Support vector machines model of computed tomography for assessing lymph node metastasis in esophageal cancer with neoadjuvant chemotherapy

Zhi Long Wang, Zhi Guo Zhou, Ying Chen, Xiao Ting Li, Ying Shi Sun

Research output: Contribution to journalArticle

6 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)455-460
Number of pages6
JournalJournal of computer assisted tomography
Volume41
Issue number3
DOIs
StatePublished - Jan 1 2017

Keywords

  • Computed tomography
  • Esophageal cancer
  • Lymph node metastasis
  • Support vector machine

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

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