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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...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
D.A. Spandidos
2019
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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 |
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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 |
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