NOMOGRAM CONSTRUCTING AND

VERIFYING OF PANCREATIC BODY AND

TAIL NEUROENDOCRINE CARCINOMA

PATIENTS

Yunhao Miao

• Department of Hepatobiliary and Pancreatic Surgery, the Second Hospital of

Jilin University, Changchun, Jilin, 130041, China

•wangyn961005@126.com

Guangqiang You

• Department of Hepatobiliary and Pancreatic Surgery, the Second Hospital of

Jilin University, Changchun, Jilin, 130041, China

Xiubo Liu

• Plastic Surgery Dept. for Burn Word, Linyi People’s Hospital, Linyi, Shandong,

276000, China

Dan Zhang*

• Department of Hepatobiliary and Pancreatic Surgery, the Second Hospital of

Jilin University, Changchun, Jilin, 130041, China

Yaning Wang

• Department of Radiology, First Hospital of Jilin University, Changchun, Jilin,

130041, China

Reception 25 February 2024 | Acceptance: 10 April 2024 | Publication: 16 May 2024

Suggested citation:

Miao, Y., You, G., Liu, X., Zhang, D. and Wang, Y. (2024). Nomogram

Constructing and Verifying of Pancreatic Body and Tail Neuroendocrine

Carcinoma Patients. 3C Empresa. Investigación y pensamiento crítico, 13(1),

196-212. https://doi.org/10.17993/3cemp.2024.130153.196-212

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ABSTRACT

Objective: To establish and evaluate a prognostic survival model for Pancreatic

neuroendocrine carcinoma (panNEC) of body and tail based on the Surveillance,

Epidemiology, and End Results (SEER).

Materials and methods: A retrospective study was conducted to collect data on

panNEC of body and tail from the SEER database between 2005 and 2019, including

clinical information and treatment regimens. A total of 246 patients were included, and

they were randomly divided into a training set and a validation set at a ratio of 8:2.

Based on independent risk factors identified through COX multivariate analysis, a

nomogram model was constructed and compared with the performance of the 8th

edition of the American Joint Committee on Cancer (AJCC) staging system in

predicting survival.

Results: Tumor differentiation, age, and treatment modality were identified as

independent risk factors for prognosis in patients with pancreatic endocrine tumors

(P<0.05). The area under the receiver operating characteristic curve (AUROC) for the

1-year, 3-year, and 5-year overall survival rates for the nomogram in the training and

validation sets were 0.850 vs. 0.992, 0.899 vs. 0.979, and 0.879 vs. 0.856,

respectively. The nomogram had a higher AUROC compared than the AJCC staging.

Calibration curves showed good calibration for the nomogram, and clinical decision

curves showed that the nomogram had higher accuracy compared with the AJCC

staging.

Conclusion: Based on the SEER database, the nomogram model can predict

individualized survival outcomes for patients with panNEC of body and tail more

accurately than the AJCC staging, providing a reference for treatment and follow-up.

KEYWORDS

SEER database, Pancreatic neuroendocrine carcinoma, Nomogram, Prognosis

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INDEX

ABSTRACT .....................................................................................................................2

KEYWORDS ...................................................................................................................2

1. INTRODUCTION .......................................................................................................4

2. MATERIALS AND METHODS ..................................................................................4

3. RESULTS ..................................................................................................................6

3.1. Construction of nomogram ...............................................................................10

3.2. Validation of Nomogram: ..................................................................................11

4. DISCUSSION ..........................................................................................................14

4.1. The impact of age on prognosis .......................................................................14

4.2. The impact of surgery on prognosis .................................................................15

4.3. The effect of tumor differentiation on prognosis ...............................................15

4.4. The shortcomings of this study ........................................................................16

5. CONCLUSION ........................................................................................................16

REFERENCES ..............................................................................................................16

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1. INTRODUCTION

In addition to pancreatic cancer, the second most common epithelial malignant

tumor of the pancreas is pancreatic neuroendocrine neoplasms (panNEN), accounting

for about 2%-5% of all pancreatic tumors. Its prognosis is often better than pancreatic

cancer [1-3]. panNEN belongs to a type of neuroendocrine tumor and is transformed

from APUD cells that originated from the endoderm during embryonic development.

