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Predicting survival of advanced laryngeal squamous cell carcinoma: comparison of machine learning models and Cox regression models

Laryngeal squamous cell carcinoma (LSCC) is a common tumor type. High recurrence rates remain an important factor affecting the survival and quality of life of advanced LSCC patients. We aimed to build a new nomogram and a random survival forest model using machine learning to predict the risk of LS...

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Autores principales: Zhang, Yi-Fan, Shen, Yu-Jie, Huang, Qiang, Wu, Chun-Ping, Zhou, Liang, Ren, Heng-Lei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10613248/
https://www.ncbi.nlm.nih.gov/pubmed/37898687
http://dx.doi.org/10.1038/s41598-023-45831-8
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author Zhang, Yi-Fan
Shen, Yu-Jie
Huang, Qiang
Wu, Chun-Ping
Zhou, Liang
Ren, Heng-Lei
author_facet Zhang, Yi-Fan
Shen, Yu-Jie
Huang, Qiang
Wu, Chun-Ping
Zhou, Liang
Ren, Heng-Lei
author_sort Zhang, Yi-Fan
collection PubMed
description Laryngeal squamous cell carcinoma (LSCC) is a common tumor type. High recurrence rates remain an important factor affecting the survival and quality of life of advanced LSCC patients. We aimed to build a new nomogram and a random survival forest model using machine learning to predict the risk of LSCC progress. The study included 671 patients with AJCC stages III–IV LSCC. To develop a prognostic model, Cox regression analyses were used to assess the relationship between clinic-pathologic factors and disease-free survival (DFS). RSF analysis was also used to predict the DFS of LSCC patients. The ROC curve revealed that the Cox model exhibited good sensitivity and specificity in predicting DFS in the training and validation cohorts (1 year, validation AUC = 0.679, training AUC = 0.693; 3 years, validation AUC = 0.716, training AUC = 0.655; 5 years, validation AUC = 0.717, training AUC = 0.659). Random survival forest analysis showed that N stage, clinical stage, and postoperative chemoradiotherapy were prognostically significant variables associated with survival. The random forest model exhibited better prediction ability than the Cox regression model in the training cohort; however, the two models showed similar prediction ability in the validation cohort.
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spelling pubmed-106132482023-10-30 Predicting survival of advanced laryngeal squamous cell carcinoma: comparison of machine learning models and Cox regression models Zhang, Yi-Fan Shen, Yu-Jie Huang, Qiang Wu, Chun-Ping Zhou, Liang Ren, Heng-Lei Sci Rep Article Laryngeal squamous cell carcinoma (LSCC) is a common tumor type. High recurrence rates remain an important factor affecting the survival and quality of life of advanced LSCC patients. We aimed to build a new nomogram and a random survival forest model using machine learning to predict the risk of LSCC progress. The study included 671 patients with AJCC stages III–IV LSCC. To develop a prognostic model, Cox regression analyses were used to assess the relationship between clinic-pathologic factors and disease-free survival (DFS). RSF analysis was also used to predict the DFS of LSCC patients. The ROC curve revealed that the Cox model exhibited good sensitivity and specificity in predicting DFS in the training and validation cohorts (1 year, validation AUC = 0.679, training AUC = 0.693; 3 years, validation AUC = 0.716, training AUC = 0.655; 5 years, validation AUC = 0.717, training AUC = 0.659). Random survival forest analysis showed that N stage, clinical stage, and postoperative chemoradiotherapy were prognostically significant variables associated with survival. The random forest model exhibited better prediction ability than the Cox regression model in the training cohort; however, the two models showed similar prediction ability in the validation cohort. Nature Publishing Group UK 2023-10-28 /pmc/articles/PMC10613248/ /pubmed/37898687 http://dx.doi.org/10.1038/s41598-023-45831-8 Text en © The Author(s) 2023 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
Zhang, Yi-Fan
Shen, Yu-Jie
Huang, Qiang
Wu, Chun-Ping
Zhou, Liang
Ren, Heng-Lei
Predicting survival of advanced laryngeal squamous cell carcinoma: comparison of machine learning models and Cox regression models
title Predicting survival of advanced laryngeal squamous cell carcinoma: comparison of machine learning models and Cox regression models
title_full Predicting survival of advanced laryngeal squamous cell carcinoma: comparison of machine learning models and Cox regression models
title_fullStr Predicting survival of advanced laryngeal squamous cell carcinoma: comparison of machine learning models and Cox regression models
title_full_unstemmed Predicting survival of advanced laryngeal squamous cell carcinoma: comparison of machine learning models and Cox regression models
title_short Predicting survival of advanced laryngeal squamous cell carcinoma: comparison of machine learning models and Cox regression models
title_sort predicting survival of advanced laryngeal squamous cell carcinoma: comparison of machine learning models and cox regression models
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10613248/
https://www.ncbi.nlm.nih.gov/pubmed/37898687
http://dx.doi.org/10.1038/s41598-023-45831-8
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