
1.27
6,449
833
163
129
118
94
2.45
6.2
8
8.58
<0.014
690
243
85
29
6,398
149.5
115.3
77.6
51.9
127.1
12.9
12.8
11.9
10.8
2.4
53.46
<0.001
2,039
3,015
2,164
227
145.1
145.4
90.6
104.6
5.3
3.6
2.8
9.2
204.38
<0.0001
0.26
Ethnicity1
2: Reflects the log rank (Mantel-Cox) 2 score from KaplanMeier analysis. Grades: WD, I = well-differentiated grade I; MD,
II = moderately differentiated grade II; PD, III = poorly differentiated grade III; UD/A, IV = undifferentiated (anaplastic) grade IV;
unknown = not graded in the NCI SEER database.
Symptoms at
diagnosis2
Elevated 5-HIAA
(>2! ULN)
Elevated CgA
(>6! ULN)
Abnormal liver
function tests3
Tumor size
Tumor invasion
(SEER stage)
Tumor histology
Ki-67 index
e.g. CT or MRI or combination), and whether patients had undergone hepatic surgery or somatostatin analog therapy were identified to be the most statistically significant prognostic factors relating to survival. An SI NET prognostic ‘survival’ score for each
variable was then developed by direct translation of the regression
coefficients or HR (table 5). The points were multiplied by 4 to generate a 0–100 scale and then summed into a raw score.
Internal validation was undertaken using patients from Yale
University School of Medicine, New Haven, Conn., USA (n = 8),
St. Olav’s Hospital, Trondheim, Norway (n = 9), and the Klinik für
Hepatologie und Gastroenterologie, Charité, Campus-VirchowKlinikum, Berlin, Germany (n = 16). The demographics of this
group was: median age 63 years (range 42–80), the M:F ratio
20:13, and ethnicity 32 White, 1 Black, 0 other (Asian). The observed follow-up was 0.5–19 years, and overall survival was 9.1 8
1.3 years. Nomogram scores were compared between alive and
deceased patients (Mann-Whitney 2-tailed test) while the predictive utility of the nomogram scores was assessed using KaplanMeier survival analysis [14, 34] (fig. 3).
148
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Carcinoid
heart disease4
Liver metastases
Surgical therapy5
Medical therapy
Female
Male
White
Black
Other (Hispanic)
None
Yes
No
Yes
No
Yes
No
Yes
<2 cm
2–2.5 cm
>2.5 cm
Localized
Regional
Distant
Grade I
Grade II
Grade III
Grade IV
<5
5–10
>10
No
Yes
No
Yes
None
Yes
None
SST analog
12
0
4
0
4
8
0
32
0
4
0
8
0
8
0
8
12
0
4
12
4
8
12
16
0
16
100
0
8
0
8
8
0
12
0
A linear score for age was developed based on an HR of
1.05/year (from table 3). 5-HIAA = 5-Hydroxyindoleacetic acid;
CgA = chromogranin A; ULN = upper limit of normal.
1 Ethnicity/race as defined by NCI SEER database (White,
Black, other/Hispanic).
2 Symptoms – ‘carcinoid syndrome’.
3 Elevation beyond upper limit for bilirubin, alkaline phosphatase or ␥-glutamyltransferase.
4
Confirmed by echocardiography.
5 Surgical resection of hepatic metastases.
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Indices/
detail
1.0
Gender
Female
Male
Cumulative survival
0.8
0.6
0.4
0.4
0.2
0.2
0
0
0
100
200
300
400
Survival (months)
500
Grade
WD I
MD II
PD III
UG/A IV
Ungraded
1.0
0.8
b
0.4
0.4
0.2
0.2
0
0
100
200
300
400
Survival (months)
500
100
200
300
400
Survival (months)
d
500
SEER
stage
Localized
Regional
Distant
Unstaged
0.8
0.6
0
0
1.0
0.6
c
Ethnicity
White
Black
Other
0.8
0.6
a
Cumulative survival
1.0
0
100
200
300
400
Survival (months)
500
Fig. 1. SEER database analysis 2007. Survival by gender (a), race (ethnicity, b), histological grade (c) and SEER
staging (extent, d) of n = 7,445 patients. A significant survival effect was conferred by grade, stage and ethnicity but not gender. WD = Well-differentiated (grade I); MD = moderately differentiated (grade II); PD = poorly differentiated (grade III); UG/A = undifferentiated/anaplastic (grade IV). SEER stage abbreviations refer to
localized, regional lymph node involvement or distant metastases.
