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Abstract

BACKGROUND:

Abdominal or back pain is a common symptom in pancreatic diseases. However, the role of pain in pancreatic neuroendocrine neoplasm (PNENs) has not been clarified.

OBJECTIVE:

In this study, we aimed to show the association between the pain and the grade of PNENs.

METHODS:

A total of 186 patients with pathologically confirmed PNENs were included in this study. Clinical features and histological or radiological findings (size, location, and vascular invasion and local organs invasion and distal metastasis) were collected. Logistic regression analyses were used to show the association between pain and grade of PNENs. Nomogram was developed based on associated factors to predict the higher grade of PNENs. Receiver operating characteristic (ROC) curve was used to evaluate the diagnostic performance of size and nomogram model.

RESULTS:

The prevalence of pain in the cohort was 30.6% ( $n=$ 57). The vascular invasion and G3 PNENs were more common in the pain group ( $P=$ 0.02, $P<$ 0.01). The tumor size was larger and incident of higher grade of PNENs was higher in the pain group than the non-pain group ( $p<$ 0.01). Age, pain and size were independent risk factors for G2/G3 or G3 PNENs. The odds ratio was 3.03 (95% CI: 1.67–7.91) and 3.32 (95% CI: 1.42–7.79) for pain, respectively. The nomogram model was developed to predict the G2/G3 or G3 PNENs. The area under the curve (AUC) of the nomogram model was 0.84 (95% CI, 0.77–0.91) in predicting the G2/G3 PNENs, and was 0.84 (95% CI, 0.78–0.91) in predicting the G3 PNENs.

CONCLUSION:

Abdominal or back pain is associated with the grade of PNENs. The nomograms based on clinical features may be a powerful numerical tool for predicting the grade of PNENs.

Keywords

Pancreatic neuroendocrine neoplasm grade pain nomogram clinical features

Abbreviations

AUC	area under the curve
CI	confidence interval
CT	computed tomography
OR	odds ratio
PDAC	pancreatic ductal adenocarcinoma
PNEN	pancreatic neuroendocrine neoplasm

NEC	neuroendocrine carcinoma
NGF	nerve growth factor
ROC	receiver operating characteristic
MRI	magnetic resonance imaging

1. Introduction

Pancreas is one of the main common organs for neuroendocrine neoplasms (PNENs) occurrence. PNENs account for 1% to 2% of all pancreatic neoplasms [1]. The incidence of PNENs has been increasing due to improvement of available imaging modalities [1, 2, 3]. In 2017, the updated WHO classification of gastrointestinal neuroendocrine neoplasms divided NENs into grade 1 (G1), grade 2 (G2) and well-differentiated high-grade neoplasm (PNEN G3) and poorly differentiated carcinomas (PNEC G3). PNENs grades are related to genetic changes, clinical presentations, imaging features, prognosis, and therapeutic strategies [1, 2]. Therefore, it is helpful to identify those PNENs preoperately with a higher grade for treatment planning.

Previous studies had shown that radiological features derived from magnetic resonance imaging (MRI) and computed tomography (CT) favor the identification of PNENs pathological grades [2, 4, 5, 6, 7, 8]. However, little is known on the association between clinical presentations and WHO grade of PNENs. Abdominal or back pain is a common symptom of pancreatic diseases. Some studies indicated that abdominal pain sensation in pancreatic diseases may be used as an additional diagnostic tool to estimate pancreatic tumor and patient’s prognosis [9, 10, 11]. Many studies showed the association between pain and chronic pancreatitis or pancreatic cancer [12, 13, 14, 15]. Little is known on the relationship between abdominal pain and PNENs. Zaidi et al. showed that pain may be related with PNENs recurrence in univariate analysis [3]. However, the association between pain and PNENs grade is not clear. Nerve invasion, which may be a cause of pain in pancreatic cancer [16], is not uncommon in PNENs [17]. Therefore, the purpose of our study was to determine the association between the abdominal or back pain and PNENs grade and to develop nomogram models to predict higher grade of PNENs.

2. Materials and methods

2.1 Patients

A total of 204 patients with pathologically diagnosed PNENs were identified between January 2011 and July 2020. Ten patients was excluded due to the missing information of clinical symptom or histological grade. Eight patients underwent biopsy were excluded because the limitations of nerve invasion evaluation. Finally, 186 patients were included for further analysis. Demographic information and clinical symptoms were obtained from the medical database. Pain was evaluated based on a 6-point Likert scale: no pain, very mild, mild, moderate, severe, and very severe. The pain assessments were performed by patients via self-report.

