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Radiomics models based on CT at different phases predicting lymph node metastasis of esophageal squamous cell carcinoma (GASTO-1089)

PURPOSE: To investigate the value of radiomics models based on CT at different phases (non-contrast-enhanced and contrast-enhanced images) in predicting lymph node (LN) metastasis in esophageal squamous cell carcinoma (ESCC). METHODS AND MATERIALS: Two hundred and seventy-four eligible patients with...

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Autores principales: Peng, Guobo, Zhan, Yizhou, Wu, Yanxuan, Zeng, Chengbing, Wang, Siyan, Guo, Longjia, Liu, Weitong, Luo, Limei, Wang, Ruoheng, Huang, Kang, Huang, Baotian, Chen, Jianzhou, Chen, Chuangzhen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9643555/
https://www.ncbi.nlm.nih.gov/pubmed/36387160
http://dx.doi.org/10.3389/fonc.2022.988859
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author Peng, Guobo
Zhan, Yizhou
Wu, Yanxuan
Zeng, Chengbing
Wang, Siyan
Guo, Longjia
Liu, Weitong
Luo, Limei
Wang, Ruoheng
Huang, Kang
Huang, Baotian
Chen, Jianzhou
Chen, Chuangzhen
author_facet Peng, Guobo
Zhan, Yizhou
Wu, Yanxuan
Zeng, Chengbing
Wang, Siyan
Guo, Longjia
Liu, Weitong
Luo, Limei
Wang, Ruoheng
Huang, Kang
Huang, Baotian
Chen, Jianzhou
Chen, Chuangzhen
author_sort Peng, Guobo
collection PubMed
description PURPOSE: To investigate the value of radiomics models based on CT at different phases (non-contrast-enhanced and contrast-enhanced images) in predicting lymph node (LN) metastasis in esophageal squamous cell carcinoma (ESCC). METHODS AND MATERIALS: Two hundred and seventy-four eligible patients with ESCC were divided into a training set (n =193) and a validation set (n =81). The least absolute shrinkage and selection operator algorithm (LASSO) was used to select radiomics features. The predictive models were constructed with radiomics features and clinical factors through multivariate logistic regression analysis. The predictive performance and clinical application value of the models were evaluated by area under receiver operating characteristic curve (AUC) and decision curve analysis (DCA). The Delong Test was used to evaluate the differences in AUC among models. RESULTS: Sixteen and eighteen features were respectively selected from non-contrast-enhanced CT (NECT) and contrast-enhanced CT (CECT) images. The model established using only clinical factors (Model 1) has an AUC value of 0.655 (95%CI 0.552-0.759) with a sensitivity of 0.585, a specificity of 0.725 and an accuracy of 0.654. The models contained clinical factors with radiomics features of NECT or/and CECT (Model 2,3,4) have significantly improved prediction performance. The values of AUC of Model 2,3,4 were 0.766, 0.811 and 0.809, respectively. It also achieved a great AUC of 0.800 in the model built with only radiomics features derived from NECT and CECT (Model 5). DCA suggested the potential clinical benefit of model prediction of LN metastasis of ESCC. A comparison of the receiver operating characteristic (ROC) curves using the Delong test indicated that Models 2, 3, 4, and 5 were superior to Model 1(P< 0.05), and no difference was found among Model 2, 3, 4 and Model 5(P > 0.05). CONCLUSION: Radiomics models based on CT at different phases could accurately predict the lymph node metastasis in patients with ESCC, and their predictive efficiency was better than the clinical model based on tumor size criteria. NECT–based radiomics model could be a reasonable option for ESCC patients due to its lower price and availability for renal failure or allergic patients.