panNEN includes well-differentiated neuroendocrine tumors (panNET) and poorly

differentiated neuroendocrine carcinomas (panNEC). Among them, panNEC is further

classified into small cell type and large cell type. According to the 2022 WHO

classification definition of neuroendocrine tumors, panNEN is classified into three

grades based on mitotic rate and ki67 labeling index: G1 level (mitotic count <2/2mm²

and/or ki67 index <3%); G2 level (mitotic count 2-20/2mm²

and/or ki67 index

3%-20%); well-differentiated G3 level (mitotic count >20/HPF and/or ki67 index >20%)

is called pancreatic high-grade neuroendocrine tumor (panNET). Poorly differentiated

G3 level (mitotic count >20/2mm² and/or ki67 index >70%) is called pancreatic low-

grade neuroendocrine carcinoma (panNEC). The incidence of panNEC is increasing

year by year, with an incidence of about 0.8/100,000 in the United States and about

1.27/100,000 in Japan [4]. panNEC is classified into functional and non-functional

types based on whether patients exhibit hormone-related clinical manifestations.

Functional panNEC accounts for about 34%, including insulinoma, gastrinoma,

somatostatinoma, vasoactive intestinal peptide tumor, glucagonoma, etc. Functional

panNEC patients often exhibit symptoms of hormone over secretion, so they are

usually detected and treated early in clinical practice. Non-functional panNEC

accounts for about 66%, which usually has a concealed onset and no typical clinical

manifestations in the early stage. It often presents with non-specific symptoms such

as abdominal pain, abdominal distension, indigestion, weight loss, biliary tract

obstruction, duodenal obstruction, jaundice, etc. The prognosis of pancreatic head

and tail panNEC is different. Pancreatic tail panNEC has a lower incidence and this

study explores the prognostic factors of pancreatic tail panNEC based on precision

medicine concepts.

2. MATERIALS AND METHODS

The SEER database is one of the commonly used public databases in clinical

practice. It includes a large number of retrospective clinical tumor studies in some US

states and counties (about 35% of the US population). The data is easily accessible

and publicly available free of charge, making it popular among researchers. The

included tumors include breast cancer, colorectal cancer, lung cancer, prostate

cancer, reproductive system tumors, lymphoma, leukemia, and other digestive system

tumors as well as other types of tumors that have not yet been clearly identified. The

included variables include the number of patients with the disease, age, race, time of

diagnosis, tumor size, degree of differentiation, TNM staging, primary or metastatic,

treatment method, radiotherapy and chemotherapy, survival time, and survival status

at the last follow-up.

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This study used a retrospective cohort study method to retrospectively analyze the

clinical data of 686 patients with pancreatic body and tail panNEC in the SEER

database from 2005 to 2019. The following inclusion and exclusion criteria were used:

<I> Inclusion criteria:

1. Age ≥ 18 years old;

2. Lesion is a primary malignant tumor;

3. Have a clear TNM staging;

4. Follow-up information is complete;

5. Histological diagnosis of pancreatic body and tail neuroendocrine cancer, with

histological code 8246/3 in the tumor disease classification code (ICD-O-3).

<II> Exclusion criteria:

1. Polymorphic tumor;

2. Follow-up information is incomplete;

3. Treatment method is unclear;

4. Tumor type and TNM staging are incomplete.

After strict inclusion and exclusion criteria, a total of 246 cases were included out of

the original 686 data. The influencing factors studied included ethnicity, age, gender,

marital status, number of primary tumors, tumor size, TNM stage, tumor

differentiation, chemotherapy, and surgical intervention. The observation indices were

the overall survival time (OS), which refers to the time interval from the date of

diagnosis to death due to any cause, and the survival status at the last follow-up.

Collected clinical data and treatment methods of patients with pancreatic body and

tail panNEC diagnosed clinically from the SEER database between 2005 and 2019.

246 collected data were randomly divided into a training set and a validation set at a

ratio of 8:2. The training set was used for model establishment and internal validation,

while the validation set was used for external validation. IBM SPSS was used for data

analysis. Factors with significant univariate Cox regression analysis (p<0.05) were

included in multivariate Cox regression analysis. Variables with p<0.05 in multivariate

Cox analysis were plotted using the Kaplan-Meier survival curve. Multivariate analysis

results (p<0.05) were used to construct nomograms using RStudio, and compared

with the eighth edition of the American Joint Committee on Cancer (AJCC) staging

system. The prognostic performance of the models was compared using consistency

index (C-index), calibration curve, and area under the receiver operating characteristic

curve (AUROC). Decision curve analysis (DCA) was used to quantify the net benefit

at different threshold probabilities to evaluate the clinical utility of the model.

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3. RESULTS

The 246 cases were randomly divided into a training set (197 cases) and a

validation set (49 cases) at a ratio of 8:2. The baseline data of the patients is shown in

Table 1.