HRs for the prognostic impact of each variable were obtained from the SEER database and from literature review.
De novo Analysis of the SEER Database
Clinicopathological characteristics, survival (in
months) and analysis of factors (HRs) associated with
survival of the NCI SEER patient population are included
Nomogram to Assess Small-Intestinal
Neuroendocrine Tumor Survival
in table 4; survival curves are displayed in figure 1. Significant effects of ethnicity, tumor histology and dissemination (localized, regional or distant) were associated
with risk of death. These data were included in the nomogram (table 5; fig. 2).
Prognostic Variables in the NET Literature
Age. Increasing age, particularly high age, represents a
risk factor for poor prognosis in most cancers [35]. ComNeuroendocrinology 2010;92:143–157
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Results
Points
0
10
20 30 40 50
Age (years)
20
60
1.5 2.5 4.0 6.6 10.9
Gender
Ethnicity
F
0
4
B
4
No
Elevated urinary 5-HIAA
Elevated CgA (>6× upper limit)
No
Elevated liver function test
No
Carcinoid heart disease
Tumor size (cm)
0
Ki-67 index (%)
Yes
8
<2.5 >3
8
12
I
II
III
4
8
12 16
<10
0
20
10-year survival
(probability)
>10
100
Yes
8
No
8
Yes
0
5-year survival
(probability)
IV
<5
0
Total points
12
Distant
4
0
Somatostatin therapy
Points
Yes
Loc. Reg.
Yes
100
8
<2
Surgery (hepatic)
90
Yes
0
No
80
8
0
Liver metastases
70
4
No
0
60
32
Yes
0
Tumor histology (grade)
22.6
50
Other
8
0
Tumor stage (SEER)
17.7
40
Yes
0
No
75
M
W
0
Symptoms at diagnosis
30
70
No
12
0
20
40
60
80
100
120
140
0.95
0.90
0.75
0.5 0.35
0.24
0.15 0.1
0.76
0.72
0.60
0.4 0.24
0.19
0.12 0.08
160
180
200
220
Total
points
Using the nomogram
U
Age is the only continuous variable – all others are categorical
U
‘Ethnicity’ is based on SEER data, and therefore the SEER specificity criteria are retained such that ‘other’ includes
Hispanic and Latino
U
‘Symptoms at diagnosis’ refers to carcinoid syndrome
U
‘Elevated urinary 5-HIAA’ reflects any elevation outside the normal range
U
‘Elevated liver function tests’ refers to any elevation beyond the normal range in bilirubin, alkaline phosphatase or
␥-glutamyltransferase
U
‘Carcinoid heart disease’ requires echocardiographic assessment and confirmation
U
‘SEER stage’ abbreviations refer to localized (loc.), regional lymph node involvement (reg.) or distant metastases
U
‘Tumor grade’: I = well differentiated; II = moderately differentiated; III = poorly differentiated; IV = undifferentiated/
anaplastic
U
‘Liver metastases’ is a clinical designation based upon available imaging modalities
150
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Fig. 2. Five- and 10-year probability survival nomogram for SI NETs based on the overall literature review (n =
12,412) and additional analysis of 7,445 patients in the NCI SEER database. F = Female; M = male; W = White;
B = Black; 5-HIAA = 5-hydroxyindoleacetic acid; CgA = chromogranin A.
150
NET nomoscore
100
75
50
25
1
2
3
0.8
0.6
0.4
0.2
0
0
a
Alive
Deceased
b
0
5
10
15
Survival (years)
20
pared to GEP NET patients !65 years, the excess 5-year
death risk for patients 165 years is doubled (1:1.91) [20].
