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Predictive performance of different NTCP techniques for radiation-induced esophagitis in NSCLC patients receiving proton radiotherapy

This study aimed to compare the predictive performance of different modeling methods in developing normal tissue complication probability (NTCP) models for predicting radiation-induced esophagitis (RE) in non–small cell lung cancer (NSCLC) patients receiving proton radiotherapy. The dataset was comp...

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Autores principales: Chen, Mei, Wang, Zeming, Jiang, Shengpeng, Sun, Jian, Wang, Li, Sahoo, Narayan, Brandon Gunn, G., Frank, Steven J., Xu, Cheng, Chen, Jiayi, Nguyen, Quynh-Nhu, Chang, Joe Y., Liao, Zhongxing, Ronald Zhu, X., Zhang, Xiaodong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9163134/
https://www.ncbi.nlm.nih.gov/pubmed/35655073
http://dx.doi.org/10.1038/s41598-022-12898-8
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author Chen, Mei
Wang, Zeming
Jiang, Shengpeng
Sun, Jian
Wang, Li
Sahoo, Narayan
Brandon Gunn, G.
Frank, Steven J.
Xu, Cheng
Chen, Jiayi
Nguyen, Quynh-Nhu
Chang, Joe Y.
Liao, Zhongxing
Ronald Zhu, X.
Zhang, Xiaodong
author_facet Chen, Mei
Wang, Zeming
Jiang, Shengpeng
Sun, Jian
Wang, Li
Sahoo, Narayan
Brandon Gunn, G.
Frank, Steven J.
Xu, Cheng
Chen, Jiayi
Nguyen, Quynh-Nhu
Chang, Joe Y.
Liao, Zhongxing
Ronald Zhu, X.
Zhang, Xiaodong
author_sort Chen, Mei
collection PubMed
description This study aimed to compare the predictive performance of different modeling methods in developing normal tissue complication probability (NTCP) models for predicting radiation-induced esophagitis (RE) in non–small cell lung cancer (NSCLC) patients receiving proton radiotherapy. The dataset was composed of 328 NSCLC patients receiving passive-scattering proton therapy and 41.6% of the patients experienced ≥ grade 2 RE. Five modeling methods were used to build NTCP models: standard Lyman–Kutcher–Burman (sLKB), generalized LKB (gLKB), multivariable logistic regression using two variable selection procedures-stepwise forward selection (Stepwise-MLR), and least absolute shrinkage and selection operator (LASSO-MLR), and support vector machines (SVM). Predictive performance was internally validated by a bootstrap approach for each modeling method. The overall performance, discriminative ability, and calibration were assessed using the Negelkerke R(2), area under the receiver operator curve (AUC), and Hosmer–Lemeshow test, respectively. The LASSO-MLR model showed the best discriminative ability with an AUC value of 0.799 (95% confidence interval (CI): 0.763–0.854), and the best overall performance with a Negelkerke R(2) value of 0.332 (95% CI: 0.266–0.486). Both of the optimism-corrected Negelkerke R(2) values of the SVM and sLKB models were 0.301. The optimism-corrected AUC of the gLKB model (0.796) was higher than that of the SVM model (0.784). The sLKB model had the smallest optimism in the model variation and discriminative ability. In the context of classification and probability estimation for predicting the NTCP for radiation-induced esophagitis, the MLR model developed with LASSO provided the best predictive results. The simplest LKB modeling had similar or even better predictive performance than the most complex SVM modeling, and it was least likely to overfit the training data. The advanced machine learning approach might have limited applicability in clinical settings with a relatively small amount of data.
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spelling pubmed-91631342022-06-05 Predictive performance of different NTCP techniques for radiation-induced esophagitis in NSCLC patients receiving proton radiotherapy Chen, Mei Wang, Zeming Jiang, Shengpeng Sun, Jian Wang, Li Sahoo, Narayan Brandon Gunn, G. Frank, Steven J. Xu, Cheng Chen, Jiayi Nguyen, Quynh-Nhu Chang, Joe Y. Liao, Zhongxing Ronald Zhu, X. Zhang, Xiaodong Sci Rep Article This study aimed to compare the predictive performance of different modeling methods in developing normal tissue complication probability (NTCP) models for predicting radiation-induced esophagitis (RE) in non–small cell lung cancer (NSCLC) patients receiving proton radiotherapy. The dataset was composed of 328 NSCLC patients receiving passive-scattering proton therapy and 41.6% of the patients experienced ≥ grade 2 RE. Five modeling methods were used to build NTCP models: standard Lyman–Kutcher–Burman (sLKB), generalized LKB (gLKB), multivariable logistic regression using two variable selection procedures-stepwise forward selection (Stepwise-MLR), and least absolute shrinkage and selection operator (LASSO-MLR), and support vector machines (SVM). Predictive performance was internally validated by a bootstrap approach for each modeling method. The overall performance, discriminative ability, and calibration were assessed using the Negelkerke R(2), area under the receiver operator curve (AUC), and Hosmer–Lemeshow test, respectively. The LASSO-MLR model showed the best discriminative ability with an AUC value of 0.799 (95% confidence interval (CI): 0.763–0.854), and the best overall performance with a Negelkerke R(2) value of 0.332 (95% CI: 0.266–0.486). Both of the optimism-corrected Negelkerke R(2) values of the SVM and sLKB models were 0.301. The optimism-corrected AUC of the gLKB model (0.796) was higher than that of the SVM model (0.784). The sLKB model had the smallest optimism in the model variation and discriminative ability. In the context of classification and probability estimation for predicting the NTCP for radiation-induced esophagitis, the MLR model developed with LASSO provided the best predictive results. The simplest LKB modeling had similar or even better predictive performance than the most complex SVM modeling, and it was least likely to overfit the training data. The advanced machine learning approach might have limited applicability in clinical settings with a relatively small amount of data. Nature Publishing Group UK 2022-06-02 /pmc/articles/PMC9163134/ /pubmed/35655073 http://dx.doi.org/10.1038/s41598-022-12898-8 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This 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/) .
spellingShingle Article
Chen, Mei
Wang, Zeming
Jiang, Shengpeng
Sun, Jian
Wang, Li
Sahoo, Narayan
Brandon Gunn, G.
Frank, Steven J.
Xu, Cheng
Chen, Jiayi
Nguyen, Quynh-Nhu
Chang, Joe Y.
Liao, Zhongxing
Ronald Zhu, X.
Zhang, Xiaodong
Predictive performance of different NTCP techniques for radiation-induced esophagitis in NSCLC patients receiving proton radiotherapy
title Predictive performance of different NTCP techniques for radiation-induced esophagitis in NSCLC patients receiving proton radiotherapy
title_full Predictive performance of different NTCP techniques for radiation-induced esophagitis in NSCLC patients receiving proton radiotherapy
title_fullStr Predictive performance of different NTCP techniques for radiation-induced esophagitis in NSCLC patients receiving proton radiotherapy
title_full_unstemmed Predictive performance of different NTCP techniques for radiation-induced esophagitis in NSCLC patients receiving proton radiotherapy
title_short Predictive performance of different NTCP techniques for radiation-induced esophagitis in NSCLC patients receiving proton radiotherapy
title_sort predictive performance of different ntcp techniques for radiation-induced esophagitis in nsclc patients receiving proton radiotherapy
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9163134/
https://www.ncbi.nlm.nih.gov/pubmed/35655073
http://dx.doi.org/10.1038/s41598-022-12898-8
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