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Whole-liver enhanced CT radiomics analysis to predict metachronous liver metastases after rectal cancer surgery

BACKGROUND: To develop a radiomics model based on pretreatment whole-liver portal venous phase (PVP) contrast-enhanced CT (CE-CT) images for predicting metachronous liver metastases (MLM) within 24 months after rectal cancer (RC) surgery. METHODS: This study retrospectively analyzed 112 RC patients...

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Autores principales: Liang, Meng, Ma, Xiaohong, Wang, Leyao, Li, Dengfeng, Wang, Sicong, Zhang, Hongmei, Zhao, Xinming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9465956/
https://www.ncbi.nlm.nih.gov/pubmed/36089623
http://dx.doi.org/10.1186/s40644-022-00485-z
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author Liang, Meng
Ma, Xiaohong
Wang, Leyao
Li, Dengfeng
Wang, Sicong
Zhang, Hongmei
Zhao, Xinming
author_facet Liang, Meng
Ma, Xiaohong
Wang, Leyao
Li, Dengfeng
Wang, Sicong
Zhang, Hongmei
Zhao, Xinming
author_sort Liang, Meng
collection PubMed
description BACKGROUND: To develop a radiomics model based on pretreatment whole-liver portal venous phase (PVP) contrast-enhanced CT (CE-CT) images for predicting metachronous liver metastases (MLM) within 24 months after rectal cancer (RC) surgery. METHODS: This study retrospectively analyzed 112 RC patients without preoperative liver metastases who underwent rectal surgery between January 2015 and December 2017 at our institution. Volume of interest (VOI) segmentation of the whole-liver was performed on the PVP CE-CT images. All 1316 radiomics features were extracted automatically. The maximum-relevance and minimum-redundancy and least absolute shrinkage and selection operator methods were used for features selection and radiomics signature constructing. Three models based on radiomics features (radiomics model), clinical features (clinical model), and radiomics combined with clinical features (combined model) were built by multivariable logistic regression analysis. Receiver operating characteristic (ROC) curves were used to assess the diagnostic performance of models, and calibration curve and the decision curve analysis were performed to evaluate the clinical application value. RESULTS: In total, 52 patients in the MLM group and 60 patients in the non-MLM group were enrolled in this study. The radscore was built using 16 selected features and the corresponding coefficients. Both the radiomics model and the combined model showed higher diagnostic performance than clinical model (AUCs of training set: radiomics model 0.84 (95% CI, 0.76–0.93), clinical model 0.65 (95% CI, 0.55–0.75), combined model 0.85 (95% CI, 0.77–0.94); AUCs of validation set: radiomics model 0.84 (95% CI, 0.70–0.98), clinical model 0.58 (95% CI, 0.40–0.76), combined model 0.85 (95% CI, 0.71–0.99)). The calibration curves showed great consistency between the predicted value and actual event probability. The DCA showed that both the radiomics and combined models could add a net benefit on a large scale. CONCLUSIONS: The radiomics model based on preoperative whole-liver PVP CE-CT could predict MLM within 24 months after RC surgery. Clinical features could not significantly improve the prediction efficiency of the radiomics model. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40644-022-00485-z.
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spelling pubmed-94659562022-09-13 Whole-liver enhanced CT radiomics analysis to predict metachronous liver metastases after rectal cancer surgery Liang, Meng Ma, Xiaohong Wang, Leyao Li, Dengfeng Wang, Sicong Zhang, Hongmei Zhao, Xinming Cancer Imaging Research Article BACKGROUND: To develop a radiomics model based on pretreatment whole-liver portal venous phase (PVP) contrast-enhanced CT (CE-CT) images for predicting metachronous liver metastases (MLM) within 24 months after rectal cancer (RC) surgery. METHODS: This study retrospectively analyzed 112 RC patients without preoperative liver metastases who underwent rectal surgery between January 2015 and December 2017 at our institution. Volume of interest (VOI) segmentation of the whole-liver was performed on the PVP CE-CT images. All 1316 radiomics features were extracted automatically. The maximum-relevance and minimum-redundancy and least absolute shrinkage and selection operator methods were used for features selection and radiomics signature constructing. Three models based on radiomics features (radiomics model), clinical features (clinical model), and radiomics combined with clinical features (combined model) were built by multivariable logistic regression analysis. Receiver operating characteristic (ROC) curves were used to assess the diagnostic performance of models, and calibration curve and the decision curve analysis were performed to evaluate the clinical application value. RESULTS: In total, 52 patients in the MLM group and 60 patients in the non-MLM group were enrolled in this study. The radscore was built using 16 selected features and the corresponding coefficients. Both the radiomics model and the combined model showed higher diagnostic performance than clinical model (AUCs of training set: radiomics model 0.84 (95% CI, 0.76–0.93), clinical model 0.65 (95% CI, 0.55–0.75), combined model 0.85 (95% CI, 0.77–0.94); AUCs of validation set: radiomics model 0.84 (95% CI, 0.70–0.98), clinical model 0.58 (95% CI, 0.40–0.76), combined model 0.85 (95% CI, 0.71–0.99)). The calibration curves showed great consistency between the predicted value and actual event probability. The DCA showed that both the radiomics and combined models could add a net benefit on a large scale. CONCLUSIONS: The radiomics model based on preoperative whole-liver PVP CE-CT could predict MLM within 24 months after RC surgery. Clinical features could not significantly improve the prediction efficiency of the radiomics model. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40644-022-00485-z. BioMed Central 2022-09-11 /pmc/articles/PMC9465956/ /pubmed/36089623 http://dx.doi.org/10.1186/s40644-022-00485-z 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 Article
Liang, Meng
Ma, Xiaohong
Wang, Leyao
Li, Dengfeng
Wang, Sicong
Zhang, Hongmei
Zhao, Xinming
Whole-liver enhanced CT radiomics analysis to predict metachronous liver metastases after rectal cancer surgery
title Whole-liver enhanced CT radiomics analysis to predict metachronous liver metastases after rectal cancer surgery
title_full Whole-liver enhanced CT radiomics analysis to predict metachronous liver metastases after rectal cancer surgery
title_fullStr Whole-liver enhanced CT radiomics analysis to predict metachronous liver metastases after rectal cancer surgery
title_full_unstemmed Whole-liver enhanced CT radiomics analysis to predict metachronous liver metastases after rectal cancer surgery
title_short Whole-liver enhanced CT radiomics analysis to predict metachronous liver metastases after rectal cancer surgery
title_sort whole-liver enhanced ct radiomics analysis to predict metachronous liver metastases after rectal cancer surgery
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9465956/
https://www.ncbi.nlm.nih.gov/pubmed/36089623
http://dx.doi.org/10.1186/s40644-022-00485-z
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