Cargando…
Support Vector Machines Model of Computed Tomography for Assessing Lymph Node Metastasis in Esophageal Cancer with Neoadjuvant Chemotherapy
OBJECTIVE: The aim of this study was to diagnose lymph node metastasis of esophageal cancer by support vector machines model based on computed tomography. MATERIALS AND METHODS: A total of 131 esophageal cancer patients with preoperative chemotherapy and radical surgery were included. Various indica...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Lippincott Williams & Wilkins
2017
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5457826/ https://www.ncbi.nlm.nih.gov/pubmed/27879527 http://dx.doi.org/10.1097/RCT.0000000000000555 |
_version_ | 1783241617427136512 |
---|---|
author | Wang, Zhi-Long Zhou, Zhi-Guo Chen, Ying Li, Xiao-Ting Sun, Ying-Shi |
author_facet | Wang, Zhi-Long Zhou, Zhi-Guo Chen, Ying Li, Xiao-Ting Sun, Ying-Shi |
author_sort | Wang, Zhi-Long |
collection | PubMed |
description | OBJECTIVE: The aim of this study was to diagnose lymph node metastasis of esophageal cancer by support vector machines model based on computed tomography. MATERIALS AND METHODS: A total of 131 esophageal cancer patients with preoperative chemotherapy and radical surgery were included. Various indicators (tumor thickness, tumor length, tumor CT value, total number of lymph nodes, and long axis and short axis sizes of largest lymph node) on CT images before and after neoadjuvant chemotherapy were recorded. A support vector machines model based on these CT indicators was built to predict lymph node metastasis. RESULTS: Support vector machines model diagnosed lymph node metastasis better than preoperative short axis size of largest lymph node on CT. The area under the receiver operating characteristic curves were 0.887 and 0.705, respectively. CONCLUSIONS: The support vector machine model of CT images can help diagnose lymph node metastasis in esophageal cancer with preoperative chemotherapy. |
format | Online Article Text |
id | pubmed-5457826 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Lippincott Williams & Wilkins |
record_format | MEDLINE/PubMed |
spelling | pubmed-54578262017-06-13 Support Vector Machines Model of Computed Tomography for Assessing Lymph Node Metastasis in Esophageal Cancer with Neoadjuvant Chemotherapy Wang, Zhi-Long Zhou, Zhi-Guo Chen, Ying Li, Xiao-Ting Sun, Ying-Shi J Comput Assist Tomogr Thoracic Imaging OBJECTIVE: The aim of this study was to diagnose lymph node metastasis of esophageal cancer by support vector machines model based on computed tomography. MATERIALS AND METHODS: A total of 131 esophageal cancer patients with preoperative chemotherapy and radical surgery were included. Various indicators (tumor thickness, tumor length, tumor CT value, total number of lymph nodes, and long axis and short axis sizes of largest lymph node) on CT images before and after neoadjuvant chemotherapy were recorded. A support vector machines model based on these CT indicators was built to predict lymph node metastasis. RESULTS: Support vector machines model diagnosed lymph node metastasis better than preoperative short axis size of largest lymph node on CT. The area under the receiver operating characteristic curves were 0.887 and 0.705, respectively. CONCLUSIONS: The support vector machine model of CT images can help diagnose lymph node metastasis in esophageal cancer with preoperative chemotherapy. Lippincott Williams & Wilkins 2017-05 2016-11-23 /pmc/articles/PMC5457826/ /pubmed/27879527 http://dx.doi.org/10.1097/RCT.0000000000000555 Text en Copyright © 2016 The Author(s). Published by Wolters Kluwer Health, Inc. This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND) (http://creativecommons.org/licenses/by-nc-nd/4.0/) , where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. |
spellingShingle | Thoracic Imaging Wang, Zhi-Long Zhou, Zhi-Guo Chen, Ying Li, Xiao-Ting Sun, Ying-Shi Support Vector Machines Model of Computed Tomography for Assessing Lymph Node Metastasis in Esophageal Cancer with Neoadjuvant Chemotherapy |
title | Support Vector Machines Model of Computed Tomography for Assessing Lymph Node Metastasis in Esophageal Cancer with Neoadjuvant Chemotherapy |
title_full | Support Vector Machines Model of Computed Tomography for Assessing Lymph Node Metastasis in Esophageal Cancer with Neoadjuvant Chemotherapy |
title_fullStr | Support Vector Machines Model of Computed Tomography for Assessing Lymph Node Metastasis in Esophageal Cancer with Neoadjuvant Chemotherapy |
title_full_unstemmed | Support Vector Machines Model of Computed Tomography for Assessing Lymph Node Metastasis in Esophageal Cancer with Neoadjuvant Chemotherapy |
title_short | Support Vector Machines Model of Computed Tomography for Assessing Lymph Node Metastasis in Esophageal Cancer with Neoadjuvant Chemotherapy |
title_sort | support vector machines model of computed tomography for assessing lymph node metastasis in esophageal cancer with neoadjuvant chemotherapy |
topic | Thoracic Imaging |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5457826/ https://www.ncbi.nlm.nih.gov/pubmed/27879527 http://dx.doi.org/10.1097/RCT.0000000000000555 |
work_keys_str_mv | AT wangzhilong supportvectormachinesmodelofcomputedtomographyforassessinglymphnodemetastasisinesophagealcancerwithneoadjuvantchemotherapy AT zhouzhiguo supportvectormachinesmodelofcomputedtomographyforassessinglymphnodemetastasisinesophagealcancerwithneoadjuvantchemotherapy AT chenying supportvectormachinesmodelofcomputedtomographyforassessinglymphnodemetastasisinesophagealcancerwithneoadjuvantchemotherapy AT lixiaoting supportvectormachinesmodelofcomputedtomographyforassessinglymphnodemetastasisinesophagealcancerwithneoadjuvantchemotherapy AT sunyingshi supportvectormachinesmodelofcomputedtomographyforassessinglymphnodemetastasisinesophagealcancerwithneoadjuvantchemotherapy |