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Short-term prognosis for hepatocellular carcinoma patients with lung metastasis: A retrospective cohort study based on the SEER database
Our study aimed to develop a prediction model to predict the short-term mortality of hepatocellular carcinoma (HCC) patients with lung metastasis. The retrospective data of HCC patients with lung metastasis was from the Surveillance, Epidemiology, and End Results registration database between 2010 a...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Lippincott Williams & Wilkins
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9666127/ https://www.ncbi.nlm.nih.gov/pubmed/36397445 http://dx.doi.org/10.1097/MD.0000000000031399 |
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author | Chen, Shicheng Li, Xiaowen Liang, Yichao Lu, Xinyu Huang, Yingyi Zhu, Jiajia Li, Jun |
author_facet | Chen, Shicheng Li, Xiaowen Liang, Yichao Lu, Xinyu Huang, Yingyi Zhu, Jiajia Li, Jun |
author_sort | Chen, Shicheng |
collection | PubMed |
description | Our study aimed to develop a prediction model to predict the short-term mortality of hepatocellular carcinoma (HCC) patients with lung metastasis. The retrospective data of HCC patients with lung metastasis was from the Surveillance, Epidemiology, and End Results registration database between 2010 and 2015. 1905 patients were randomly divided into training set (n = 1333) and validation set (n = 572). There were 1092 patients extracted from the Surveillance, Epidemiology, and End Results database 2015 to 2019 as the validation set. The variable importance was calculated to screen predictors. The constructed prediction models of logistic regression, random forest, broad learning system, deep neural network, support vector machine, and naïve Bayes were compared through the predictive performance. The mortality of HCC patients with lung metastasis was 51.65% within 1 month. The screened prognostic factors (age, N stage, T stage, tumor size, surgery, grade, radiation, and chemotherapy) and gender were used to construct prediction models. The area under curve (0.853 vs. 0.771) of random forest model was more optimized than that of logistic regression model in the training set. But, there were no significant differences in testing and validation sets between random forest and logistic regression models. The value of area under curve in the logistic regression model was significantly higher than that of the broad learning system model (0.763 vs. 0.745), support vector machine model (0.763 vs. 0.689) in the validation set, and higher than that of the naïve Bayes model (0.775 vs. 0.744) in the testing model. We further chose the logistic regression prediction model and built the prognostic nomogram. We have developed a prediction model for predicting short-term mortality with 9 easily acquired predictors of HCC patients with lung metastasis, which performed well in the internal and external validation. It could assist clinicians to adjust treatment strategies in time to improve the prognosis. |
format | Online Article Text |
id | pubmed-9666127 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Lippincott Williams & Wilkins |
record_format | MEDLINE/PubMed |
spelling | pubmed-96661272022-11-16 Short-term prognosis for hepatocellular carcinoma patients with lung metastasis: A retrospective cohort study based on the SEER database Chen, Shicheng Li, Xiaowen Liang, Yichao Lu, Xinyu Huang, Yingyi Zhu, Jiajia Li, Jun Medicine (Baltimore) 5700 Our study aimed to develop a prediction model to predict the short-term mortality of hepatocellular carcinoma (HCC) patients with lung metastasis. The retrospective data of HCC patients with lung metastasis was from the Surveillance, Epidemiology, and End Results registration database between 2010 and 2015. 1905 patients were randomly divided into training set (n = 1333) and validation set (n = 572). There were 1092 patients extracted from the Surveillance, Epidemiology, and End Results database 2015 to 2019 as the validation set. The variable importance was calculated to screen predictors. The constructed prediction models of logistic regression, random forest, broad learning system, deep neural network, support vector machine, and naïve Bayes were compared through the predictive performance. The mortality of HCC patients with lung metastasis was 51.65% within 1 month. The screened prognostic factors (age, N stage, T stage, tumor size, surgery, grade, radiation, and chemotherapy) and gender were used to construct prediction models. The area under curve (0.853 vs. 0.771) of random forest model was more optimized than that of logistic regression model in the training set. But, there were no significant differences in testing and validation sets between random forest and logistic regression models. The value of area under curve in the logistic regression model was significantly higher than that of the broad learning system model (0.763 vs. 0.745), support vector machine model (0.763 vs. 0.689) in the validation set, and higher than that of the naïve Bayes model (0.775 vs. 0.744) in the testing model. We further chose the logistic regression prediction model and built the prognostic nomogram. We have developed a prediction model for predicting short-term mortality with 9 easily acquired predictors of HCC patients with lung metastasis, which performed well in the internal and external validation. It could assist clinicians to adjust treatment strategies in time to improve the prognosis. Lippincott Williams & Wilkins 2022-11-11 /pmc/articles/PMC9666127/ /pubmed/36397445 http://dx.doi.org/10.1097/MD.0000000000031399 Text en Copyright © 2022 the Author(s). Published by Wolters Kluwer Health, Inc. https://creativecommons.org/licenses/by-nc/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial License 4.0 (CCBY-NC) (https://creativecommons.org/licenses/by-nc/4.0/) , where it is permissible to download, share, remix, transform, and buildup the work provided it is properly cited. The work cannot be used commercially without permission from the journal. |
spellingShingle | 5700 Chen, Shicheng Li, Xiaowen Liang, Yichao Lu, Xinyu Huang, Yingyi Zhu, Jiajia Li, Jun Short-term prognosis for hepatocellular carcinoma patients with lung metastasis: A retrospective cohort study based on the SEER database |
title | Short-term prognosis for hepatocellular carcinoma patients with lung metastasis: A retrospective cohort study based on the SEER database |
title_full | Short-term prognosis for hepatocellular carcinoma patients with lung metastasis: A retrospective cohort study based on the SEER database |
title_fullStr | Short-term prognosis for hepatocellular carcinoma patients with lung metastasis: A retrospective cohort study based on the SEER database |
title_full_unstemmed | Short-term prognosis for hepatocellular carcinoma patients with lung metastasis: A retrospective cohort study based on the SEER database |
title_short | Short-term prognosis for hepatocellular carcinoma patients with lung metastasis: A retrospective cohort study based on the SEER database |
title_sort | short-term prognosis for hepatocellular carcinoma patients with lung metastasis: a retrospective cohort study based on the seer database |
topic | 5700 |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9666127/ https://www.ncbi.nlm.nih.gov/pubmed/36397445 http://dx.doi.org/10.1097/MD.0000000000031399 |
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