Among 258 SI NET patients, age 664 years demonstrated a significantly increased risk of death with a univariate
analysis HR of 2.78 and a multivariate analysis HR of 3.12
[14]. An Italian study of 156 GEP NETs (67 pancreatic endocrine tumors, 73 gastrointestinal carcinoids and 16 of
unknown origin) noted that age 150 years compared to
!50 years at diagnosis correlated with an increased risk
of death, HR 2.33 (univariate analysis) [21]. Similarly, in
a Dutch study of 76 patients with midgut carcinoid tumors, age as a continuous variable was a prognostic factor
for survival with an HR of 1.043 and 1.052 per year for
uni- and multivariate analyses, respectively [23]. A large
European multicenter study comprising 7,693 patients
with GEP NETs investigated the relative risk of death for
patients 675 years and the age group of 55–74 years, and
identified that compared to patients between 15 and 54
years these two groups exhibited HR values of 3.58 and
2.26, respectively [18]. A report from the Mayo Clinic examined long-term survival of gastrointestinal carcinoids
(n = 154, median age 62 years, range 12–84 years) classified as foregut (7%), midgut (62%) and hindgut (30%) origin [33]. Overall, the HR in multivariate analysis for
death in patients 162 years was 3.40, and the HR was 2.7
in the subgroup classified as midgut carcinoids [33].
Among 3,231 well-differentiated GEP NETs in England
and Wales, patients aged 55–74 years had an HR of 1.9 for
death compared to the group aged 15–54 years [20].
Gender. Male gender represents an independent risk
factor for decreased 5-year survival in GEP NET disease.
In a European multicenter study including 3,715 GEP
NETs, 5-year survival was 45.5% for men compared to
49.4% for women, and the odds ratio for death was 1:0.89
[20]. In a study assessing gender in England and Wales
(1986–2001), in 3,231 well-differentiated GEP NETs, the
HR for death for women was 0.8 compared to men [20].
Similarly, in an analysis of a Scandinavian cohort of 258
SI NETs, male patients had a significantly increased risk
of death, with an HR of 1.66 in univariate analysis which
was similar (1.52) but did not reach statistical significance
in multivariate analysis [14].
Ethnicity. Race is a well-recognized independent risk
factor for survival [11, 12]. In the USA, Asians have a better prognosis and Blacks a slightly worse prognosis, compared to Whites. Specifically, Asians have the best survival among patients with localized disease, whereas
Whites had the best survival among patients with metastatic disease [11, 12].
Clinical Symptomatology (Any Clinical Symptom Related to the Neoplasm, Hormonal or Nonhormonal). In a
mixed NET population (n = 399; foregut 46.1%, midgut
37.1%, hindgut 4.5%, unknown origin 10.5%), multivariate analysis demonstrated an HR of 8.2 for death (5-year
survival) if clinical symptoms were present at diagnosis
[22].
Hormonal Symptoms. Tumors that produce bioactive
products leading to the carcinoid syndrome have been
associated with a worse prognosis with an HR of 2.9 (yes
vs. no) for shorter 5-year survival [19]. Presumably this
may reflect not only the production of bioactive products,
but also the presence of hepatic metastases or carcinoid
heart disease.
Urinary 5-HIAA. Among 256 patients with midgut
carcinoid tumors, urinary 5-HIAA 1300 mol/24 h was
associated with a shorter 5-year survival, 45 versus 72
Nomogram to Assess Small-Intestinal
Neuroendocrine Tumor Survival
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ysis (Kaplan-Meier) in the 3-group, international validation sample (n = 33).
a Nomogram scores were significantly
increased (p = 0.013, Mann-Whitney, 2tailed) in deceased (n = 17) compared to
living patients (n = 16). b Survival analysis
using the Kaplan-Meier approach identified that a nomogram score !75 (group 1)
conferred a mean survival of 15.5 8 4.3
years. Group 2 (nomoscore: 75–95) and
group 3 (nomoscore 195) had significantly
lower survivals (9.7 8 2.4 and 6.4 8 1.1
years, respectively; p = 0.032, log rank
test).
