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Radiomics signature of computed tomography imaging for prediction of survival and chemotherapeutic benefits in gastric cancer

To develop and validate a radiomics signature for the prediction of gastric cancer (GC) survival and chemotherapeutic benefits. In this multicenter retrospective analysis, we analyzed the radiomics features of portal venous-phase computed tomography in 1591 consecutive patients. A radiomics signatur...

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Autores principales: Jiang, Yuming, Chen, Chuanli, Xie, Jingjing, Wang, Wei, Zha, Xuefan, Lv, Wenbing, Chen, Hao, Hu, Yanfeng, Li, Tuanjie, Yu, Jiang, Zhou, Zhiwei, Xu, Yikai, Li, Guoxin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6197796/
https://www.ncbi.nlm.nih.gov/pubmed/30224313
http://dx.doi.org/10.1016/j.ebiom.2018.09.007
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author Jiang, Yuming
Chen, Chuanli
Xie, Jingjing
Wang, Wei
Zha, Xuefan
Lv, Wenbing
Chen, Hao
Hu, Yanfeng
Li, Tuanjie
Yu, Jiang
Zhou, Zhiwei
Xu, Yikai
Li, Guoxin
author_facet Jiang, Yuming
Chen, Chuanli
Xie, Jingjing
Wang, Wei
Zha, Xuefan
Lv, Wenbing
Chen, Hao
Hu, Yanfeng
Li, Tuanjie
Yu, Jiang
Zhou, Zhiwei
Xu, Yikai
Li, Guoxin
author_sort Jiang, Yuming
collection PubMed
description To develop and validate a radiomics signature for the prediction of gastric cancer (GC) survival and chemotherapeutic benefits. In this multicenter retrospective analysis, we analyzed the radiomics features of portal venous-phase computed tomography in 1591 consecutive patients. A radiomics signature was generated by using the Lasso-Cox regression model in 228 patients and validated in internal and external validation cohorts. Radiomics nomograms integrating the radiomics signature were constructed, demonstrating the incremental value of the radiomics signature to the traditional staging system for individualized survival estimation. The performance of the nomograms was assessed with respect to calibration, discrimination, and clinical usefulness. The radiomics signature consisted of 19 selected features and was significantly associated with DFS (disease-free survival) and OS (overall survival). Multivariate analysis demonstrated that the radiomics signature was an independent prognostic factor. Incorporating the radiomics signature into the radiomics-based nomograms resulted in better performance for the estimation of DFS and OS than the clinicopathological nomograms and TNM staging system, with improved accuracy of the classification of survival outcomes. Further analysis showed that stage II and III patients with higher radiomics scores exhibited a favorable response to chemotherapy. In conclusion, the newly developed radiomics signature is a powerful predictor of DFS and OS, and it may predict which patients with stage II and III GC benefit from chemotherapy.
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spelling pubmed-61977962018-10-25 Radiomics signature of computed tomography imaging for prediction of survival and chemotherapeutic benefits in gastric cancer Jiang, Yuming Chen, Chuanli Xie, Jingjing Wang, Wei Zha, Xuefan Lv, Wenbing Chen, Hao Hu, Yanfeng Li, Tuanjie Yu, Jiang Zhou, Zhiwei Xu, Yikai Li, Guoxin EBioMedicine Research paper To develop and validate a radiomics signature for the prediction of gastric cancer (GC) survival and chemotherapeutic benefits. In this multicenter retrospective analysis, we analyzed the radiomics features of portal venous-phase computed tomography in 1591 consecutive patients. A radiomics signature was generated by using the Lasso-Cox regression model in 228 patients and validated in internal and external validation cohorts. Radiomics nomograms integrating the radiomics signature were constructed, demonstrating the incremental value of the radiomics signature to the traditional staging system for individualized survival estimation. The performance of the nomograms was assessed with respect to calibration, discrimination, and clinical usefulness. The radiomics signature consisted of 19 selected features and was significantly associated with DFS (disease-free survival) and OS (overall survival). Multivariate analysis demonstrated that the radiomics signature was an independent prognostic factor. Incorporating the radiomics signature into the radiomics-based nomograms resulted in better performance for the estimation of DFS and OS than the clinicopathological nomograms and TNM staging system, with improved accuracy of the classification of survival outcomes. Further analysis showed that stage II and III patients with higher radiomics scores exhibited a favorable response to chemotherapy. In conclusion, the newly developed radiomics signature is a powerful predictor of DFS and OS, and it may predict which patients with stage II and III GC benefit from chemotherapy. Elsevier 2018-09-14 /pmc/articles/PMC6197796/ /pubmed/30224313 http://dx.doi.org/10.1016/j.ebiom.2018.09.007 Text en © 2018 Published by Elsevier B.V. http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Research paper
Jiang, Yuming
Chen, Chuanli
Xie, Jingjing
Wang, Wei
Zha, Xuefan
Lv, Wenbing
Chen, Hao
Hu, Yanfeng
Li, Tuanjie
Yu, Jiang
Zhou, Zhiwei
Xu, Yikai
Li, Guoxin
Radiomics signature of computed tomography imaging for prediction of survival and chemotherapeutic benefits in gastric cancer
title Radiomics signature of computed tomography imaging for prediction of survival and chemotherapeutic benefits in gastric cancer
title_full Radiomics signature of computed tomography imaging for prediction of survival and chemotherapeutic benefits in gastric cancer
title_fullStr Radiomics signature of computed tomography imaging for prediction of survival and chemotherapeutic benefits in gastric cancer
title_full_unstemmed Radiomics signature of computed tomography imaging for prediction of survival and chemotherapeutic benefits in gastric cancer
title_short Radiomics signature of computed tomography imaging for prediction of survival and chemotherapeutic benefits in gastric cancer
title_sort radiomics signature of computed tomography imaging for prediction of survival and chemotherapeutic benefits in gastric cancer
topic Research paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6197796/
https://www.ncbi.nlm.nih.gov/pubmed/30224313
http://dx.doi.org/10.1016/j.ebiom.2018.09.007
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