There are 89 males and 97 females. All patients were divided into two groups based on the presence or absence of abdominal or back pain: non-pain group ( $n=$ 129, 61 men and 68 women), average age of 55.46 years; pain group ( $n=$ 57, 28 men and 29 women), average age of 57.30 years. The institutional review board of the Affiliated Hospital of Nanjing University of Chinese Medicine approved our study (No. 2017NL-137-05) and waived the formal consent because this was a retrospective study. Declaration of Helsinki was followed during the whole investigation.

2.2 Histopathologic analysis

Histopathologic analysis was performed based on the pathology report. The following information was collected: lymph node invasion, vascular invasion (vein and arteries), local organs invasion and nerve invasion. Distal metastasis was evaluated by radiological examinations. In accordance with the updated 2017 World Health Organization classification, the PNENs were classified as G1 ( $<$ 2 mitoses per 10 high-power fields (HPF), Ki-67 $<$ 3%), G2 (2–20 mitoses per 10 HPF; Ki-67, 3% to 20%), and G3 ( $>$ 20 mitoses per 10 HPF; Ki-67 $>$ 20%). We divided G3 PNENs into well-differentiated high-grade neoplasm (PanNEN G3) and poorly differentiated carcinomas (PanNEC G3) based on ki67 index of 55%.

Table 1
The characteristic of subjects with or without abdominal pain

	Non-pain ( $n=$ 129)	Pain ( $n=$ 57)	$p$
Age	55.46 $\pm$ 11.93	57.30 $\pm$ 11.03	0.32
Sex (male/female)	61/68	28/29	0.82
Lymph node invasion (yes)	9	9	0.058
Vascular invasion (yes)	13	13	0.02
Local organs invasion and distal metastasis (yes)	18	9	0.74
Perineural invasion (yes)	9	8	0.087
Grade (G1/G2/G3)	53/56/20	11/23/23	$<$ 0.01
Location (head-neck/body/tail)	59/43/27	29/19/9	0.68
Diabetes (yes)	24	15	0.23

Figure 1.

The tumor size and ki67 index in pancreatic neuroendocrine neoplasms with or without pain.

2.3 Statistical analyses

All statistical analyses were performed with commercially available software (SPSS 20.0 Chicago, III, USA). Quantitative data with normal distribution were presented as means $\pm$ standard deviation. The characteristics of the lesions were compared using the independent samples $t$ -test, or Chi-square or Fisher’ exact test. Multivariate logistic regression were used to evaluate the associations between pain and grade of PNENs (G1 vs G2/G3 and G1/G2 vs G3). Linear regression was used to test the collinearity between pain and tumor size. Two models were adopted: model 1 was only adjusted with location; model 2 was further adjusted with lymph node, vascular, nerve invasion and local organs invasion and distal metastasis. Based on the result of the multivariate logistic regression, the nomogram model was constructed to predict G2/G3 or G3 of PNENs. Receiver operating characteristic (ROC) curve was used to evaluate the diagnostic performance of size and nomogram model. The nomogram model and ROC cover were performed with R 3.4.1 (http://www.R-project.org) using the rms, pROC package. $P<$ 0.05 was considered to indicate a significance difference.

3. Results

3.1 Patients characteristics

The symptom of pain was mild or moderate in our patients, which persisted for more than one week. Patients characteristics are shown in Table 1. Vascular invasion ( $P=$ 0.02) and high pathological grade ( $p<$ 0.01) were more common in the pain group than that non-pain group. The size of tumor was lager and ki67-index was higher in the pain group than the non-pain group ( $p<$ 0.001, Fig. 1). But no significant differences were observed for the remaining variables including location lymph node invasion, local organs invasion and distal metastasis (Table 1). Nerve invasion was also common in pain group (6.9% vs 13.9%, $p=$ 0.087). No significant differences were found in age, sex and prevalence of diabetes between the two groups.

3.2 The association between pain and PNENs grade

Linear regression test showed that there were no collinearity between pain and tumor size (VIF $=$ 1.01). Multiple logistic regression was used to show the association between pain and G2/G3 PNENs (Table 2). Age (odds ratio (OR) $=$ 1.05, 95% confidence interval (CI): 1.06–1.10), tumor size (OR $=$ 18.56, 95% CI: 7.81–44.1) and pain (OR $=$ 2.91, 95% CI: 1.16–7.30) were independently associated with G2/G3 PNENs. Similar results were found when considered the possible modifiers. The OR was 1.05 (95% CI: 1.01–1.09) for age, 13.53 (95% CI: 5.44–33.69) for tumor size and 3.03 (95% CI: 1.67–7.91) for pain.