Cumulative survival
(independent validation group)
125
Fig. 3. NET nomoscores and survival anal-
NET nomoscore
1.0
152
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Metastasis. Although the recognition that spread beyond the primary location is associated with diminished
outcome, the precise relationship between the extent (tumor volume) and location of spread (local area, liver,
lymph node and extracompartmental disease) has been
difficult to quantify. In 256 patients with midgut carcinoid tumors, individuals with local disease (primary tumor and/or lymph node metastases) exhibited a median
survival of 108 months, compared to 159 months if !5
liver metastases and 53 months if 15 liver metastases [19].
Thus, hepatic metastasis represented a critical variable.
Similarly, the extent of the hepatic metastases was notable
in that the HR for 15 liver metastases compared to no
metastases was 3.0 [19]. Retrospective investigation of
258 SI NETs found that liver or other distant metastasis
was associated with an HR of 2.70 in univariate analysis
and 1.98 in multivariate analysis [14]. Yet another study
of tumors classified as midgut NETs (77 SI, 17 appendix
and 1 right colon) noted that the presence of liver metastases at diagnosis correlated with an HR of 2.3 for excess
death risk (multivariate analysis) [33].
Histological Morphological Grade. Among factors that
impact the prognosis for GEP NETs, differentiation represents one of the most critical determinants in most
studies. In a survival analysis that collated data from the
cancer registries of 12 European countries, a 4-fold increase in relative excess risk of death within 5 years of
diagnosis for poorly differentiated NETs compared to
well-differentiated ones (HR 1.0:0.27) was identified [20].
Similar results were demonstrated in a study of 119 metastatic NETs in which poorly differentiated tumors had
an increased relative risk of death with an HR of 4.02
compared to well-differentiated tumors [17]. Panzuto et
al. [21] reported that in 156 GEP NETs (67 pancreatic endocrine tumors, 73 gastrointestinal carcinoids and 16 of
unknown origin) being poorly versus well differentiated
was associated with an increased risk of death, with HR
7.70 (univariate analysis) and 2.99 (multivariate). In a
large European multicenter study that included 7,693
(6,718 well differentiated and 975 undifferentiated), the
5-year survival for well-differentiated lesions was 62.1%
in contrast to undifferentiated tumors (7.8%) while the
HR of death was 4.60 compared to those classified as well
differentiated [18].
Ki-67 Index. Ki-67 is a marker of the proliferative activity in neoplasia and is regarded by many neuroendocrine authorities as a critical index in determining both
outcome and therapy, although there is some disagreement as to how it should be calculated [10]. Cunningham
et al. [16] examined the clinical relevance of Ki-67 in 81
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months, and an HR of death of 1.8 compared to those
with values !300 mol/24 h [19]. Another study that assessed 90 metastatic GEP NETs found similar results
with an HR of 2.36 for death in multivariate analysis if
urinary 5-HIAA levels were increased twice the upper
normal limit [17]. An evaluation of 258 SI NETs demonstrated that a urinary 5-HIAA ratio 13.7! the upper normal limit was associated with an HR of 2.35 in univariate
analysis but this factor was nonsignificant in multivariate
analysis [14].
Chromogranin A Blood Levels. In a study of 301 patients with carcinoid tumors, plasma chromogranin A
values 15,000 g/l were associated with a shorter 5-year
survival, 33 versus 57 months, and an HR of death of 4.5
compared to those with values !5,000 g/l [19]. A Norwegian retrospective study of 258 SI NETs found that a
chromogranin A ratio 16.2! the upper limit of normal
was associated with an HR of 2.47 in univariate analysis
and 1.90 in multivariate analysis [14].
Abnormal Parameters of Hepatic Function. In a retrospective analysis of 137 patients with metastatic NETs,
alkaline phosphatase levels above normal were associated
with excess risk of death (HR 2.4), compared to patients
with normal alkaline phosphatase levels [15]. Similarly,
21 metastatic GEP NETs with elevated liver function tests
(alkaline phosphatase, bilirubin and ␥-glutamyltransferase), exhibited an HR of 2.08 of death compared to 84
patients with normal values [17].
