Support Vector Machines Model of Computed Tomography for Assessing Lymph Node Metastasis in Esophageal Cancer with Neoadjuvant Chemotherapy

Overview

Journal J Comput Assist Tomogr

Specialty Radiology

Date 2016 Nov 24

PMID 27879527

Citations 15

Authors

Zhi-Long Wang

Zhi-Guo Zhou

Ying Chen

Xiao-Ting Li

Ying-Shi Sun

Affiliations

Soon will be listed here.

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.

Citing Articles

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Performance of radiomics-based artificial intelligence systems in the diagnosis and prediction of treatment response and survival in esophageal cancer: a systematic review and meta-analysis of diagnostic accuracy.

Menon N, Guidozzi N, Chidambaram S, Markar S Dis Esophagus. 2023; 36(6).

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Ding Y, Pu C, Zhang X, Tang G, Zhang F, Yu G J Inflamm Res. 2023; 16:1555-1570.

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ARMO: automated and reliable multi-objective model for lymph node metastasis prediction in head and neck cancer.

Zhou Z, Chen L, Dohopolski M, Sher D, Wang J Phys Med Biol. 2023; 68(9).

PMID: 37017082 PMC: 11034768. DOI: 10.1088/1361-6560/acca5b.

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