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A CT-based radiomics nomogram for prediction of human epidermal growth factor receptor 2 status in patients with gastric cancer
OBJECTIVE: To develop and validate a computed tomography (CT)-based radiomics nomogram for predicting human epidermal growth factor receptor 2 (HER2) status in patients with gastric cancer. METHODS: This retrospective study included 134 patients with gastric cancer (HER2-negative: n=87; HER2-positiv...
Autores principales: | , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
AME Publishing Company
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7072015/ https://www.ncbi.nlm.nih.gov/pubmed/32194306 http://dx.doi.org/10.21147/j.issn.1000-9604.2020.01.08 |
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author | Li, Yexing Cheng, Zixuan Gevaert, Olivier He, Lan Huang, Yanqi Chen, Xin Huang, Xiaomei Wu, Xiaomei Zhang, Wen Dong, Mengyi Huang, Jia Huang, Yucun Xia, Ting Liang, Changhong Liu, Zaiyi |
author_facet | Li, Yexing Cheng, Zixuan Gevaert, Olivier He, Lan Huang, Yanqi Chen, Xin Huang, Xiaomei Wu, Xiaomei Zhang, Wen Dong, Mengyi Huang, Jia Huang, Yucun Xia, Ting Liang, Changhong Liu, Zaiyi |
author_sort | Li, Yexing |
collection | PubMed |
description | OBJECTIVE: To develop and validate a computed tomography (CT)-based radiomics nomogram for predicting human epidermal growth factor receptor 2 (HER2) status in patients with gastric cancer. METHODS: This retrospective study included 134 patients with gastric cancer (HER2-negative: n=87; HER2-positive: n=47) from April 2013 to March 2018, who were then randomly divided into training (n=94) and validation (n=40) cohorts. Radiomics features were obtained from the CT images showing gastric cancer. Least absolute shrinkage and selection operator (LASSO) regression analysis was utilized for building the radiomics signature. A multivariable logistic regression method was applied to develop a prediction model incorporating the radiomics signature and independent clinicopathologic risk predictors, which were then visualized as a radiomics nomogram. The predictive performance of the nomogram was assessed in the training and validation cohorts. RESULTS: The radiomics signature was significantly associated with HER2 status in both training (P<0.001) and validation (P=0.023) cohorts. The prediction model that incorporated the radiomics signature and carcinoembryonic antigen (CEA) level demonstrated good discriminative performance for HER2 status prediction, with an area under the curve (AUC) of 0.799 [95% confidence interval (95% CI): 0.704−0.894] in the training cohort and 0.771 (95% CI: 0.607−0.934) in the validation cohort. The calibration curve of the radiomics nomogram also showed good calibration. Decision curve analysis showed that the radiomics nomogram was useful. CONCLUSIONS: We built and validated a radiomics nomogram with good performance for HER2 status prediction in gastric cancer. This radiomics nomogram could serve as a non-invasive tool to predict HER2 status and guide clinical treatment. |
format | Online Article Text |
id | pubmed-7072015 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | AME Publishing Company |
record_format | MEDLINE/PubMed |
spelling | pubmed-70720152020-03-19 A CT-based radiomics nomogram for prediction of human epidermal growth factor receptor 2 status in patients with gastric cancer Li, Yexing Cheng, Zixuan Gevaert, Olivier He, Lan Huang, Yanqi Chen, Xin Huang, Xiaomei Wu, Xiaomei Zhang, Wen Dong, Mengyi Huang, Jia Huang, Yucun Xia, Ting Liang, Changhong Liu, Zaiyi Chin J Cancer Res Original Article OBJECTIVE: To develop and validate a computed tomography (CT)-based radiomics nomogram for predicting human epidermal growth factor receptor 2 (HER2) status in patients with gastric cancer. METHODS: This retrospective study included 134 patients with gastric cancer (HER2-negative: n=87; HER2-positive: n=47) from April 2013 to March 2018, who were then randomly divided into training (n=94) and validation (n=40) cohorts. Radiomics features were obtained from the CT images showing gastric cancer. Least absolute shrinkage and selection operator (LASSO) regression analysis was utilized for building the radiomics signature. A multivariable logistic regression method was applied to develop a prediction model incorporating the radiomics signature and independent clinicopathologic risk predictors, which were then visualized as a radiomics nomogram. The predictive performance of the nomogram was assessed in the training and validation cohorts. RESULTS: The radiomics signature was significantly associated with HER2 status in both training (P<0.001) and validation (P=0.023) cohorts. The prediction model that incorporated the radiomics signature and carcinoembryonic antigen (CEA) level demonstrated good discriminative performance for HER2 status prediction, with an area under the curve (AUC) of 0.799 [95% confidence interval (95% CI): 0.704−0.894] in the training cohort and 0.771 (95% CI: 0.607−0.934) in the validation cohort. The calibration curve of the radiomics nomogram also showed good calibration. Decision curve analysis showed that the radiomics nomogram was useful. CONCLUSIONS: We built and validated a radiomics nomogram with good performance for HER2 status prediction in gastric cancer. This radiomics nomogram could serve as a non-invasive tool to predict HER2 status and guide clinical treatment. AME Publishing Company 2020-02 /pmc/articles/PMC7072015/ /pubmed/32194306 http://dx.doi.org/10.21147/j.issn.1000-9604.2020.01.08 Text en Copyright © 2020 Chinese Journal of Cancer Research. All rights reserved. http://creativecommons.org/licenses/by-nc-sa/4.0/ This work is licensed under a Creative Commons Attribution-Non Commercial-Share Alike 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/4.0/ |
spellingShingle | Original Article Li, Yexing Cheng, Zixuan Gevaert, Olivier He, Lan Huang, Yanqi Chen, Xin Huang, Xiaomei Wu, Xiaomei Zhang, Wen Dong, Mengyi Huang, Jia Huang, Yucun Xia, Ting Liang, Changhong Liu, Zaiyi A CT-based radiomics nomogram for prediction of human epidermal growth factor receptor 2 status in patients with gastric cancer |
title | A CT-based radiomics nomogram for prediction of human epidermal growth factor receptor 2 status in patients with gastric cancer |
title_full | A CT-based radiomics nomogram for prediction of human epidermal growth factor receptor 2 status in patients with gastric cancer |
title_fullStr | A CT-based radiomics nomogram for prediction of human epidermal growth factor receptor 2 status in patients with gastric cancer |
title_full_unstemmed | A CT-based radiomics nomogram for prediction of human epidermal growth factor receptor 2 status in patients with gastric cancer |
title_short | A CT-based radiomics nomogram for prediction of human epidermal growth factor receptor 2 status in patients with gastric cancer |
title_sort | ct-based radiomics nomogram for prediction of human epidermal growth factor receptor 2 status in patients with gastric cancer |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7072015/ https://www.ncbi.nlm.nih.gov/pubmed/32194306 http://dx.doi.org/10.21147/j.issn.1000-9604.2020.01.08 |
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