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Sub-region based radiomics analysis for survival prediction in oesophageal tumours treated by definitive concurrent chemoradiotherapy

BACKGROUND: Evaluating clinical outcome prior to concurrent chemoradiotherapy remains challenging for oesophageal squamous cell carcinoma (OSCC) as traditional prognostic markers are assessed at the completion of treatment. Herein, we investigated the potential of using sub-region radiomics as a nov...

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Autores principales: Xie, Congying, Yang, Pengfei, Zhang, Xuebang, Xu, Lei, Wang, Xiaoju, Li, Xiadong, Zhang, Luhan, Xie, Ruifei, Yang, Ling, Jing, Zhao, Zhang, Hongfang, Ding, Lingyu, Kuang, Yu, Niu, Tianye, Wu, Shixiu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6606893/
https://www.ncbi.nlm.nih.gov/pubmed/31129097
http://dx.doi.org/10.1016/j.ebiom.2019.05.023
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author Xie, Congying
Yang, Pengfei
Zhang, Xuebang
Xu, Lei
Wang, Xiaoju
Li, Xiadong
Zhang, Luhan
Xie, Ruifei
Yang, Ling
Jing, Zhao
Zhang, Hongfang
Ding, Lingyu
Kuang, Yu
Niu, Tianye
Wu, Shixiu
author_facet Xie, Congying
Yang, Pengfei
Zhang, Xuebang
Xu, Lei
Wang, Xiaoju
Li, Xiadong
Zhang, Luhan
Xie, Ruifei
Yang, Ling
Jing, Zhao
Zhang, Hongfang
Ding, Lingyu
Kuang, Yu
Niu, Tianye
Wu, Shixiu
author_sort Xie, Congying
collection PubMed
description BACKGROUND: Evaluating clinical outcome prior to concurrent chemoradiotherapy remains challenging for oesophageal squamous cell carcinoma (OSCC) as traditional prognostic markers are assessed at the completion of treatment. Herein, we investigated the potential of using sub-region radiomics as a novel tumour biomarker in predicting overall survival of OSCC patients treated by concurrent chemoradiotherapy. METHODS: Independent patient cohorts from two hospitals were included for training (n = 87) and validation (n = 46). Radiomics features were extracted from sub-regions clustered from patients' tumour regions using K-means method. The LASSO regression for ‘Cox’ method was used for feature selection. The survival prediction model was constructed based on the sub-region radiomics features using the Cox proportional hazards model. The clinical and biological significance of radiomics features were assessed by correlation analysis of clinical characteristics and copy number alterations(CNAs) in the validation dataset. FINDINGS: The overall survival prediction model combining with seven sub-regional radiomics features was constructed. The C-indexes of the proposed model were 0.729 (0.656–0.801, 95% CI) and 0.705 (0.628–0.782, 95%CI) in the training and validation cohorts, respectively. The 3-year survival receiver operating characteristic (ROC) curve showed an area under the ROC curve of 0.811 (0.670–0.952, 95%CI) in training and 0.805 (0.638–0.973, 95%CI) in validation. The correlation analysis showed a significant correlation between radiomics features and CNAs. INTERPRETATION: The proposed sub-regional radiomics model could predict the overall survival risk for patients with OSCC treated by definitive concurrent chemoradiotherapy. FUND: This work was supported by the Zhejiang Provincial Foundation for Natural Sciences, National Natural Science Foundation of China.
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spelling pubmed-66068932019-07-15 Sub-region based radiomics analysis for survival prediction in oesophageal tumours treated by definitive concurrent chemoradiotherapy Xie, Congying Yang, Pengfei Zhang, Xuebang Xu, Lei Wang, Xiaoju Li, Xiadong Zhang, Luhan Xie, Ruifei Yang, Ling Jing, Zhao Zhang, Hongfang Ding, Lingyu Kuang, Yu Niu, Tianye Wu, Shixiu EBioMedicine Research paper BACKGROUND: Evaluating clinical outcome prior to concurrent chemoradiotherapy remains challenging for oesophageal squamous cell carcinoma (OSCC) as traditional prognostic markers are assessed at the completion of treatment. Herein, we investigated the potential of using sub-region radiomics as a novel tumour biomarker in predicting overall survival of OSCC patients treated by concurrent chemoradiotherapy. METHODS: Independent patient cohorts from two hospitals were included for training (n = 87) and validation (n = 46). Radiomics features were extracted from sub-regions clustered from patients' tumour regions using K-means method. The LASSO regression for ‘Cox’ method was used for feature selection. The survival prediction model was constructed based on the sub-region radiomics features using the Cox proportional hazards model. The clinical and biological significance of radiomics features were assessed by correlation analysis of clinical characteristics and copy number alterations(CNAs) in the validation dataset. FINDINGS: The overall survival prediction model combining with seven sub-regional radiomics features was constructed. The C-indexes of the proposed model were 0.729 (0.656–0.801, 95% CI) and 0.705 (0.628–0.782, 95%CI) in the training and validation cohorts, respectively. The 3-year survival receiver operating characteristic (ROC) curve showed an area under the ROC curve of 0.811 (0.670–0.952, 95%CI) in training and 0.805 (0.638–0.973, 95%CI) in validation. The correlation analysis showed a significant correlation between radiomics features and CNAs. INTERPRETATION: The proposed sub-regional radiomics model could predict the overall survival risk for patients with OSCC treated by definitive concurrent chemoradiotherapy. FUND: This work was supported by the Zhejiang Provincial Foundation for Natural Sciences, National Natural Science Foundation of China. Elsevier 2019-05-23 /pmc/articles/PMC6606893/ /pubmed/31129097 http://dx.doi.org/10.1016/j.ebiom.2019.05.023 Text en © 2019 The Authors 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
Xie, Congying
Yang, Pengfei
Zhang, Xuebang
Xu, Lei
Wang, Xiaoju
Li, Xiadong
Zhang, Luhan
Xie, Ruifei
Yang, Ling
Jing, Zhao
Zhang, Hongfang
Ding, Lingyu
Kuang, Yu
Niu, Tianye
Wu, Shixiu
Sub-region based radiomics analysis for survival prediction in oesophageal tumours treated by definitive concurrent chemoradiotherapy
title Sub-region based radiomics analysis for survival prediction in oesophageal tumours treated by definitive concurrent chemoradiotherapy
title_full Sub-region based radiomics analysis for survival prediction in oesophageal tumours treated by definitive concurrent chemoradiotherapy
title_fullStr Sub-region based radiomics analysis for survival prediction in oesophageal tumours treated by definitive concurrent chemoradiotherapy
title_full_unstemmed Sub-region based radiomics analysis for survival prediction in oesophageal tumours treated by definitive concurrent chemoradiotherapy
title_short Sub-region based radiomics analysis for survival prediction in oesophageal tumours treated by definitive concurrent chemoradiotherapy
title_sort sub-region based radiomics analysis for survival prediction in oesophageal tumours treated by definitive concurrent chemoradiotherapy
topic Research paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6606893/
https://www.ncbi.nlm.nih.gov/pubmed/31129097
http://dx.doi.org/10.1016/j.ebiom.2019.05.023
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