COX univariate analysis using IBM SPSS produced the following results: race

(p=0.64), gender (p=0.44), age (p=0.01), marital status (p=0.39), T staging (p<0.01),

N staging (p=0.09), M staging (p<0.01), tumor size (p<0.01), tumor number (p=0.25),

systemic therapy (p=0.41), tumor differentiation (p<0.01), and surgical intervention

(p<0.01). The variables with p<0.05 in the COX univariate analysis were included in

the COX multivariate analysis, which produced the following results: age (p<0.01), T

staging (p=0.96), M staging (p=0.40), tumor size (p=0.93), tumor differentiation

(p<0.01), and surgical intervention (p<0.01). The training set univariate and

multivariate analysis results are shown in Table 2.

Table 1. Characteristics of training cohort and validation cohort

Variable Total

(n=246)

Training Cohorts

(n=197)

Validation Cohorts

(n=49)

Age

≤54 74 (30.08%) 62 (31.47%) 12 (24.49%)

55~74 137 (55.69%) 109 (55.33%) 28 (57.14%)

≥75 35 (14.23%) 26 (13.20%) 9 (18.37%)

Gender

Male 146 (59.35%) 115 (58.38%) 31 (63.27%)

Female 100 (40.65%) 82 (41.62%) 18 (36.73%)

Race

White 181 (73.58%) 146 (74.11%) 35 (71.43%)

Black 25 (10.16%) 21 (10.66%) 4 (8.16%)

Other 40 (16.26%) 30 (15.23%) 10 (20.41%)

Marital status

Married 159 (64.63%) 127 (64.47%) 32 (65.31%)

Unmarried 87 (35.37%) 70 (35.53%) 17 (34.69%)

Chemotherapy

Yes 20 (8.13%) 17 (8.63%) 3 (6.12%)

No 226 (91.87%) 180 (91.37%) 46 (93.88%)

Surgery

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Yes 207 (84.15%) 163 (82.74%) 44 (89.80%)

No 39 (15.85%) 34 (17.26%) 5 (10.20%)

T1 73 (29.67%) 57 (28.93%) 16 (32.65%)

T2 81 (32.93%) 64 (32.49%) 17 (34.69%)

T3 77 (31.30%) 64 (32.49%) 13 (26.53%)

T4 15 (6.10%) 12 (6.09%) 3 (6.12%)

N0 168 (68.29%) 133 (67.51%) 35 (71.43%)

N1 78 (31.71%) 64 (32.49%) 14 (28.57%)

M0 182 (73.98%) 145 (73.60%) 37 (75.51%)

M1 64 (26.02%) 52 (26.40%) 12 (24.49%)

Differentiation

Highly 165 (67.07%) 137 (69.54%) 28 (57.14%)

Moderately 47 (19.11%) 32 (16.24%) 15 (30.61%)

Poorly 34 (13.82%) 28 (14.21%) 6 (12.24%)

Tumor size

≤2cm 81 (32.93%) 63 (31.98%) 18 (36.73%)

＞2cm 165 (67.07%) 134 (68.02%) 31 (63.27%)

Tumor number

Single 170 (69.11%) 139 (70.56%) 31 (63.27%)

Multiple 76 (30.89%) 58 (29.44%) 18 (36.73%)

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Table 2. Univariate and multivariate analysis for panNEC of the training cohort.

Variable Univariate analysis

HR(95%CI) P-value

Multivariate analysis

HR(95%CI) P-value

Age

≤54

2.274(1.382-3.743) 0.01

0.831（0.337-2.049）688

55~74

4.131

（

1.615-10.562

）3

≥75

Gender

Male

1.289(0.673-2.470) 444

Female

Race

White

1.101(0.737-1.645) 639

Black

Other

Marital status

Married

0.757(0.402-1.426) 389

Unmarried

Chemotherapy

Yes

1.483(0.581-3.788) 410

Surgery

Yes

0.076(0.039-0.151) ＜0.01 0.195（0.070-0.542）＜0.01

T1 2.158（1.489-3.130）＜0.01 —— 938

T2 938

T3 940

T4 ——

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The univariate analysis showed that age, T staging, M staging, surgery, tumor size,

and tumor differentiation were related factors affecting the prognosis of patients with

pancreatic body and tail panNEC (p<0.05). Multivariate analysis showed that age,

tumor differentiation, and surgical intervention were independent risk factors for the

prognosis of patients with pancreatic body and tail panNEC (p<0.05). Based on the

results of the three multivariate analyses, a Kaplan-Meier curve was plotted, as shown

in Figure 1.

