Cargando…

A multiphase contrast-enhanced CT radiomics model for prediction of human epidermal growth factor receptor 2 status in advanced gastric cancer

Background: Accurate evaluation of human epidermal growth factor receptor 2 (HER2) status is of great importance for appropriate management of advanced gastric cancer (AGC) patients. This study aims to develop and validate a CT-based radiomics model for prediction of HER2 overexpression in AGC. Mate...

Descripción completa

Detalles Bibliográficos
Autores principales: Ma, Tingting, Cui, Jingli, Wang, Lingwei, Li, Hui, Ye, Zhaoxiang, Gao, Xujie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9585247/
https://www.ncbi.nlm.nih.gov/pubmed/36276942
http://dx.doi.org/10.3389/fgene.2022.968027
_version_ 1784813449730064384
author Ma, Tingting
Cui, Jingli
Wang, Lingwei
Li, Hui
Ye, Zhaoxiang
Gao, Xujie
author_facet Ma, Tingting
Cui, Jingli
Wang, Lingwei
Li, Hui
Ye, Zhaoxiang
Gao, Xujie
author_sort Ma, Tingting
collection PubMed
description Background: Accurate evaluation of human epidermal growth factor receptor 2 (HER2) status is of great importance for appropriate management of advanced gastric cancer (AGC) patients. This study aims to develop and validate a CT-based radiomics model for prediction of HER2 overexpression in AGC. Materials and Methods: Seven hundred and forty-five consecutive AGC patients (median age, 59 years; interquartile range, 52–66 years; 515 male and 230 female) were enrolled and separated into training set (n = 521) and testing set (n = 224) in this retrospective study. Radiomics features were extracted from three phases images of contrast-enhanced CT scans. A radiomics signature was built based on highly reproducible features using the least absolute shrinkage and selection operator method. Univariable and multivariable logistical regression analysis were used to establish predictive model with independent risk factors of HER2 overexpression. The predictive performance of radiomics model was assessed in the training and testing sets. Results: The positive rate of HER2 was 15.9% and 13.8% in the training set and testing set, respectively. The positive rate of HER2 in intestinal-type GC was significantly higher than that in diffuse-type GC. The radiomics signature comprised eight robust features demonstrated good discrimination ability for HER2 overexpression in the training set (AUC = 0.84) and the testing set (AUC = 0.78). A radiomics-based model that incorporated radiomics signature and pathological type showed good discrimination and calibration in the training (AUC = 0.85) and testing (AUC = 0.84) sets. Conclusion: The proposed radiomics model showed favorable accuracy for prediction of HER2 overexpression in AGC.
format Online
Article
Text
id pubmed-9585247
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-95852472022-10-22 A multiphase contrast-enhanced CT radiomics model for prediction of human epidermal growth factor receptor 2 status in advanced gastric cancer Ma, Tingting Cui, Jingli Wang, Lingwei Li, Hui Ye, Zhaoxiang Gao, Xujie Front Genet Genetics Background: Accurate evaluation of human epidermal growth factor receptor 2 (HER2) status is of great importance for appropriate management of advanced gastric cancer (AGC) patients. This study aims to develop and validate a CT-based radiomics model for prediction of HER2 overexpression in AGC. Materials and Methods: Seven hundred and forty-five consecutive AGC patients (median age, 59 years; interquartile range, 52–66 years; 515 male and 230 female) were enrolled and separated into training set (n = 521) and testing set (n = 224) in this retrospective study. Radiomics features were extracted from three phases images of contrast-enhanced CT scans. A radiomics signature was built based on highly reproducible features using the least absolute shrinkage and selection operator method. Univariable and multivariable logistical regression analysis were used to establish predictive model with independent