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Application of preoperative CT texture analysis in papillary gastric adenocarcinoma
BACKGROUND: This study aimed to analyze the ability of computed tomography (CT) texture analysis to discriminate papillary gastric adenocarcinoma (PGC) and to explore the diagnostic efficacy of multivariate models integrating clinical information and CT texture parameters for discriminating PGCs. ME...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9650902/ https://www.ncbi.nlm.nih.gov/pubmed/36357844 http://dx.doi.org/10.1186/s12885-022-10261-8 |
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author | Xu, Mengying Qiao, Xiangmei Li, Lin Liu, Song Zhou, Zhengyang |
author_facet | Xu, Mengying Qiao, Xiangmei Li, Lin Liu, Song Zhou, Zhengyang |
author_sort | Xu, Mengying |
collection | PubMed |
description | BACKGROUND: This study aimed to analyze the ability of computed tomography (CT) texture analysis to discriminate papillary gastric adenocarcinoma (PGC) and to explore the diagnostic efficacy of multivariate models integrating clinical information and CT texture parameters for discriminating PGCs. METHODS: This retrospective study included 20 patients with PGC and 80 patients with tubular adenocarcinoma (TAC). The clinical data and CT texture parameters based on the arterial phase (AP) and venous phase (VP) of all patients were collected and analyzed. Two CT signatures based on the AP and VP were built with the optimum features selected by the least absolute shrinkage and selection operator method. The performance of CT signatures was tested by regression analysis. Multivariate models based on regression analysis and the support vector machine (SVM) algorithm were established. The diagnostic performance of the established nomogram based on regression analysis was evaluated by receiver operating characteristic curve analysis. RESULTS: Thirty-two and fifteen CT texture parameters extracted from AP and VP CT images, respectively, differed significantly between PGCs and TACs (all p < 0.05). The diagnostic performance of CT signatures based on the AP and VP achieved AUCs of 0.873 and 0.859 in distinguishing PGCs. Multivariate models that integrated two CT signatures and age based on regression analysis and the SVM algorithm showed favorable performance in preoperatively predicting PGCs (AUC = 0.922 and 0.914, respectively). CONCLUSION: CT texture analysis based multivariate models could preoperatively predict PGCs with satisfactory diagnostic efficacy. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12885-022-10261-8. |
format | Online Article Text |
id | pubmed-9650902 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-96509022022-11-15 Application of preoperative CT texture analysis in papillary gastric adenocarcinoma Xu, Mengying Qiao, Xiangmei Li, Lin Liu, Song Zhou, Zhengyang BMC Cancer Research BACKGROUND: This study aimed to analyze the ability of computed tomography (CT) texture analysis to discriminate papillary gastric adenocarcinoma (PGC) and to explore the diagnostic efficacy of multivariate models integrating clinical information and CT texture parameters for discriminating PGCs. METHODS: This retrospective study included 20 patients with PGC and 80 patients with tubular adenocarcinoma (TAC). The clinical data and CT texture parameters based on the arterial phase (AP) and venous phase (VP) of all patients were collected and analyzed. Two CT signatures based on the AP and VP were built with the optimum features selected by the least absolute shrinkage and selection operator method. The performance of CT signatures was tested by regression analysis. Multivariate models based on regression analysis and the support vector machine (SVM) algorithm were established. The diagnostic performance of the established nomogram based on regression analysis was evaluated by receiver operating characteristic curve analysis. RESULTS: Thirty-two and fifteen CT texture parameters extracted from AP and VP CT images, respectively, differed significantly between PGCs and TACs (all p < 0.05). The diagnostic performance of CT signatures based on the AP and VP achieved AUCs of 0.873 and 0.859 in distinguishing PGCs. Multivariate models that integrated two CT signatures and age based on regression analysis and the SVM algorithm showed favorable performance in preoperatively predicting PGCs (AUC = 0.922 and 0.914, respectively). CONCLUSION: CT texture analysis based multivariate models could preoperatively predict PGCs with satisfactory diagnostic efficacy. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12885-022-10261-8. BioMed Central 2022-11-10 /pmc/articles/PMC9650902/ /pubmed/36357844 http://dx.doi.org/10.1186/s12885-022-10261-8 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Xu, Mengying Qiao, Xiangmei Li, Lin Liu, Song Zhou, Zhengyang Application of preoperative CT texture analysis in papillary gastric adenocarcinoma |
title | Application of preoperative CT texture analysis in papillary gastric adenocarcinoma |
title_full | Application of preoperative CT texture analysis in papillary gastric adenocarcinoma |
title_fullStr | Application of preoperative CT texture analysis in papillary gastric adenocarcinoma |
title_full_unstemmed | Application of preoperative CT texture analysis in papillary gastric adenocarcinoma |
title_short | Application of preoperative CT texture analysis in papillary gastric adenocarcinoma |
title_sort | application of preoperative ct texture analysis in papillary gastric adenocarcinoma |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9650902/ https://www.ncbi.nlm.nih.gov/pubmed/36357844 http://dx.doi.org/10.1186/s12885-022-10261-8 |
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