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Developing a competing risk nomogram that predicts the survival of patients with a primary hepatic neuroendocrine tumor

Primary hepatic neuroendocrine tumor (PHNET) is rare liver cancer and related prognostic factors are unclear. The aim of this study was to analyze the prognostic risk factors of patients with PHNETs and establish an assessment model for prognosis. The clinical information of 539 patients with PHNETs...

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Autores principales: Lin, Jianyang, Li, Xiang, Ding, Xin, Chen, Zhihong, Wu, Yinyan, Zhao, Kun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9679223/
https://www.ncbi.nlm.nih.gov/pubmed/36425110
http://dx.doi.org/10.3389/fmed.2022.960235
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author Lin, Jianyang
Li, Xiang
Ding, Xin
Chen, Zhihong
Wu, Yinyan
Zhao, Kun
author_facet Lin, Jianyang
Li, Xiang
Ding, Xin
Chen, Zhihong
Wu, Yinyan
Zhao, Kun
author_sort Lin, Jianyang
collection PubMed
description Primary hepatic neuroendocrine tumor (PHNET) is rare liver cancer and related prognostic factors are unclear. The aim of this study was to analyze the prognostic risk factors of patients with PHNETs and establish an assessment model for prognosis. The clinical information of 539 patients with PHNETs who met the criteria for inclusion was extracted from the Surveillance, Epidemiology, and End Results (SEER) database. These patients were randomly assigned to the training (269 cases) and validation sets (270 cases). Prognostic factors in patients with PHNETs were screened using the Cox proportional regression model and Fine–Gray competing risk model. Based on the training set analysis using the Fine–Gray competing risk model, a nomogram was constructed to predict cumulative probabilities for PHNET-specific death. The performance of the nomogram was measured by using receiver operating characteristic curves, the concordance index (C-index), calibration curves, and decision curve analysis (DCA). No differences in clinical baseline characteristics between the training and validation sets were observed, and the Fine–Gray analysis showed that surgery and more than one primary malignancy were associated with a low cumulative probability of PHNET-specific death. The training set nomograms were well-calibrated and had good discriminative ability, and good agreement between predicted and observed survival was observed. Patients with PHNETs with a high-risk score had a significantly increased risk of PHNET-specific death and non-PHNET death. Surgical treatment and the number of primary malignancies were found to be independent protective factors for PHNETs. The competing risk nomogram has high accuracy in predicting disease-specific survival (DSS) for patients with PHNETs, which may help clinicians to develop individualized treatment strategies.
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spelling pubmed-96792232022-11-23 Developing a competing risk nomogram that predicts the survival of patients with a primary hepatic neuroendocrine tumor Lin, Jianyang Li, Xiang Ding, Xin Chen, Zhihong Wu, Yinyan Zhao, Kun Front Med (Lausanne) Medicine Primary hepatic neuroendocrine tumor (PHNET) is rare liver cancer and related prognostic factors are unclear. The aim of this study was to analyze the prognostic risk factors of patients with PHNETs and establish an assessment model for prognosis. The clinical information of 539 patients with PHNETs who met the criteria for inclusion was extracted from the Surveillance, Epidemiology, and End Results (SEER) database. These patients were randomly assigned to the training (269 cases) and validation sets (270 cases). Prognostic factors in patients with PHNETs were screened using the Cox proportional regression model and Fine–Gray competing risk model. Based on the training set analysis using the Fine–Gray competing risk model, a nomogram was constructed to predict cumulative probabilities for PHNET-specific death. The performance of the nomogram was measured by using receiver operating characteristic curves, the concordance index (C-index), calibration curves, and decision curve analysis (DCA). No differences in clinical baseline characteristics between the training and validation sets were observed, and the Fine–Gray analysis showed that surgery and more than one primary malignancy were associated with a low cumulative probability of PHNET-specific death. The training set nomograms were well-calibrated and had good discriminative ability, and good agreement between predicted and observed survival was observed. Patients with PHNETs with a high-risk score had a significantly increased risk of PHNET-specific death and non-PHNET death. Surgical treatment and the number of primary malignancies were found to be independent protective factors for PHNETs. The competing risk nomogram has high accuracy in predicting disease-specific survival (DSS) for patients with PHNETs, which may help clinicians to develop individualized treatment strategies. Frontiers Media S.A. 2022-11-08 /pmc/articles/PMC9679223/ /pubmed/36425110 http://dx.doi.org/10.3389/fmed.2022.960235 Text en Copyright © 2022 Lin, Li, Ding, Chen, Wu and Zhao. 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 Medicine
Lin, Jianyang
Li, Xiang
Ding, Xin
Chen, Zhihong
Wu, Yinyan
Zhao, Kun
Developing a competing risk nomogram that predicts the survival of patients with a primary hepatic neuroendocrine tumor
title Developing a competing risk nomogram that predicts the survival of patients with a primary hepatic neuroendocrine tumor
title_full Developing a competing risk nomogram that predicts the survival of patients with a primary hepatic neuroendocrine tumor
title_fullStr Developing a competing risk nomogram that predicts the survival of patients with a primary hepatic neuroendocrine tumor
title_full_unstemmed Developing a competing risk nomogram that predicts the survival of patients with a primary hepatic neuroendocrine tumor
title_short Developing a competing risk nomogram that predicts the survival of patients with a primary hepatic neuroendocrine tumor
title_sort developing a competing risk nomogram that predicts the survival of patients with a primary hepatic neuroendocrine tumor
topic Medicine
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9679223/
https://www.ncbi.nlm.nih.gov/pubmed/36425110
http://dx.doi.org/10.3389/fmed.2022.960235
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