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A predictive nomogram for lymph node metastasis of incidental gallbladder cancer: a SEER population-based study

BACKGROUND: Existing imaging techniques have a low ability to detect lymph node metastasis (LNM) of gallbladder cancer (GBC). Gallbladder removal by laparoscopic cholecystectomy can provide pathological information regarding the tumor itself for incidental gallbladder cancer (IGBC). The purpose of t...

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Autores principales: Yang, Yingnan, Tu, Zhuolong, Cai, Huajie, Hu, Bingren, Ye, Chentao, Tu, Jinfu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7461264/
https://www.ncbi.nlm.nih.gov/pubmed/32867722
http://dx.doi.org/10.1186/s12885-020-07341-y
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author Yang, Yingnan
Tu, Zhuolong
Cai, Huajie
Hu, Bingren
Ye, Chentao
Tu, Jinfu
author_facet Yang, Yingnan
Tu, Zhuolong
Cai, Huajie
Hu, Bingren
Ye, Chentao
Tu, Jinfu
author_sort Yang, Yingnan
collection PubMed
description BACKGROUND: Existing imaging techniques have a low ability to detect lymph node metastasis (LNM) of gallbladder cancer (GBC). Gallbladder removal by laparoscopic cholecystectomy can provide pathological information regarding the tumor itself for incidental gallbladder cancer (IGBC). The purpose of this study was to identify the risk factors associated with LNM of IGBC and to establish a nomogram to improve the ability to predict the risk of LNM for IGBC. METHODS: A total of 796 patients diagnosed with stage T1/2 GBC between 2004 and 2015 who underwent surgery and lymph node evaluation were enrolled in this study. We randomly divided the dataset into a training set (70%) and a validation set (30%). A logistic regression model was used to construct the nomogram in the training set and then was verified in the validation set. Nomogram performance was quantified with respect to discrimination and calibration. RESULTS: The rates of LNM in T1a, T1b and T2 patients were 7, 11.1 and 44.3%, respectively. Tumor diameter, T stage, and tumor differentiation were independent factors affecting LNM. The C-index and AUC of the training set were 0.718 (95% CI, 0.676–0.760) and 0.702 (95% CI, 0.659–0.702), respectively, demonstrating good prediction performance. The calibration curves showed perfect agreement between the nomogram predictions and actual observations. Decision curve analysis showed that the LNM nomogram was clinically useful when the risk was decided at a possibility threshold of 2–63%. The C-index and AUC of the validation set were 0.73 (95% CI: 0.665–0.795) and 0.692 (95% CI: 0.625–0.759), respectively. CONCLUSION: The nomogram established in this study has good prediction ability. For patients with IGBC requiring re-resection, the model can effectively predict the risk of LNM and make up for the inaccuracy of imaging.
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spelling pubmed-74612642020-09-02 A predictive nomogram for lymph node metastasis of incidental gallbladder cancer: a SEER population-based study Yang, Yingnan Tu, Zhuolong Cai, Huajie Hu, Bingren Ye, Chentao Tu, Jinfu BMC Cancer Research Article BACKGROUND: Existing imaging techniques have a low ability to detect lymph node metastasis (LNM) of gallbladder cancer (GBC). Gallbladder removal by laparoscopic cholecystectomy can provide pathological information regarding the tumor itself for incidental gallbladder cancer (IGBC). The purpose of this study was to identify the risk factors associated with LNM of IGBC and to establish a nomogram to improve the ability to predict the risk of LNM for IGBC. METHODS: A total of 796 patients diagnosed with stage T1/2 GBC between 2004 and 2015 who underwent surgery and lymph node evaluation were enrolled in this study. We randomly divided the dataset into a training set (70%) and a validation set (30%). A logistic regression model was used to construct the nomogram in the training set and then was verified in the validation set. Nomogram performance was quantified with respect to discrimination and calibration. RESULTS: The rates of LNM in T1a, T1b and T2 patients were 7, 11.1 and 44.3%, respectively. Tumor diameter, T stage, and tumor differentiation were independent factors affecting LNM. The C-index and AUC of the training set were 0.718 (95% CI, 0.676–0.760) and 0.702 (95% CI, 0.659–0.702), respectively, demonstrating good prediction performance. The calibration curves showed perfect agreement between the nomogram predictions and actual observations. Decision curve analysis showed that the LNM nomogram was clinically useful when the risk was decided at a possibility threshold of 2–63%. The C-index and AUC of the validation set were 0.73 (95% CI: 0.665–0.795) and 0.692 (95% CI: 0.625–0.759), respectively. CONCLUSION: The nomogram established in this study has good prediction ability. For patients with IGBC requiring re-resection, the model can effectively predict the risk of LNM and make up for the inaccuracy of imaging. BioMed Central 2020-08-31 /pmc/articles/PMC7461264/ /pubmed/32867722 http://dx.doi.org/10.1186/s12885-020-07341-y Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research Article
Yang, Yingnan
Tu, Zhuolong
Cai, Huajie
Hu, Bingren
Ye, Chentao
Tu, Jinfu
A predictive nomogram for lymph node metastasis of incidental gallbladder cancer: a SEER population-based study
title A predictive nomogram for lymph node metastasis of incidental gallbladder cancer: a SEER population-based study
title_full A predictive nomogram for lymph node metastasis of incidental gallbladder cancer: a SEER population-based study
title_fullStr A predictive nomogram for lymph node metastasis of incidental gallbladder cancer: a SEER population-based study
title_full_unstemmed A predictive nomogram for lymph node metastasis of incidental gallbladder cancer: a SEER population-based study
title_short A predictive nomogram for lymph node metastasis of incidental gallbladder cancer: a SEER population-based study
title_sort predictive nomogram for lymph node metastasis of incidental gallbladder cancer: a seer population-based study
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7461264/
https://www.ncbi.nlm.nih.gov/pubmed/32867722
http://dx.doi.org/10.1186/s12885-020-07341-y
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