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. 2022 Apr 27:12:899759.
doi: 10.3389/fonc.2022.899759. eCollection 2022.

Prognostic Nomogram Based on the Metastatic Lymph Node Ratio for T1-4N0-1M0 Pancreatic Neuroendocrine Tumors After Surgery

Jingxiang Shi  1   2   3   4 , Sifan Liu  5 , Jisen Cao  1   2   3   4 , Shigang Shan  1   2   3   4 , Chaoyi Ren  1   2   3   4 , Jinjuan Zhang  1   2   3   4 , Yijun Wang  1   2   3   4
Affiliations

Prognostic Nomogram Based on the Metastatic Lymph Node Ratio for T1-4N0-1M0 Pancreatic Neuroendocrine Tumors After Surgery

Jingxiang Shi et al. Front Oncol. .

Abstract

Purpose: This study aimed to investigate the prognostic significance of the metastatic lymph node ratio (LNR) in patients with pancreatic neuroendocrine tumors (pNETs) and to develop and validate nomograms to predict 5-, 7-, and 10-year overall survival (OS) and cancer-specific survival (CSS) rates for pNETs after surgical resection.

Methods: The demographics and clinicopathological information of T1-4N0-1M0 pNET patients between 2004 and 2018 were extracted from the Surveillance, Epidemiology and End Results database. X-tile software was used to determine the best cutoff value for the LNR. Patients were randomly divided into the training and the validation groups. A Cox regression model was used in the training group to obtain independent prognostic factors to develop nomograms for predicting OS and CSS. The concordance index (C-index), calibration curves, area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA) were used to assess the nomograms. Patients were divided into four groups according to the model scores, and their survival curves were generated by the Kaplan-Meier method.

Results: A total of 806 patients were included in this study. The best cutoff value for the LNR was 0.16. The LNR was negatively correlated with both OS and CSS. Age, sex, marital status, primary site, grade, the LNR and radiotherapy were used to construct OS and CSS nomograms. In the training group, the C-index was 0.771 for OS and 0.778 for CSS. In the validation group, the C-index was 0.737 for OS and 0.727 for CSS. The calibration curves and AUC also indicated their good predictability. DCA demonstrated that the nomograms displayed better performance than the American Joint Committee on Cancer (AJCC) TNM staging system (8th edition). Risk stratification indicated that patients with higher risk had a worse prognosis.

Conclusions: The LNR is an independent negative prognostic factor for pNETs. The nomograms we built can accurately predict long-term survival for pNETs after surgery.

Keywords: cancer-specific survival; lymph node ratio; nomogram; overall survival; pancreatic neuroendocrine tumors.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
This is the flow chart of enrolled patients.
Figure 2
Figure 2
Kaplan–Meier curves of OS (A) and CSS (B) for patients with different LNRs in the overall dataset. OS, overall survival; CSS, cancer-specific survival; LNR, lymph node ratio.
Figure 3
Figure 3
(A) Nomogram used to predict the 5-, 7- and 10-year OS rates of patients with T1-4N0-1M0 pNETs after surgery. (B) Nomogram used to predict the 5-, 7- and 10-year CSS rates of patients with T1-4N0-1M0 pNETs after surgery. OS, overall survival; pNETs, pancreatic neuroendocrine tumors; CSS, cancer-specific survival.
Figure 4
Figure 4
Correlations between variables in the overall dataset (A), the training group (B) and the validation group (C).
Figure 5
Figure 5
Calibration curve of the nomogram for OS prediction from the training group (A) and the validation group (E). Decision curve analysis of the AJCC 8th edition staging system, nomogram and the LNR for the 5- (B), 7- (C) and 10-year (D) OS rates of patients with T1-4N0-1M0 pNETs from the training group. Decision curve analysis of the AJCC 8th edition staging system, nomogram and the LNR for the 5- (F), 7- (G) and 10-year (H) OS rates of patients with T1-4N0-1M0 pNETs from the validation group. OS, overall survival; LNR, lymph node ratio; pNETs, pancreatic neuroendocrine tumors. For calibration curves, red, blue and green lines represent 5, 7, and 10 years, respectively.
Figure 6
Figure 6
Calibration curve of the nomogram for CSS prediction from the training group (A) and the validation group (E). Decision curve analysis of the AJCC 8th edition staging system, nomogram and the LNR for the 5- (B), 7- (C) and 10-year (D) CSS rates of patients with T1-4N0-1M0 pNETs from the training group. Decision curve analysis of the AJCC 8th edition staging system, nomogram and the LNR for the 5- (F), 7- (G) and 10-year (H) CSS rates of patients with T1-4N0-1M0 pNETs from the validation group. CSS, cancer-specific survival; LNR, lymph node ratio; pNETs, pancreatic neuroendocrine tumors. For calibration curves, red, blue and green lines represent 5, 7, and 10 years, respectively.
Figure 7
Figure 7
Receiver operating characteristic curves (ROCs) of the nomogram for OS prediction (A, training group; B, validation group) and CSS prediction (C, training group; D, validation group). OS, overall survival; CSS, cancer-specific survival; AUC, area under the receiver operating characteristic curve. For ROCs, red represents 5 years, blue represents 7 years and yellow represents 10 years.
Figure 8
Figure 8
Kaplan–Meier curves of risk stratification within the training group for OS (A) and CSS (C) and within the validation group for OS (B) and CSS (D). OS, overall survival; CSS, cancer-specific survival.
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References

    1. Halfdanarson TR, Rabe KG, Rubin J, Petersen GM. Pancreatic Neuroendocrine Tumors (PNETs): Incidence, Prognosis and Recent Trend Toward Improved Survival. Ann Oncol (2008) 19:1727–33. doi: 10.1093/annonc/mdn351 - DOI - PMC - PubMed
    1. Vaghaiwalla T, Keutgen XM. Surgical Management of Pancreatic Neuroendocrine Tumors. Surg Oncol Clin N Am (2020) 29:243–52. doi: 10.1016/j.soc.2019.11.008 - DOI - PubMed
    1. Dasari A, Shen C, Halperin D, Zhao B, Zhou S, Xu Y, et al. . Trends in the Incidence, Prevalence, and Survival Outcomes in Patients With Neuroendocrine Tumors in the United States. JAMA Oncol (2017) 3:1335–42. doi: 10.1001/jamaoncol.2017.0589 - DOI - PMC - PubMed
    1. Zhang Z, Liu M, Ji S, Luo G, Xu W, Liu W, et al. . Prognostic Value and Clinical Predictors of Lymph Node Metastases in Pancreatic Neuroendocrine Tumors. Pancreas (2020) 49:381–6. doi: 10.1097/MPA.0000000000001493 - DOI - PubMed
    1. Fathi AH, Romanyshyn J, Barati M, Choudhury U, Chen A, Sosa JA. Predicting Aggressive Behavior in Nonfunctional Pancreatic Neuroendocrine Tumors With Emphasis on Tumor Size Significance and Survival Trends: A Population-Based Analysis of 1787 Patients. Am Surg (2020) 86:458–66. doi: 10.1177/0003134820919730 - DOI - PubMed

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