Carcinoid Heart Disease. This represents a serious directly related comorbidity of serotonin-producing NETs
[36]. The presence of carcinoid heart disease represents
an individual risk factor for death in SI NET disease, with
an HR of 1.76 in univariate analysis [14]. Among the subgroup of patients with the carcinoid syndrome, echocardiography can be used to further predict the 5-year survival [28]. The HR for death in 52 patients with the carcinoid syndrome was 2.52 (univariate analysis) if tricuspid
regurgitation was present [28].
Tumor Size. As indicated in the TNM classification,
increasing tumor size has intuitively been associated with
outcome in many forms of neoplasia [37]. Evaluating a
heterogeneous group of NETs (n = 399; foregut 46.1%,
midgut 37.1%, hindgut 4.5%, unknown origin 10.5%) and
using a multivariate analysis, Pape et al. [34] demonstrated an HR of 4.44 for death (5-year survival) if the size of
the primary tumor was 12.5 cm. Similarly, Panzuto et al.
[21] reported that in 156 GEP NETs (67 pancreatic endocrine tumors, 73 gastrointestinal carcinoids and 16 of unknown origin) a primary tumor size of 13 cm increased
the risk of death with an HR of 4.26 (univariate analysis).
Extrapolation of HRs to the Prognostic Model
The HR for each variable, developed in multivariate
models in previous prognostic studies or de novo from
the NCI SEER database, was translated directly into a
prognostic weighting (table 5). For example, the HR for
symptoms at diagnosis was 8.2 and was therefore assigned a weighting of 8 points, which, like all variables,
was then multiplied by 4 to achieve the final score (variable: symptoms at diagnosis, score 32).
Internal Validation
The nomogram was applied to a validation sample
comprising 33 patients with SI NETs from the 3 collaborating institutions. An examination of nomogram scores
identified significant elevation (p = 0.01) in deceased
compared to living patients (fig. 3a). For Kaplan-Meier
analyses, scores were stratified into 3 different groups by
5-/10-year survival: group 1, nomoscore !75 (estimated
5-year survival 10.75, n = 10); group 2, nomoscore 75–95
(estimated 5-year survival 0.4–0.74, n = 9), and group 3,
nomoscore 195 (estimated 5-year survival !0.4, n = 14).
At the time of analysis 80% of group 1, 37.5% of group 2
and 31.2% of group 3 were alive. The estimated mean survival of group 1 was 15.5 8 4.3 years, for group 2 it was
9.7 8 2.5 years and 6.4 8 1.1 years for group 3 (table 6).
This was statistically significant (2 = 4.6, p = 0.03, log
rank test) and indicated that the nomogram score effectively predicted survival (fig. 3b).
Nomogram to Assess Small-Intestinal
Neuroendocrine Tumor Survival
Table 6. Summary of Kaplan-Meier analysis of NET nomogram
scores
NET
nomoscore
Group Risk
Mean
follow-up
years
Mean
Percentage survival
years
alive1
<75
75–95
>95
1
2
3
5.3385.8
6.6184.9
4.684.2
80
37.5
31.2
1
low
medium
high
15.584.3
9.782.5
6.481.1
At time of current assessment (June 2010).
Discussion
At present, the criteria for assessing the prognosis and
predicting the progression of SI NETs represent a variety
of clinical and pathological indices that are adjudged
differently in various countries and interpreted variably
by different caregivers. There exists no mathematically
based assessment of a compilation of clinical, pathological and biochemical parameters to provide a multivariate
assessment of the weighting of the numerous different
indices that comprise the clinical milieu within which the
individual disease of a particular patient and tumor may
be objectively assessed. This NET nomogram (Modlin
Score) provides a tool that can be utilized for prognosis
prediction, patient group comparisons and serve as a
guide for stratification of treatment and surveillance. Not
least is the fact that a patient may be provided with an
objective assessment by a physician regarding the future
course of his disease, thereby helping to allay a critical
concern of many patients as to ‘what the future may hold
for them’.