Table 2
The association between pain and G2/G3 grade of pancreatic neuroendocrine neoplasms

	Model 1	$p$	Model 2	$p$
	OR (95% CI)		OR (95% CI)
Age (y)	1.05 (1.06–1.10)	0.01	1.05 (1.01–1.09)	0.007
Sex (M vs F)	1.90 (0.88–4.12)	0.13	1.67 (0.73–3.79)	0.21
Pain (yes vs no)	2.91 (1.16–7.30)	0.02	3.03 (1.16–7.91)	0.021
Size ( $\geqslant$ 2 vs $<$ 2.0 cm)	18.56 (7.81–44.1)	$<$ 0.001	13.53 (5.44–33.69)	$<$ 0.001

Model 1 was adjusted with location. Model 2 was further adjusted with lymph node, vascular, nerve invasion and local organs invasion and distal metastasis. CI: confidence interval; OR: odds ratio.

Table 3

The association between pain and G3 grade of pancreatic neuroendocrine neoplasms

	Model 1	$p$	Model 2	$p$
	OR (95% CI)		OR (95% CI)
Age (y)	1.06 (1.02–1.09)	0.003	1.05 (1.01–1.09)	0.007
Sex (M vs F)	4.36 (1.88–10.10)	$<$ 0.001	4.46 (1.85–10.74)	$<$ 0.001
Pain (yes vs no)	3.62 (1.61–8.15)	0.002	3.32 (1.42–7.79)	0.008
Size ( $\geqslant$ 2 vs $<$ 2.0 cm)	12.31 (3.41–44.48)	$<$ 0.001	9.89 (2.66–36.80)	0.001

Model 1 was adjusted with location. Model 2 was further adjusted with lymph node, vascular, nerve invasion and local organs invasion and distal metastasis. CI: confidence interval; OR: odds ratio.

Table 4

The association between pain and PanNEC

	Model 1	$p$	Model 2	$p$
	OR (95% CI)		OR (95% CI)
Age (y)	1.07 (1.02–1.12)	0.007	1.07 (1.02–1.13)	0.007
Sex (M vs F)	3.12 (1.12–9.09)	0.03	3.22 (1.08–9.90)	0.036
Pain (yes vs no)	3.11 (1.17–8.26)	0.002	2.81 (1.01–7.81)	0.048
Size ( $\geqslant$ 2 vs $<$ 2.0 cm)	3.65 (0.98–13.55)	$<$ 0.001	2.44 (0.61–9.73)	0.21

Model 1 was adjusted with location. Model 2 was further adjusted with lymph node, vascular, nerve invasion and local organs invasion and distal metastasis. CI: confidence interval; OR: odds ratio; PanNEC: pancreatic neuroendocrine carcinoma.

Table 5

The association between pain and G2/G3 or G3 grade of pancreatic neuroendocrine neoplasms in patients younger than 70 years old ( $n=$ 161)

	G2/G3		G3
	OR (95% CI)	$p$	OR (95% CI)	$p$
Age (y)	1.06 (1.02–1.10)	0.003	1.11 (1.05–1.17)	$<$ 0.001
Sex (M vs F)	1.82 (0.81–4.03)	0.15	6.66 (2.50–19.16)	$<$ 0.001
Pain (yes vs no)	2.79 (1.11–7.04)	0.029	5.25 (1.98–13.92)	0.008
Size ( $\geqslant$ 2 vs $<$ 2.0 cm)	14.38 (5.89–35.17)	$<$ 0.001	24.67 (4.92–123.88)	$<$ 0.001

Model was further adjusted with location, lymph node, vascular, nerve invasion and local organs invasion and distal metastasis. CI: confidence interval; OR: odds ratio.

We also showed the association between pain and G2 PNENs. Multiple logistic regression showed a weak association between pain the G2 tumor (OR $=$ 2.09, 95% CI: 0.76–5.79, $p=$ 0.16). Subsequently, we showed the association between pain and G3 PNENs (Table 3). Age (OR $=$ 1.06, 95% CI: 1.02–1.09), pain (OR $=$ 3.62, 95% CI: 1.61–3.85), sex (OR $=$ 4.36, 95% CI: 1.88–10.10) and size (OR $=$ 12.31, 95% CI: 3.41–44.48) were independently associated with G3 PNENs. Similar results (OR $=$ 3.32, 95% CI: 1.42–7.79) were observed after adjusting lymph node metastasis, vascular invasion, nerve invasion, local organs invasion and distal metastasis.

