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Prognostic Nomogram Based on the Metastatic Lymph Node Ratio for T(1-4)N(0-1)M(0) Pancreatic Neuroendocrine Tumors After Surgery
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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9092648/ https://www.ncbi.nlm.nih.gov/pubmed/35574346 http://dx.doi.org/10.3389/fonc.2022.899759 |
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author | Shi, Jingxiang Liu, Sifan Cao, Jisen Shan, Shigang Ren, Chaoyi Zhang, Jinjuan Wang, Yijun |
author_facet | Shi, Jingxiang Liu, Sifan Cao, Jisen Shan, Shigang Ren, Chaoyi Zhang, Jinjuan Wang, Yijun |
author_sort | Shi, Jingxiang |
collection | PubMed |
description | 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 T(1-4)N(0-1)M(0) 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. |
format | Online Article Text |
id | pubmed-9092648 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-90926482022-05-12 Prognostic Nomogram Based on the Metastatic Lymph Node Ratio for T(1-4)N(0-1)M(0) Pancreatic Neuroendocrine Tumors After Surgery Shi, Jingxiang Liu, Sifan Cao, Jisen Shan, Shigang Ren, Chaoyi Zhang, Jinjuan Wang, Yijun Front Oncol Oncology 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 T(1-4)N(0-1)M(0) 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. Frontiers Media S.A. 2022-04-27 /pmc/articles/PMC9092648/ /pubmed/35574346 http://dx.doi.org/10.3389/fonc.2022.899759 Text en Copyright © 2022 Shi, Liu, Cao, Shan, Ren, Zhang and Wang https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Oncology Shi, Jingxiang Liu, Sifan Cao, Jisen Shan, Shigang Ren, Chaoyi Zhang, Jinjuan Wang, Yijun Prognostic Nomogram Based on the Metastatic Lymph Node Ratio for T(1-4)N(0-1)M(0) Pancreatic Neuroendocrine Tumors After Surgery |
title | Prognostic Nomogram Based on the Metastatic Lymph Node Ratio for T(1-4)N(0-1)M(0) Pancreatic Neuroendocrine Tumors After Surgery |
title_full | Prognostic Nomogram Based on the Metastatic Lymph Node Ratio for T(1-4)N(0-1)M(0) Pancreatic Neuroendocrine Tumors After Surgery |
title_fullStr | Prognostic Nomogram Based on the Metastatic Lymph Node Ratio for T(1-4)N(0-1)M(0) Pancreatic Neuroendocrine Tumors After Surgery |
title_full_unstemmed | Prognostic Nomogram Based on the Metastatic Lymph Node Ratio for T(1-4)N(0-1)M(0) Pancreatic Neuroendocrine Tumors After Surgery |
title_short | Prognostic Nomogram Based on the Metastatic Lymph Node Ratio for T(1-4)N(0-1)M(0) Pancreatic Neuroendocrine Tumors After Surgery |
title_sort | prognostic nomogram based on the metastatic lymph node ratio for t(1-4)n(0-1)m(0) pancreatic neuroendocrine tumors after surgery |
topic | Oncology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9092648/ https://www.ncbi.nlm.nih.gov/pubmed/35574346 http://dx.doi.org/10.3389/fonc.2022.899759 |
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