Literature DB >> 28189209

Early clinical esophageal adenocarcinoma (cT1): Utility of CT in regional nodal metastasis detection and can the clinical accuracy be improved?

Sonia L Betancourt Cuellar1, Bradley Sabloff2, Brett W Carter3, Marcelo F Benveniste4, Arlene M Correa5, Dipen M Maru6, Jaffer A Ajani7, Jeremy J Erasmus8, Wayne L Hofstetter9.   

Abstract

INTRODUCTION: Treatment of early esophageal cancer depends on the extent of the primary tumor and presence of regional lymph node metastasis.(RNM). Short axis diameter>10mm is typically used to detect RNM. However, clinical determination of RNM is inaccurate and can result in inappropriate treatment. Purpose of this study is to evaluate the accuracy of a single linear measurement (short axis>10mm) of regional nodes on CT in predicting nodal metastasis, in patients with early esophageal cancer and whether using a mean diameter value (short axis+long axis/2) as well as nodal shape improves cN designation.
METHODS: CTs of 49 patients with cT1 adenocarcinoma treated with surgical resection alone were reviewed retrospectively. Regional nodes were considered positive for malignancy when round or ovoid and mean size>5mm adjacent to the primary tumor and>7mm when not adjacent. Results were compared with pN status after esophagectomy.
RESULTS: 18/49 patients had pN+ at resection. Using a single short axis diameter>10mm on CT, nodal metastasis (cN) was positive in 7/49. Only 1 of these patients was pN+ at resection (sensitivity 5%, specificity 80%, accuracy 53%). Using mean size and morphologic criteria, cN was positive in 28/49. 11 of these patients were pN+ at resection (sensitivity 61%, specificity 45%, accuracy 51%). EUS with limited FNA of regional nodes resulted in 16/49 patients with pN+ being inappropriately designated as cN0.
CONCLUSIONS: Evaluation of size, shape and location of regional lymph nodes on CT improves the sensitivity of cN determination compared with a short axis measurement alone in patients with cT1 esophageal cancer, although clinical utility is limited.
Copyright © 2017 Elsevier Ireland Ltd. All rights reserved.

Entities:  

Keywords:  CT; Early clinical esophageal adenocarcinoma; Regional nodal disease; Utility

Mesh:

Year:  2017        PMID: 28189209     DOI: 10.1016/j.ejrad.2017.01.001

Source DB:  PubMed          Journal:  Eur J Radiol        ISSN: 0720-048X            Impact factor:   3.528


  5 in total

1.  Radiomics nomogram outperforms size criteria in discriminating lymph node metastasis in resectable esophageal squamous cell carcinoma.

Authors:  Xianzheng Tan; Zelan Ma; Lifen Yan; Weitao Ye; Zaiyi Liu; Changhong Liang
Journal:  Eur Radiol       Date:  2018-06-19       Impact factor: 5.315

2.  Olfactomedin 4 (OLFM4) expression is associated with nodal metastases in esophageal adenocarcinoma.

Authors:  Lucia Suzuki; Fiebo J C Ten Kate; Annieke W Gotink; Hans Stoop; Michail Doukas; Daan Nieboer; Manon C W Spaander; Jan J B van Lanschot; Bas P L van Wijnhoven; Arjun D Koch; Marco J Bruno; Leendert H J Looijenga; Katharina Biermann
Journal:  PLoS One       Date:  2019-07-08       Impact factor: 3.240

3.  Multiple Level CT Radiomics Features Preoperatively Predict Lymph Node Metastasis in Esophageal Cancer: A Multicentre Retrospective Study.

Authors:  Lei Wu; Xiaojun Yang; Wuteng Cao; Ke Zhao; Wenli Li; Weitao Ye; Xin Chen; Zhiyang Zhou; Zaiyi Liu; Changhong Liang
Journal:  Front Oncol       Date:  2020-01-21       Impact factor: 6.244

4.  A novel web-based dynamic nomogram for recurrent laryngeal nerve lymph node metastasis in esophageal squamous cell carcinoma.

Authors:  Ting-Ting Chen; Hao-Ji Yan; Xi He; Si-Yi Fu; Sheng-Xuan Zhang; Wan Yang; Yu-Jie Zuo; Hong-Tao Tang; Jun-Jie Yang; Pei-Zhi Liu; Hong-Ying Wen; Dong Tian
Journal:  Front Surg       Date:  2022-08-23

5.  Machine learning models predict lymph node metastasis in patients with stage T1-T2 esophageal squamous cell carcinoma.

Authors:  Dong-Lin Li; Lin Zhang; Hao-Ji Yan; Yin-Bin Zheng; Xiao-Guang Guo; Sheng-Jie Tang; Hai-Yang Hu; Hang Yan; Chao Qin; Jun Zhang; Hai-Yang Guo; Hai-Ning Zhou; Dong Tian
Journal:  Front Oncol       Date:  2022-09-08       Impact factor: 5.738

  5 in total

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