Figure 1. Kplan-Meier analysis for independent risk factor of panNEC: age(A), differentiation

(B), surgery (C)

From Figure 1, it can be observed that as survival time increases, patients of older

age have a faster decrease in survival probability. Patients who receive conservative

1.703（0.913-3.177）94

4.832（2.582-9.040）＜0.01 1.452（0.615-3.429）395

Differentiation

Highly

3.275(2.274-4.717) ＜0.01

1.860（0.690-5.017）220

Moderately

4.367

（1.714-11.123）＜0.01

Poorly —— ——

Tumor size

≤2cm 9.618

（2.319-39.900）＜0.01 —— 926

＞2cm

Tumor number

Single

1.455（0.766-2.766）252

Multiple

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treatment have a faster decrease in survival probability compared to those who

undergo surgery. Additionally, lower tumor differentiation corresponds to a faster

decrease in survival probability. This is consistent with previous research findings.

3.1. CONSTRUCTION OF NOMOGRAM

Nomogram, also known as an alignment diagram, is a graph that uses a family of

disjoint line segments in a two-dimensional Cartesian coordinate system to represent

a function with two independent variables. This type of graph is primarily used to

express the relationships between variables in predictive models and can be applied

in many fields, including medicine, meteorology, and economics.

In the field of medicine, nomograms can combine various clinical characteristics to

predict individualized outcomes, allowing for more convenient and rapid access to

targeted predictive outcomes, as well as intuitive observation of the results of

regression analysis. For example, in tumor prognosis studies, nomograms can be

used to predict the survival, prognosis, and recurrence risk of tumor patients. By

constructing a multi-factor regression model, integrating multiple predictive indicators,

and then using a graduated line segment drawn on a common plane according to a

certain proportion, the relationship between each variable in the predictive model can

be expressed. In this way, researchers can intuitively understand the patient's

condition, predict the disease's development trend, and evaluate the treatment effect

by observing the nomogram based on the patient's specific situation.

According to the results of the three COX multivariate analyses, we used RStudio

to construct nomograms for predicting 1-, 3-, and 5-year survival rates. (Figure 2)

Figure 2. Prognostic nomogram for patients with pancreatic body and tal panNEC

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3.2. VALIDATION OF NOMOGRAM:

The C-index of Nomogram in the training set was 0.835, and in the validation set

was 0.861. Calibration curves of Nomogram and AJCC staging were plotted in the

training and validation sets (Figure 3). The 1-year overall survival rate calibration

curve of Nomogram was in good agreement with the ideal slope of 1, suggesting that

compared with AJCC staging, using the Nomogram established in this study to predict

overall survival rate was more consistent with the actual results and more accurate.

ROC curves of Nomogram and AJCC staging were plotted in the training and

validation sets, including 1-year, 3-year, and 5-year survival rates (Figure 4). In the

training set, the AUROC of Nomogram was 0.850, 0.899, and 0.879, while the

AUROC of AJCC staging was 0.875, 0.830, and 0.777; in the validation set, the

AUROC of Nomogram was 0.992, 0.979, and 0.856, while the AUROC of AJCC

staging was 0.832, 0.817, and 0.836. From the above data, it can be seen that both in

the training and validation sets, Nomogram had higher C-index and AUROC, showing

better predictive performance without significant overfitting. To further evaluate the

clinical value of Nomogram, clinical decision curves for 1-year, 3-year, and 5-year

overall survival rates were plotted (Figure 5). The trend of DCA curve represented the

predictive ability and accuracy of the model under different decision thresholds. The

upper the curve is, the higher the predictive ability and accuracy of the model are.

Obviously, DCA curve showed that Nomogram had better predictive efficiency than

AJCC staging in this study.

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Figure 3. Calibrationcurves of nomogram and AJCC staging (A: training calibrationcurve, B:

validation calibrationcurve, C: AJCC training calibrationcurve, D: AJCC calibrationcurve)

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Figure 4. AUROC of nomogram and AJCC staging (A: training AUROC, B: validation AUROC

C: AJCC training AUROC D: AJCC validation AUROC)

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Figure 5. Decision curve of nomogram and AJCC staging (a: DCA of training, b: DCA of

validation, c: DCA of AJCC training, d: DCA of AJCC validation)

4. DISCUSSION

4.1. THE IMPACT OF AGE ON PROGNOSIS

The COX multivariate analysis shows that age is an independent risk factor for the

prognosis of patients with pancreatic endocrine neoplasms (panNEC) in the body and

tail of the pancreas. This may be related to the following aspects:

1. Previous studies have shown that with increasing age, immune function

decreases and DNA repair abnormalities increase, which directly leads to

tumorigenesis [5]. Epidemiological investigations have also suggested that

elderly patients with cancer have a poor prognosis, which is consistent with the

conclusion of this study.

