Cargando…

A nomogram to predict vascular invasion before resection of colorectal cancer

Vascular invasion (VI) is an important feature for systemic recurrence and an indicator for the application of adjuvant therapy in colorectal cancer (CRC). Preoperative knowledge of VI is important in determining whether adjuvant therapy is necessary, as well as the adequacy of surgical resection. I...

Descripción completa

Detalles Bibliográficos
Autores principales: Xie, Weishun, Liu, Jungang, Huang, Xiaoliang, Wu, Guo, Jeen, Franco, Chen, Shaomei, Zhang, Chuqiao, Yang, Wenkang, Li, Chan, Li, Zhengtian, Ge, Lianying, Tang, Weizhong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: D.A. Spandidos 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6865036/
https://www.ncbi.nlm.nih.gov/pubmed/31788051
http://dx.doi.org/10.3892/ol.2019.10937
_version_ 1783472015489892352
author Xie, Weishun
Liu, Jungang
Huang, Xiaoliang
Wu, Guo
Jeen, Franco
Chen, Shaomei
Zhang, Chuqiao
Yang, Wenkang
Li, Chan
Li, Zhengtian
Ge, Lianying
Tang, Weizhong
author_facet Xie, Weishun
Liu, Jungang
Huang, Xiaoliang
Wu, Guo
Jeen, Franco
Chen, Shaomei
Zhang, Chuqiao
Yang, Wenkang
Li, Chan
Li, Zhengtian
Ge, Lianying
Tang, Weizhong
author_sort Xie, Weishun
collection PubMed
description Vascular invasion (VI) is an important feature for systemic recurrence and an indicator for the application of adjuvant therapy in colorectal cancer (CRC). Preoperative knowledge of VI is important in determining whether adjuvant therapy is necessary, as well as the adequacy of surgical resection. In the present study, a predictive nomogram for VI in patients with CRC was constructed. The prediction model consisted of 664 eligible patients with CRC, who were divided into a training set (n=468) and a validation set (n=196). Data were collected between August 2013 and April 2018. The feature selection model was established using the least absolute shrinkage and selection operator regression model. Multivariable logistic regression analysis was used to construct the predictive nomogram. The performance of the nomogram was evaluated by calibration, discrimination and clinical usefulness. Differentiation, computed tomography (CT)-based on N stage (CT N stage), hemameba and tumor distance from the anus (cm) were integrated into the nomogram. The nomogram exhibited good discrimination, with an area under the curve (AUC) of 0.731 and good calibration. Application of the nomogram in the validation cohort showed acceptable discrimination, with an AUC of 0.710 and good calibration. Decision curve analysis revealed that the nomogram was clinically useful. These findings suggests, to the best of our knowledge, that this may be the first nomogram for individual preoperative prediction of VI in patients with CRC, which may promote preoperative optimization strategies for this selected group of patients.
format Online
Article
Text
id pubmed-6865036
institution National Center for Biotechnology Information
language English
publishDate 2019
publisher D.A. Spandidos
record_format MEDLINE/PubMed
spelling pubmed-68650362019-11-30 A nomogram to predict vascular invasion before resection of colorectal cancer Xie, Weishun Liu, Jungang Huang, Xiaoliang Wu, Guo Jeen, Franco Chen, Shaomei Zhang, Chuqiao Yang, Wenkang Li, Chan Li, Zhengtian Ge, Lianying Tang, Weizhong Oncol Lett Articles Vascular invasion (VI) is an important feature for systemic recurrence and an indicator for the application of adjuvant therapy in colorectal cancer (CRC). Preoperative knowledge of VI is important in determining whether adjuvant therapy is necessary, as well as the adequacy of surgical resection. In the present study, a predictive nomogram for VI in patients with CRC was constructed. The prediction model consisted of 664 eligible patients with CRC, who were divided into a training set (n=468) and a validation set (n=196). Data were collected between August 2013 and April 2018. The feature selection model was established using the least absolute shrinkage and selection operator regression model. Multivariable logistic regression analysis was