The nomogram is useful for visualizing the associations between each predictor variable and SI NET-specific death. However, there are limitations given the paucity of data that can be evaluated for some indices as well
as the future need for the identification of specific biomarkers that define the proliferative capacity of NET
cells and identify metastatic potential. In addition, independent external validation is necessary to confirm efficacy and identify possible additional indices that might
strengthen the mathematical basis of prediction. The
predictive power of the nomogram in the current validation sample is statistically significant but modest, reflecting the limited size of the validation sample. As the
nomogram is tested in other sample sets, we expect some
modification and substantial improvement in its predictive power.
Neuroendocrinology 2010;92:143–157
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midgut carcinoid patients all of whom had metastases
with a survival range of 1–223 months. Primary tumors
with a Ki-67 index 61.0% had an HR for death of 5.4
compared to those with Ki-67 !1% [16]. When metastases
were investigated, a Ki-67 index 61.0% had an HR for
death of 2.5 compared to those with Ki-67 !1% [16]. Bergestuen et al. [14] reported that in 130 SI NETs, Ki-67
values of 65% were associated with a significantly increased risk of death (HR of 2.24) in univariate analysis.
Separate from the prior two Scandinavian studies, a study
of a German mixed NET population (n = 399; foregut
46.1%, midgut 37.1%, hindgut 4.5%, unknown origin
10.5%) demonstrated an HR of 3.99 (multivariate analysis) for death (5-year survival) if the Ki-67 index was between 5 and 10% and an HR of 24.8 if Ki-67 was 110%
[34]. An Italian study of 156 GEP NETs (67 pancreatic
endocrine tumors, 73 gastrointestinal carcinoids and 16
of unknown origin) found that if the Ki-67 index was 12,
the risk of death was increased with an HR of 3.84 (univariate analysis) [21].
154
Neuroendocrinology 2010;92:143–157
this kind can be the development of an electronic version
for a handheld software application, minimizing computational burden and allowing for easy clinic or bedside
prediction as well as the development of additional prediction time points (e.g. 1- to 20-year predictions; table 7).
The nomogram may be useful for patient counseling,
because it predicts the probability that the patient will die
of SI NET disease within 5–10 years of diagnosis and
treatment, assuming death does not occur for other reasons. Similarly, patients who are anxious since they believe they are at high risk of tumor-related death may be
reassured based upon their nomogram score (table 7).
Physicians who are unable to determine patients at high
risk of death from SI NETs can use the tool to identify patients appropriate for early interventional therapy. A clear
advantage of the nomogram is that it can be used to predict disease-specific death more accurately than would be
achieved with straightforward subset analysis with the
Kaplan-Meier method. The nomogram could be used to
identify patients by computing their probability of SI
NET-specific death at 5 and 10 years, followed by offering
the therapy to those whose prediction is higher than a predetermined amount, which is treatment dependent.
Older patients will likely have a higher NET-specific
death prediction than younger patients. Histology seems
to be an important predictor, extending across the full
range of the point axis. In addition, the nomogram illustrates the magnitude of the worsening prognosis as the
tumor size increases. The nomogram clearly identifies
the shift in prognosis associated with grade of the tumor
and especially the Ki-67 index. Thus, for example, individuals with low-grade metastatic histological disease
plus minimal other indices of an adverse nature might
have a low to intermediate death prediction score. This
would be amended to a substantially higher death prediction if the histological parameters changed to high-grade
disease. Nevertheless, the proposed numerical weighting
system is based on published data and may be objectively
revised once additional prospective histomorphological
data are assimilated into the nomogram and further external validation is undertaken.
Additionally, the nomogram may become useful in determining the interval necessary for ideal follow-up visits,
since patients at lower risk for NET-specific death may require less stringent follow-up evaluation. The nomogram
can be used as a potential method to measure the efficacy
of individual therapies such as surgery or a particular
therapy by assessment of outcome alteration in comparison with a similar nontreated group. In addition, such calculations would allow for the development of economic
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Nevertheless, this nomogram represents an objective
analysis of all currently available data, a statistical assessment of its accuracy and an analytic compilation of its
numeric efficacy in predicting survival. As such it provides a comprehensive and easily utilized clinical tool
whereby SI NET disease may be objectively assessed and
applied to patient management. No such instrument currently exists and it is likely that application of this nomogram will yield further data that can be added to it and
allow for greater accuracy and increased predictive ability.