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The mechanism of poor prognosis in elderly patients may be related to the

overactivation of related signaling pathways [6]. Compared with non-elderly

patients, there are differences in the expression of tumor-related genes in

elderly patients. With increasing age, mutations in tumor suppressor genes

lead to the loss of control of tumor signaling, with the Akt/mTOR-representing

pro-tumor signaling pathway expression increasing, making tumors more prone

to development, growth, and metastasis.

Elderly patients often have more underlying diseases, more conservative

treatment options, early symptoms not obvious, poor family economic

conditions, and less active treatment, which leads to a worse prognosis

compared to younger patients.

4.2. THE IMPACT OF SURGERY ON PROGNOSIS

The COX multivariate analysis shows that surgical resection is an independent risk

factor for the prognosis of patients with pancreatic endocrine neoplasms (panNEC) in

the body and tail of the pancreas. The Kaplan-Meier curve shows that patients who

undergo surgery have a better prognosis than those who receive conservative

treatment. This is consistent with previous literature reports, where surgical treatment

is the preferred treatment option for most resectable panNEC patients [7-8]. Surgery

can reduce disease-related symptoms, alleviate patient suffering, and improve quality

of life and survival time. Whether patients with metastatic panNEC require aggressive

surgical intervention has long been controversial. Some studies have shown that for

patients with panNEC who cannot undergo radical resection, debulking surgery

(removing as much of the tumor as possible, including the primary tumor and

metastatic deposits) can also alleviate clinical symptoms and improve long-term

outcomes [9-12]. Haugvik [13]and colleagues studied 119 patients with panNEC and

found that patients who underwent radical resection had a 3-year survival rate of 69%.

Even for patients with metastatic disease, removing the primary tumor can improve

patient prognosis. For elderly patients, patients with poor general condition, or

patients who cannot tolerate surgery, if conservative treatment has no significant

effect, palliative surgery can still be performed to treat tumor-related complications

[14].

4.3. THE EFFECT OF TUMOR DIFFERENTIATION ON

PROGNOSIS

The COX multivariate analysis shows that tumor differentiation is an independent

risk factor for the prognosis of patients with pancreatic endocrine neoplasms

(panNEC) in the body and tail of the pancreas. The degree of tumor differentiation has

a significant impact on the prognosis of panNEC. Generally speaking, the higher the

degree of tumor differentiation, the better the prognosis is usually. This is consistent

with multi-center studies [15-18]. In panNEC, well differentiated tumors have a cell

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morphology and biological behavior similar to normal neuroendocrine cells, with a

lower proliferation rate and lower aggressiveness. This type of tumor is usually

associated with a good prognosis and a longer survival time after surgical resection.

In contrast, poorly differentiated tumors have a cell morphology and biological

behavior that differ significantly from normal neuroendocrine cells, with a higher

proliferation rate and stronger aggressiveness. This type of tumor is prone to

metastasis and recurrence, usually has a poor prognosis, and a shorter survival time.

4.4. THE SHORTCOMINGS OF THIS STUDY

Despite the large volume of data in the SEER database, which covers a majority of

the US population and has follow-up data for each patient, this study extracted data

with the same characteristics. However, there are still some unavoidable limitations:

(1) The database does not include specific information on radiotherapy and

chemotherapy, with only whether the patient received chemotherapy being recorded,

without specific regimens and doses; radiotherapy information only includes the site

and some techniques (such as particle implantation or external irradiation, etc.),

without important treatment information such as surgical margin status. (2) The follow-

up outcome only includes death and the cause of death, which limits research on

recurrence, metastasis, or progression. (3) Some cases in the database have

incompletely recorded surgical methods.

5. CONCLUSION

In summary, in this study, age, treatment modality, and tumor differentiation were

independent risk factors for the prognosis of patients with pancreatic endocrine

neoplasms in the body and tail (p<0.05). The nomogram based on the SEER

database can more accurately assess patient prognosis and predict survival time,

providing a feasible prediction model for clinicians to better individualize treatment

plans for patients, which has certain significance.

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