used to construct the predictive nomogram. The performance of the nomogram was evaluated by calibration, discrimination and clinical usefulness. Differentiation, computed tomography (CT)-based on N stage (CT N stage), hemameba and tumor distance from the anus (cm) were integrated into the nomogram. The nomogram exhibited good discrimination, with an area under the curve (AUC) of 0.731 and good calibration. Application of the nomogram in the validation cohort showed acceptable discrimination, with an AUC of 0.710 and good calibration. Decision curve analysis revealed that the nomogram was clinically useful. These findings suggests, to the best of our knowledge, that this may be the first nomogram for individual preoperative prediction of VI in patients with CRC, which may promote preoperative optimization strategies for this selected group of patients. D.A. Spandidos 2019-12 2019-09-30 /pmc/articles/PMC6865036/ /pubmed/31788051 http://dx.doi.org/10.3892/ol.2019.10937 Text en Copyright: © Xie et al. This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License (https://creativecommons.org/licenses/by-nc-nd/4.0/) , which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
spellingShingle Articles
Xie, Weishun
Liu, Jungang
Huang, Xiaoliang
Wu, Guo
Jeen, Franco
Chen, Shaomei
Zhang, Chuqiao
Yang, Wenkang
Li, Chan
Li, Zhengtian
Ge, Lianying
Tang, Weizhong
A nomogram to predict vascular invasion before resection of colorectal cancer
title A nomogram to predict vascular invasion before resection of colorectal cancer
title_full A nomogram to predict vascular invasion before resection of colorectal cancer
title_fullStr A nomogram to predict vascular invasion before resection of colorectal cancer
title_full_unstemmed A nomogram to predict vascular invasion before resection of colorectal cancer
title_short A nomogram to predict vascular invasion before resection of colorectal cancer
title_sort nomogram to predict vascular invasion before resection of colorectal cancer
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6865036/
https://www.ncbi.nlm.nih.gov/pubmed/31788051
http://dx.doi.org/10.3892/ol.2019.10937
work_keys_str_mv AT xieweishun anomogramtopredictvascularinvasionbeforeresectionofcolorectalcancer
AT liujungang anomogramtopredictvascularinvasionbeforeresectionofcolorectalcancer
AT huangxiaoliang anomogramtopredictvascularinvasionbeforeresectionofcolorectalcancer
AT wuguo anomogramtopredictvascularinvasionbeforeresectionofcolorectalcancer
AT jeenfranco anomogramtopredictvascularinvasionbeforeresectionofcolorectalcancer
AT chenshaomei anomogramtopredictvascularinvasionbeforeresectionofcolorectalcancer
AT zhangchuqiao anomogramtopredictvascularinvasionbeforeresectionofcolorectalcancer
AT yangwenkang anomogramtopredictvascularinvasionbeforeresectionofcolorectalcancer
AT lichan anomogramtopredictvascularinvasionbeforeresectionofcolorectalcancer
AT lizhengtian anomogramtopredictvascularinvasionbeforeresectionofcolorectalcancer
AT gelianying anomogramtopredictvascularinvasionbeforeresectionofcolorectalcancer
AT tangweizhong anomogramtopredictvascularinvasionbeforeresectionofcolorectalcancer
AT xieweishun nomogramtopredictvascularinvasionbeforeresectionofcolorectalcancer
AT liujungang nomogramtopredictvascularinvasionbeforeresectionofcolorectalcancer
AT huangxiaoliang nomogramtopredictvascularinvasionbeforeresectionofcolorectalcancer
AT wuguo nomogramtopredictvascularinvasionbeforeresectionofcolorectalcancer
AT jeenfranco nomogramtopredictvascularinvasionbeforeresectionofcolorectalcancer
AT chenshaomei nomogramtopredictvascularinvasionbeforeresectionofcolorectalcancer
AT zhangchuqiao nomogramtopredictvascularinvasionbeforeresectionofcolorectalcancer
AT yangwenkang nomogramtopredictvascularinvasionbeforeresectionofcolorectalcancer
AT lichan nomogramtopredictvascularinvasionbeforeresectionofcolorectalcancer
AT lizhengtian nomogramtopredictvascularinvasionbeforeresectionofcolorectalcancer
AT gelianying nomogramtopredictvascularinvasionbeforeresectionofcolorectalcancer
AT tangweizhong nomogramtopredictvascularinvasionbeforeresectionofcolorectalcancer