Pain was also associated with PNEC (OR $=$ 2.71, 95% CI: 1.08–6.83) in univariate analysis. When adjusted with age, sex, tumor size, and tumor location, the OR was 3.11 (95% CI: 1.17–8.26) (Table 4). Similar results (OR $=$ 2.81, 95% CI: 1.01–7.81) were observed after adjusting lymph node metastasis, vascular invasion, nerve invasion, local organs invasion and distal metastasis (Table 4).

Subgroup analysis in patients younger than 70 years old also showed that pain was associated with G2/G3 (OR $=$ 2.79, 95% CI: 1.11–7.04) or G3 (OR $=$ 5.25, 95% CI: 1.98–13.92) tumors (Table 5).

3.3 Nomogram to predict G2/G3 or G3 PNENs

Figure 2.

The nomograms for predicting G2/G3 (A) or G3 (B) pancreatic neuroendocrine neoplasms (PNENs). Age, tumor size and pain was adopted in identifying G2/G3 tumor; age, tumor size (1, $\geqslant$ 2.0 cm; 0, $<$ 2.0 cm), sex (1, male; 0, female) and pain (1, yes; 0, no) were used in identifying G3 tumor.

Figure 3.

Receiver operating characteristic (ROC) curve for tumor size and nomogram in predicting G3 (A) or G2/G3 (B) pancreatic neuroendocrine neoplasms.

To visualize the multivariate logistic regression, the nomogram model was constructed in predicting the G2/G3 PNENs (Fig. 2A) or G3 PNENs (Fig. 2B). The performance of the nomogram was measured by the area under the ROC curves (AUC). In the prediction of G2/G3 PNENs, the AUC of the nomogram model was 0.84 (95% CI, 0.77–0.91), and the AUC of tumor size was 0.70 (95% CI, 0.62–0.78). The AUC value revealed a high performance of tumor grade discrimination using the combined nomogram (Fig. 3). Similar results were observed in nomogram model in predicting the G3 PNENs. The AUC of the nomogram was 0.84 (95% CI: 0.78–0.91) which was higher than the AUC of tumor size (AUC $=$ 0.82, 95% CI: 0.75–0.89).

4. Discussion

The pathological grade of PNENs is marked related with the treatment strategies and the prognosis [1]. Many studies have shown that the medical images and the radiological texture analysis are valuable for the identification of PNENs grade [4, 5, 6, 7, 8]. However, there are few studies reported the association between clinical presentations and PNENs grade, especially the common symptom in pancreatic diseases, abdominal pain. In this study, we found that abdominal pain was the independent predictors of G2/G3, G3 PNENs or PanNEC. In addition, we also developed two nomogram models based on pain and other clinical characteristics which showed high performance to predicting the G2/G3 or G3 PNENs.

Some studies reported that tumor cells can invade the perineurium in the pancreatic ductal adenocarcinoma (PDAC) [10, 14, 16]. Several studies also showed nerve invasion is common in PNENs [17, 18, 19]. The incidence of nerve invasion was ranged from 10% to 20.0% in PNENs. Our data was 9.7% (17 cases) which was slightly lower than previous published data. Many factors may affect the incidence, such as sample size and number of low grade PNENs. The mechanism of nerve invasion in PNENs has not been clarified. In our study nerve invasion was significantly associated with a higher grade of PNENs (data not shown). PanNEC shares some common features with PDAC [20]. We speculated that PanNEC cells may have similar biological behaviors with PDAC, including nerve invasion. In addition, the high proliferation ability of G2/G3 tumor may also enhance nerve invasion. How PNENs cells, especially for PanNEC cells, invade nerve need further exploration.