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spelling pubmed-96435552022-11-15 Radiomics models based on CT at different phases predicting lymph node metastasis of esophageal squamous cell carcinoma (GASTO-1089) Peng, Guobo Zhan, Yizhou Wu, Yanxuan Zeng, Chengbing Wang, Siyan Guo, Longjia Liu, Weitong Luo, Limei Wang, Ruoheng Huang, Kang Huang, Baotian Chen, Jianzhou Chen, Chuangzhen Front Oncol Oncology PURPOSE: To investigate the value of radiomics models based on CT at different phases (non-contrast-enhanced and contrast-enhanced images) in predicting lymph node (LN) metastasis in esophageal squamous cell carcinoma (ESCC). METHODS AND MATERIALS: Two hundred and seventy-four eligible patients with ESCC were divided into a training set (n =193) and a validation set (n =81). The least absolute shrinkage and selection operator algorithm (LASSO) was used to select radiomics features. The predictive models were constructed with radiomics features and clinical factors through multivariate logistic regression analysis. The predictive performance and clinical application value of the models were evaluated by area under receiver operating characteristic curve (AUC) and decision curve analysis (DCA). The Delong Test was used to evaluate the differences in AUC among models. RESULTS: Sixteen and eighteen features were respectively selected from non-contrast-enhanced CT (NECT) and contrast-enhanced CT (CECT) images. The model established using only clinical factors (Model 1) has an AUC value of 0.655 (95%CI 0.552-0.759) with a sensitivity of 0.585, a specificity of 0.725 and an accuracy of 0.654. The models contained clinical factors with radiomics features of NECT or/and CECT (Model 2,3,4) have significantly improved prediction performance. The values of AUC of Model 2,3,4 were 0.766, 0.811 and 0.809, respectively. It also achieved a great AUC of 0.800 in the model built with only radiomics features derived from NECT and CECT (Model 5). DCA suggested the potential clinical benefit of model prediction of LN metastasis of ESCC. A comparison of the receiver operating characteristic (ROC) curves using the Delong test indicated that Models 2, 3, 4, and 5 were superior to Model 1(P< 0.05), and no difference was found among Model 2, 3, 4 and Model 5(P > 0.05). CONCLUSION: Radiomics models based on CT at different phases could accurately predict the lymph node metastasis in patients with ESCC, and their predictive efficiency was better than the clinical model based on tumor size criteria. NECT–based radiomics model could be a reasonable option for ESCC patients due to its lower price and availability for renal failure or allergic patients. Frontiers Media S.A. 2022-10-26 /pmc/articles/PMC9643555/ /pubmed/36387160 http://dx.doi.org/10.3389/fonc.2022.988859 Text en Copyright © 2022 Peng, Zhan, Wu, Zeng, Wang, Guo, Liu, Luo, Wang, Huang, Huang, Chen and Chen https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Oncology
Peng, Guobo
Zhan, Yizhou
Wu, Yanxuan
Zeng, Chengbing
Wang, Siyan
Guo, Longjia
Liu, Weitong
Luo, Limei
Wang, Ruoheng
Huang, Kang
Huang, Baotian
Chen, Jianzhou
Chen, Chuangzhen
Radiomics models based on CT at different phases predicting lymph node metastasis of esophageal squamous cell carcinoma (GASTO-1089)
title Radiomics models based on CT at different phases predicting lymph node metastasis of esophageal squamous cell carcinoma (GASTO-1089)
title_full Radiomics models based on CT at different phases predicting lymph node metastasis of esophageal squamous cell carcinoma (GASTO-1089)
title_fullStr Radiomics models based on CT at different phases predicting lymph node metastasis of esophageal squamous cell carcinoma (GASTO-1089)
title_full_unstemmed Radiomics models based on CT at different phases predicting lymph node metastasis of esophageal squamous cell carcinoma (GASTO-1089)
title_short Radiomics models based on CT at different phases predicting lymph node metastasis of esophageal squamous cell carcinoma (GASTO-1089)
title_sort radiomics models based on ct at different phases predicting lymph node metastasis of esophageal squamous cell carcinoma (gasto-1089)
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9643555/
https://www.ncbi.nlm.nih.gov/pubmed/36387160
http://dx.doi.org/10.3389/fonc.2022.988859
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