risk factors of HER2 overexpression. The predictive performance of radiomics model was assessed in the training and testing sets. Results: The positive rate of HER2 was 15.9% and 13.8% in the training set and testing set, respectively. The positive rate of HER2 in intestinal-type GC was significantly higher than that in diffuse-type GC. The radiomics signature comprised eight robust features demonstrated good discrimination ability for HER2 overexpression in the training set (AUC = 0.84) and the testing set (AUC = 0.78). A radiomics-based model that incorporated radiomics signature and pathological type showed good discrimination and calibration in the training (AUC = 0.85) and testing (AUC = 0.84) sets. Conclusion: The proposed radiomics model showed favorable accuracy for prediction of HER2 overexpression in AGC. Frontiers Media S.A. 2022-10-07 /pmc/articles/PMC9585247/ /pubmed/36276942 http://dx.doi.org/10.3389/fgene.2022.968027 Text en Copyright © 2022 Ma, Cui, Wang, Li, Ye and Gao. 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 Genetics
Ma, Tingting
Cui, Jingli
Wang, Lingwei
Li, Hui
Ye, Zhaoxiang
Gao, Xujie
A multiphase contrast-enhanced CT radiomics model for prediction of human epidermal growth factor receptor 2 status in advanced gastric cancer
title A multiphase contrast-enhanced CT radiomics model for prediction of human epidermal growth factor receptor 2 status in advanced gastric cancer
title_full A multiphase contrast-enhanced CT radiomics model for prediction of human epidermal growth factor receptor 2 status in advanced gastric cancer
title_fullStr A multiphase contrast-enhanced CT radiomics model for prediction of human epidermal growth factor receptor 2 status in advanced gastric cancer
title_full_unstemmed A multiphase contrast-enhanced CT radiomics model for prediction of human epidermal growth factor receptor 2 status in advanced gastric cancer
title_short A multiphase contrast-enhanced CT radiomics model for prediction of human epidermal growth factor receptor 2 status in advanced gastric cancer
title_sort multiphase contrast-enhanced ct radiomics model for prediction of human epidermal growth factor receptor 2 status in advanced gastric cancer
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9585247/
https://www.ncbi.nlm.nih.gov/pubmed/36276942
http://dx.doi.org/10.3389/fgene.2022.968027
work_keys_str_mv AT matingting amultiphasecontrastenhancedctradiomicsmodelforpredictionofhumanepidermalgrowthfactorreceptor2statusinadvancedgastriccancer
AT cuijingli amultiphasecontrastenhancedctradiomicsmodelforpredictionofhumanepidermalgrowthfactorreceptor2statusinadvancedgastriccancer
AT wanglingwei amultiphasecontrastenhancedctradiomicsmodelforpredictionofhumanepidermalgrowthfactorreceptor2statusinadvancedgastriccancer
AT lihui amultiphasecontrastenhancedctradiomicsmodelforpredictionofhumanepidermalgrowthfactorreceptor2statusinadvancedgastriccancer
AT yezhaoxiang amultiphasecontrastenhancedctradiomicsmodelforpredictionofhumanepidermalgrowthfactorreceptor2statusinadvancedgastriccancer
AT gaoxujie amultiphasecontrastenhancedctradiomicsmodelforpredictionofhumanepidermalgrowthfactorreceptor2statusinadvancedgastriccancer
AT matingting multiphasecontrastenhancedctradiomicsmodelforpredictionofhumanepidermalgrowthfactorreceptor2statusinadvancedgastriccancer
AT cuijingli multiphasecontrastenhancedctradiomicsmodelforpredictionofhumanepidermalgrowthfactorreceptor2statusinadvancedgastriccancer
AT wanglingwei multiphasecontrastenhancedctradiomicsmodelforpredictionofhumanepidermalgrowthfactorreceptor2statusinadvancedgastriccancer
AT lihui multiphasecontrastenhancedctradiomicsmodelforpredictionofhumanepidermalgrowthfactorreceptor2statusinadvancedgastriccancer
AT yezhaoxiang multiphasecontrastenhancedctradiomicsmodelforpredictionofhumanepidermalgrowthfactorreceptor2statusinadvancedgastriccancer
AT gaoxujie multiphasecontrastenhancedctradiomicsmodelforpredictionofhumanepidermalgrowthfactorreceptor2statusinadvancedgastriccancer