The overall utility of this approach is illustrated in figure
3. A random sample of US and European patients from
three different institutions identified that assessment of
the proposed indices and calculation of a NET nomoscore
accurately identified patients at low (!75 points), medium
(75–95 points) and high (195 points) risk of death.
In the examination of the nomogram predictors, there
is an inherent difficulty in studying one predictor variable at a time since some predictors are often correlated;
thus, moving a patient on one axis may tend to simultaneously alter a related axis. Changing clinical criteria are
such that altering one variable while holding all others
fixed may therefore not accurately reflect the mathematical function of the predictor axes. It is likely that the calculation of the nomogram at fixed time points may therefore also be able to provide a geometric construct predicting disease advance or stabilization as denominator of
index increase or decrease calculated as a patient curve
slope against that of a cumulative value derived from a
large cohort of comparable tumors. Although we have
used a point system nomogram in figure 2, an alternative
representation would be a table. However, a table would
require categorization of the continuous variable ‘age’,
thereby reducing predictive accuracy. In addition, a table
with all possible combinations of the predictor variable
values might be cumbersome. A different option for the
nomogram could be in the form of survival curves. However, this representation suffers from the same limitations as the table. The advantage of the point system is
that it preserves continuous variables and accuracy in an
efficient manner. Although our results do not represent
definitive comparisons of all alternative techniques, nor
can it necessarily be concluded that Cox proportional
modeling and hazard analysis are the best tools for prediction, the analysis does provide the platform on which
a predictive nomogram can be utilized or further developed. Subsequent validated iterations might limit the
number of predictive variables in the score, for example,
but at this time the best model required all the variables
described. A possible extrapolation of a nomogram of
Nomogram to Assess Small-Intestinal
Neuroendocrine Tumor Survival
Table 7. Summary of potential utilities of a NET nomogram
Predict prognosis
– At diagnosis
– To assess progression
– Reevaluation following treatment
Patient counseling
– Provide objective layman-assessable information
– Information to family
– Objective information for insurance company assessment
Formalize patient stratification
– High-risk versus low-risk groups
– Treatment versus no treatment
– Early intervention or expectant strategy
– Surveillance intervals
Evaluate treatment efficacy
– Surgical
– Medical
– Peptide radioreceptor therapy
– Ablation (embolization/radiofrequency)
Guide to therapeutic strategy
– Poor prognosis/aggressive treatment
– When to initiate treatment
– When to cease/alter treatment
Use in hand-held computers
– Facilitate assessment in oncology clinic
– Objective information for nonspecialist MD
Research tool
– More stringent/comparable stratification of clinical trial patients
– Assess outcome for individual therapy or treatment versus no
treatment
– Patient group comparisons
score ratios applied to symptoms. Similarly, a poorly differentiated or anaplastic delineation (median survival
ranging from 10 to 77 months, depending on the database
and method of analysis) seems to be an underestimated
prognostic factor (12–16 points in the current nomogram) compared to Ki-67 110% (100 points). Nevertheless, these assessments are based upon available data. It is
likely that such inconsistencies may become refined or
mathematically reworked with the prospective evaluation of an external database.
Several additional variables may provide potentially
useful prognostic information but these were not included in the nomogram since, at present, such data or technology is not available in all clinical centers. The patient’s
geographical location has been explored in some European regions and this feature significantly influences
survival [2, 18, 20, 39, 40]. Somatostatin receptor positivNeuroendocrinology 2010;92:143–157
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assessments of cost-effectiveness of a particular management strategy among comparable disease groups. Ultimately, predictions from the nomogram could be used as
probabilities in a mathematically derived decision analytic model. The role of therapy for NETs remains controversial, unpredictable and incompletely defined for a
number of reasons including inadequate ability to type
tumors at a molecular level, limitations in tumor and patient stratification and a paucity of identifiable targeted
therapeutic options, e.g. transduction pathway identified
but no evidence of its presence in a particular patient tumor [2, 38]. Consequently clinical strategies have been inconclusive or have yielded only modest data with minimal
benefit. In most instances, results have been confounded
by the inclusion of different types of NETs, a variety of
staging/grading systems and underpowered studies as
well as a wide range in survival within the WHO/AJCC/
ENETS classification compilation. Using the nomogram,
we have identified that patients entered into clinical trials
could be more stringently stratified.
