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Prognostic impact of examined lymph-node count for patients with esophageal cancer: development and validation prediction model

Esophageal cancer (EC) is a malignant tumor with high mortality. We aimed to find the optimal examined lymph node (ELN) count threshold and develop a model to predict survival of patients after radical esophagectomy. Two cohorts were analyzed: the training cohort which included 734 EC patients from...

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Autores principales: Yuan, Shasha, Wei, Chen, Wang, Mengyu, Deng, Wenying, Zhang, Chi, Li, Ning, Luo, Suxia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9831985/
https://www.ncbi.nlm.nih.gov/pubmed/36627338
http://dx.doi.org/10.1038/s41598-022-27150-6
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author Yuan, Shasha
Wei, Chen
Wang, Mengyu
Deng, Wenying
Zhang, Chi
Li, Ning
Luo, Suxia
author_facet Yuan, Shasha
Wei, Chen
Wang, Mengyu
Deng, Wenying
Zhang, Chi
Li, Ning
Luo, Suxia
author_sort Yuan, Shasha
collection PubMed
description Esophageal cancer (EC) is a malignant tumor with high mortality. We aimed to find the optimal examined lymph node (ELN) count threshold and develop a model to predict survival of patients after radical esophagectomy. Two cohorts were analyzed: the training cohort which included 734 EC patients from the Chinese registry and the external testing cohort which included 3208 EC patients from the Surveillance, Epidemiology, and End Results (SEER) registry. Cox proportional hazards regression analysis was used to determine the prognostic value of ELNs. The cut-off point of the ELNs count was determined using R-statistical software. The prediction model was developed using random survival forest (RSF) algorithm. Higher ELNs count was significantly associated with better survival in both cohorts (training cohort: HR = 0.98, CI = 0.97–0.99, P < 0.01; testing cohort: HR = 0.98, CI = 0.98–0.99, P < 0.01) and the cut-off point was 18 (training cohort: P < 0.01; testing cohort: P < 0.01). We developed the RSF model with high prediction accuracy (AUC: training cohort: 87.5; testing cohort: 79.3) and low Brier Score (training cohort: 0.122; testing cohort: 0.152). The ELNs count beyond 18 is associated with better overall survival. The RSF model has preferable clinical capability in terms of individual prognosis assessment in patients after radical esophagectomy.
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spelling pubmed-98319852023-01-12 Prognostic impact of examined lymph-node count for patients with esophageal cancer: development and validation prediction model Yuan, Shasha Wei, Chen Wang, Mengyu Deng, Wenying Zhang, Chi Li, Ning Luo, Suxia Sci Rep Article Esophageal cancer (EC) is a malignant tumor with high mortality. We aimed to find the optimal examined lymph node (ELN) count threshold and develop a model to predict survival of patients after radical esophagectomy. Two cohorts were analyzed: the training cohort which included 734 EC patients from the Chinese registry and the external testing cohort which included 3208 EC patients from the Surveillance, Epidemiology, and End Results (SEER) registry. Cox proportional hazards regression analysis was used to determine the prognostic value of ELNs. The cut-off point of the ELNs count was determined using R-statistical software. The prediction model was developed using random survival forest (RSF) algorithm. Higher ELNs count was significantly associated with better survival in both cohorts (training cohort: HR = 0.98, CI = 0.97–0.99, P < 0.01; testing cohort: HR = 0.98, CI = 0.98–0.99, P < 0.01) and the cut-off point was 18 (training cohort: P < 0.01; testing cohort: P < 0.01). We developed the RSF model with high prediction accuracy (AUC: training cohort: 87.5; testing cohort: 79.3) and low Brier Score (training cohort: 0.122; testing cohort: 0.152). The ELNs count beyond 18 is associated with better overall survival. The RSF model has preferable clinical capability in terms of individual prognosis assessment in patients after radical esophagectomy. Nature Publishing Group UK 2023-01-10 /pmc/articles/PMC9831985/ /pubmed/36627338 http://dx.doi.org/10.1038/s41598-022-27150-6 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
Yuan, Shasha
Wei, Chen
Wang, Mengyu
Deng, Wenying
Zhang, Chi
Li, Ning
Luo, Suxia
Prognostic impact of examined lymph-node count for patients with esophageal cancer: development and validation prediction model
title Prognostic impact of examined lymph-node count for patients with esophageal cancer: development and validation prediction model
title_full Prognostic impact of examined lymph-node count for patients with esophageal cancer: development and validation prediction model
title_fullStr Prognostic impact of examined lymph-node count for patients with esophageal cancer: development and validation prediction model
title_full_unstemmed Prognostic impact of examined lymph-node count for patients with esophageal cancer: development and validation prediction model
title_short Prognostic impact of examined lymph-node count for patients with esophageal cancer: development and validation prediction model
title_sort prognostic impact of examined lymph-node count for patients with esophageal cancer: development and validation prediction model
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9831985/
https://www.ncbi.nlm.nih.gov/pubmed/36627338
http://dx.doi.org/10.1038/s41598-022-27150-6
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