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CT-based radiomic signatures for prediction of pathologic complete response in esophageal squamous cell carcinoma after neoadjuvant chemoradiotherapy

The objective of this study was to build models to predict complete pathologic response (pCR) after neoadjuvant chemoradiotherapy (nCRT) in esophageal squamous cell carcinoma (ESCC) patients using radiomic features. A total of 55 consecutive patients pathologically diagnosed as having ESCC were incl...

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Autores principales: Yang, Zhining, He, Binghui, Zhuang, Xinyu, Gao, Xiaoying, Wang, Dandan, Li, Mei, Lin, Zhixiong, Luo, Ren
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6640907/
https://www.ncbi.nlm.nih.gov/pubmed/31111948
http://dx.doi.org/10.1093/jrr/rrz027
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author Yang, Zhining
He, Binghui
Zhuang, Xinyu
Gao, Xiaoying
Wang, Dandan
Li, Mei
Lin, Zhixiong
Luo, Ren
author_facet Yang, Zhining
He, Binghui
Zhuang, Xinyu
Gao, Xiaoying
Wang, Dandan
Li, Mei
Lin, Zhixiong
Luo, Ren
author_sort Yang, Zhining
collection PubMed
description The objective of this study was to build models to predict complete pathologic response (pCR) after neoadjuvant chemoradiotherapy (nCRT) in esophageal squamous cell carcinoma (ESCC) patients using radiomic features. A total of 55 consecutive patients pathologically diagnosed as having ESCC were included in this study. Patients were divided into a training cohort (44 patients) and a testing cohort (11 patients). The logistic regression analysis using likelihood ratio forward selection was performed to select the predictive clinical parameters for pCR, and the least absolute shrinkage and selection operator (LASSO) with logistic regression to select radiomic predictors in the training cohort. Model performance in the training and testing groups was evaluated using the area under the receiver operating characteristic curves (AUC). The multivariate logistic regression analysis identified no clinical predictors for pCR. Thus, only radiomic features selected by LASSO were used to build prediction models. Three logistic regression models for pCR prediction were developed in the training cohort, and they were able to predict pCR well in both the training (AUC, 0.84–0.86) and the testing cohorts (AUC, 0.71–0.79). There were no differences between these AUCs. We developed three predictive models for pCR after nCRT using radiomic parameters and they demonstrated good model performance.
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spelling pubmed-66409072019-07-24 CT-based radiomic signatures for prediction of pathologic complete response in esophageal squamous cell carcinoma after neoadjuvant chemoradiotherapy Yang, Zhining He, Binghui Zhuang, Xinyu Gao, Xiaoying Wang, Dandan Li, Mei Lin, Zhixiong Luo, Ren J Radiat Res Regular Paper The objective of this study was to build models to predict complete pathologic response (pCR) after neoadjuvant chemoradiotherapy (nCRT) in esophageal squamous cell carcinoma (ESCC) patients using radiomic features. A total of 55 consecutive patients pathologically diagnosed as having ESCC were included in this study. Patients were divided into a training cohort (44 patients) and a testing cohort (11 patients). The logistic regression analysis using likelihood ratio forward selection was performed to select the predictive clinical parameters for pCR, and the least absolute shrinkage and selection operator (LASSO) with logistic regression to select radiomic predictors in the training cohort. Model performance in the training and testing groups was evaluated using the area under the receiver operating characteristic curves (AUC). The multivariate logistic regression analysis identified no clinical predictors for pCR. Thus, only radiomic features selected by LASSO were used to build prediction models. Three logistic regression models for pCR prediction were developed in the training cohort, and they were able to predict pCR well in both the training (AUC, 0.84–0.86) and the testing cohorts (AUC, 0.71–0.79). There were no differences between these AUCs. We developed three predictive models for pCR after nCRT using radiomic parameters and they demonstrated good model performance. Oxford University Press 2019-07 2019-05-21 /pmc/articles/PMC6640907/ /pubmed/31111948 http://dx.doi.org/10.1093/jrr/rrz027 Text en © The Author(s) 2019. Published by Oxford University Press on behalf of The Japan Radiation Research Society and Japanese Society for Radiation Oncology. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Regular Paper
Yang, Zhining
He, Binghui
Zhuang, Xinyu
Gao, Xiaoying
Wang, Dandan
Li, Mei
Lin, Zhixiong
Luo, Ren
CT-based radiomic signatures for prediction of pathologic complete response in esophageal squamous cell carcinoma after neoadjuvant chemoradiotherapy
title CT-based radiomic signatures for prediction of pathologic complete response in esophageal squamous cell carcinoma after neoadjuvant chemoradiotherapy
title_full CT-based radiomic signatures for prediction of pathologic complete response in esophageal squamous cell carcinoma after neoadjuvant chemoradiotherapy
title_fullStr CT-based radiomic signatures for prediction of pathologic complete response in esophageal squamous cell carcinoma after neoadjuvant chemoradiotherapy
title_full_unstemmed CT-based radiomic signatures for prediction of pathologic complete response in esophageal squamous cell carcinoma after neoadjuvant chemoradiotherapy
title_short CT-based radiomic signatures for prediction of pathologic complete response in esophageal squamous cell carcinoma after neoadjuvant chemoradiotherapy
title_sort ct-based radiomic signatures for prediction of pathologic complete response in esophageal squamous cell carcinoma after neoadjuvant chemoradiotherapy
topic Regular Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6640907/
https://www.ncbi.nlm.nih.gov/pubmed/31111948
http://dx.doi.org/10.1093/jrr/rrz027
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