There are some areas that will require adjustment in
the future. In particular, the pathological classifications
used in the NCI SEER database are not used uniformly in
Europe where neither a classification with ‘moderately
differentiated NET’, separate from well-differentiated
neuroendocrine carcinoma or anaplastic as a separate
group from poorly differentiated NET, is utilized. Once
an external validation is undertaken, it might be necessary to consider ‘poorly differentiated’ and ‘anaplastic’ as
one group on the scale unless there are sufficient data to
support the separation. Similarly, the importance of the
histological grade is considered on the basis of the differences between well- and poorly differentiated SI NETs
while it might be argued that the use of 4 grades for this
scale adds variables for which it is difficult to ensure uniform objectivity. Possibly the use of only 2 – ‘well’ and
‘poorly differentiated’ – may be worthy of consideration
given the unlikelihood that a globally acceptable pathological classification will become available in the discernible future or reconsidered once data from the WHO classification 2011 are available. A key unmet need and critical limitation in the development of this nomogram is the
absence of any molecular predictors of tumor behavior
and the potential for metastasis.
There is, in some areas of the nomogram, apparent
discordance with clinical intuition and anecdotal experience. Thus, the discrimination of histology (well vs. poorly differentiated), as well as the observation that the overall score for grades III and IV only accrues 12 and 16
points which appears underestimated in comparison to
ity on Octreoscan or 68Ga-DOTATOC positron emission/computed tomography imaging is associated with
better survival [41–43], and 18F-fluorodesoxyglucose
positron emission/computed tomography positivity is
linked to a poorer outcome [44]. As sophisticated scanning technology becomes more widely accessible, these
additional parameters may be added and further amplify
the accuracy of the nomogram. Similarly, the inclusion of
novel molecular indices of proliferation and metastasis
may also become available and provide increasingly accurate prognostication. The nomogram therefore, by definition, serves as a developmental framework for the evolution of prognostication.
Overall, the nomogram has limitations because some
factors impacting survival are incompletely characterized at present. However, by taking into account a greater
number of known factors, a survival nomogram allows
for a more realistic approximation of whether an individual patient will be alive for a defined period of time.
Longer follow-up, more patients and novel predictors are
likely to improve nomogram accuracy. The decision to
model NET-specific survival, which is hypothetical, rather than overall survival, is debatable; however, the addition of death from other causes would warrant inclusion
of several other predictors (e.g. comorbidity and socioeconomic status) to avoid assuming that patients with the
same disease-specific covariate values also have the same
risk of death from other causes. We have therefore eschewed this strategy and chosen to focus on NET disease
variables. In the meantime, it is conceivable that the no-
mogram may provide the most accurate predictions presently available. By identifying a group with a more homogeneous prognosis, the interpretation of trial outcomes
may become clearer.
The purpose of assembling this nomogram was to provide a clinical tool that is objective, assesses a diverse
range of parameters and is globally applicable by physicians to predict the prognosis of small-bowel NETs (table 7). It can also be used to assess outcome and the potential of NET-specific treatment to modify the disease
end point as well as allow for internationally comparable
patient and tumor stratification. As the biology of NET
disease is further elucidated, additional clinical, pathological and molecular markers can be validated and incorporated to amplify the predictive value and accuracy
of this clinical tool. With the availability of external validation, one may anticipate advances in physician decision
making, individual patient counseling and the implementation of rational therapeutic strategies based upon
quantifiable parameters that can be assessed and compared on a global basis.
Acknowledgments
Henning Jann of the Charité University and Ms. Maria Theresia Svejda of Yale University are acknowledged for collection and
assessment of patient data. We thank Prof. Michael Krauthammer, of Yale University School of Medicine, for his insightful overview and sagacious commentary regarding biostatistical aspects
of the paper.
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