The association between pain and nerve invasion in PDAC has been reported in many studies [16, 21]. Our data indicated that pain was associated with PNENs grade. Nerve invasion may play critical role because our data showed a weak association between nerve invasion and pain in PNENs (OR $=$ 2.43, 95% CI: 0.88–6.85, $p=$ 0.09). The incidence of never invasion is higher in pain group than non-pain group. It has been showed that many of the molecules involved in perineural invasion are also implicated in pain generation, and the neuropathic alterations were correlated to the severity of tissue inflammation, pancreatic neuritis, the degree of cancer cell invasion [16]. Cancer cells may cause damage to neuronal sheath which makes nerve processes vulnerable to noxious stimuli [16]. In addition, tumour-associated cells, such as immune cells and fibroblasts, can secret nerve growth factor (NGF), which can activate and sensitize sensory nerves and cause pain generation [16]. Transient receptor potential cation channel, subfamily V, member 1 (TRPV1) which can be modulated by NGF is a critical cytokine for pain generation. Granulocyte colony stimulating factor (GCSF) and granulocyte-macrophage colony stimulating factor (GMCSF) were another two factors that contribute to pain generation In addition, the tumor size of G3 PNENs was larger than G1 or G2 tumors. The larger tumor may be more easily and directly cause pain. Meanwhile, larger tumor may have more chance to invade nerve. Our data also observed an association between pain and tumor size (OR $=$ 1.18, 95% CI: 1.03–1.38, $p=$ 0.04).

Nomogram is one of the widely accepted models in clinical studies to predict treatment efficiency, cancer recurrence and prognosis, which can provides a rapid and simple graphical interpretation of numeric statistics [22, 23]. Moreover, nomogram can calculate individual risk by synthetically considering all independent risk factors and give individualized predictions [24, 25]. Previous studies suggested the combined nomogram model for preoperative prediction of the PNENs grade may facilitate the treatment and affect prognosis [4, 26]. Therefore, we developed nomogram models which is beneficial to personalized treatment decisions to predict the grade of PNENs. Age, tumor size and pain were the determinant factors in our model. Our model had acceptable performance. Some studies also reported that the size is good indicator in the differentiation PNENs grade or malignancy [27, 28, 29, 30, 31, 32]. Our results were similar with the previous reports. Moreover, our data showed the combination model was better than model using size alone in identifying G2/G3 PNENs. Our study supplied a tool for physicians to simply predict PNENs grade. The physicians may make a treatment plan based on our model. However, the final diagnosis of tumor grade should be based on histological examinations. Our study provided a reference for the clinical diagnosis and treatment.

Our study has several limitations. First, we did not perform an external validation because we did not have additional independent population. Second, the image features (such as the enhanced images) and the clinical data (such as the chromogranin A, CgA) were not included. Third, the sample size in our study was not large. A study with large sample size is need to confirm our findings. Fourth, we divided G3 PNENs into PanNEN G3 and PanNEC G3 only based on the ki67 index ( $>$ 55%) because many patients were identified between 2011–2017 and the reevaluation of specimens was impossible. In addition, we did not analyze the association between pain and the prognosis or other clinical course. Finally, pain as a symptom was derived from patient files retrospectively. We did not quantitatively assess the severity of pain based on 10-point scale.

In conclusion, our study is the first one to report an association between abdominal or back pain and the grade of PNENs. This study has identified pain might be an associated factor of the grade of PNENs. We also identified several independent risk factors (sex, age and size) to develop a nomogram model for predicting the grade of PNENs. Our data showed that nomograms based on clinical information may be a powerful numerical tool for predicting the grade of PNENs. Further studies are needed to confirm our nomogram model.

Funding

This study was funded by Jiangsu Provincial Hospital of Chinese Medicine Peak Academic Talent Project (NO: y2018rc04) and Medical development and Medical Assistance Foundation of Jiangsu Province Hospital of Chinese Medicine.

Author contributions

Conception: Xiao Chen and Zhongqiu Wang.

Interpretation or analysis of data: Cheng Wang, Tingting Lin, Xin Chen and Wenjing Cui.

Preparation of the manuscript: Cheng Wang, Tingting Lin and Xin Chen.

Revision for important intellectual content: Xiao Chen, Xin Chen and Zhongqiu Wang.

Supervision: Xiao Chen.

Footnotes

Acknowledgments

None.

Conflict of interest

The authors declare that they have no conflict of interests.

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The association between pain and WHO grade of pancreatic neuroendocrine neoplasms: A multicenter study

Abstract

BACKGROUND:

OBJECTIVE:

METHODS:

RESULTS:

CONCLUSION:

Keywords

Abbreviations

1. Introduction

2. Materials and methods

2.1 Patients

2.2 Histopathologic analysis

Table 1 The characteristic of subjects with or without abdominal pain

3. Results

3.1 Patients characteristics

3.2 The association between pain and PNENs grade

Table 2 The association between pain and G2/G3 grade of pancreatic neuroendocrine neoplasms

Funding

Author contributions

Footnotes

Acknowledgments

Conflict of interest

References

Table 1
The characteristic of subjects with or without abdominal pain

Table 2
The association between pain and G2/G3 grade of pancreatic neuroendocrine neoplasms