Using dual-layer detector spectral computed tomography to predict lymph node metastasis and identify differentiation degree of gastric cancer
Research Square (Research Square)(2022)
The First Affiliated Hospital of Kunming Medical University
Abstract
Abstract Purpose: To investigate the value of spectral computed tomography (CT) in evaluating the differentiation degree of gastric cancer and predicting lymph node (LN) metastasis. Methods: Enhanced spectral CT images of 51 gastric cancer patients were retrospectively analyzed. Effective atomic number (Zeff), normalized effective atomic number (nZeff), iodine density (ID), and normalized ID (nID) of the gastric cancer were measured at the venous stage. Zeff, nZeff, ID and nID of the study groups (poorly and moderately-highly differentiated groups; LN metastasis and no LN metastasis groups) were statistically compared using independent t-tests. Sex, age, location, and thickness of gastric cancer were analyzed using one-way analysis of variance or independent t-test. Correlations were assessed using Spearman rank correlation. The optimal diagnostic cutoff value with sensitivity, specificity, and area under the curve (AUC) was determined using receiver operating characteristic curve analysis.Results: Zeff, nZeff, ID and nID of gastric cancer were higher in the poorly differentiated group than in the moderately-highly differentiated group (P<0.05), and AUCs for evaluating the degree of differentiation were 0.909, 0.888, 0.905, and 0.916, respectively. There were no significant differences in Zeff, nZeff, ID and nID regarding age, sex, tumor thickness, or tumor location (P>0.05). Zeff, nZeff, ID and nID were higher in the LNM group than in the no LNM group (P<0.05), and AUCs for predicting LNM were 0.888, 0.824, 0.828 and 0.892, respectively. Conclusions: Zeff, nZeff, ID and nID values of spectral CT predicted the degree of differentiation of gastric cancer and LNM and were positively correlated with N stage.
MoreTranslated text
Key words
gastric cancer,lymph node metastasis,computed tomography,dual-layer
PDF
View via Publisher
AI Read Science
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined