Literature DB >> 35125811

Identification of lymph node metastasis by computed tomography in early gastric cancer.

Jingtao Wei1, Yinan Zhang1, Zhilong Wang2, Xiaojiang Wu1, Ji Zhang1, Zhaode Bu1, Jiafu Ji1.   

Abstract

OBJECTIVE: Lymph node status is critical when selecting treatment methods for patients with early gastric cancer (EGC). The aim of this study was to assess the diagnostic value of computed tomography (CT) for detection of lymph node metastasis (LNM) in patients with EGC.
METHODS: We retrospectively analyzed patients who had pathologically confirmed EGC between November 2010 and January 2019. After 1:1 propensity score matching, 65 patients with LNM and 65 patients without LNM were retained for comparison. The long diameter (LD) and short diameter (SD) of all visualized lymph nodes in all stations were recorded. The diagnostic value of LNM was assessed with receiver operating characteristic analysis.
RESULTS: Among 130 patients, we found a total of 558 lymph nodes on the CT images. Among the diagnostic indicators, the number, sum of LD and sum of SD of lymph nodes greater than 3 mm had better discrimination. The areas under the curve were all greater than 0.75. As for different regions, the optimal cutoff values of number, the sum of LD and sum of SD were determined as follows: overall, ≥4, 19.9 mm and 13.5 mm; left gastric artery basin, ≥3, 15.7 mm and 8.6 mm; right gastroepiploic artery basin, ≥2, 8.6 mm and 7.0 mm.
CONCLUSIONS: CT is valuable for diagnosing LNM in EGC patients. The number, sum of LD and sum of SD of lymph nodes greater than 3 mm are preferable indicators. Different regional lymph nodes have different optimal criteria for predicting LNM in ECG patients.
Copyright ©2021Chinese Journal of Cancer Research. All rights reserved.

Entities:  

Keywords:  Computed tomography; early gastric cancer; lymphatic basin; lymphatic metastasis

Year:  2021        PMID: 35125811      PMCID: PMC8742171          DOI: 10.21147/j.issn.1000-9604.2021.06.04

Source DB:  PubMed          Journal:  Chin J Cancer Res        ISSN: 1000-9604            Impact factor:   5.087


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1.  Development and validation of a predictive model combining clinical, radiomics, and deep transfer learning features for lymph node metastasis in early gastric cancer.

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