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Development and Validation of a Recurrence-Free Survival Prediction Model for Locally Advanced Esophageal Squamous Cell Carcinoma with Neoadjuvant Chemoradiotherapy

BACKGROUND: A recurrence-free survival (RFS) prediction model was developed and validated for patients with locally advanced esophageal squamous cell carcinoma treated with neoadjuvant chemoradiotherapy (NCRT) in combination with surgery. PATIENTS AND METHODS: We included 282 patients with esophagea...

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Autores principales: Zhou, Yehan, He, Wenwu, Guo, Peng, Zhou, Chengmin, Luo, Min, Liu, Ying, Yang, Hong, Qin, Sheng, Leng, Xuefeng, Huang, Zongyao, Liu, Yang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10695895/
https://www.ncbi.nlm.nih.gov/pubmed/37751117
http://dx.doi.org/10.1245/s10434-023-14308-3
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author Zhou, Yehan
He, Wenwu
Guo, Peng
Zhou, Chengmin
Luo, Min
Liu, Ying
Yang, Hong
Qin, Sheng
Leng, Xuefeng
Huang, Zongyao
Liu, Yang
author_facet Zhou, Yehan
He, Wenwu
Guo, Peng
Zhou, Chengmin
Luo, Min
Liu, Ying
Yang, Hong
Qin, Sheng
Leng, Xuefeng
Huang, Zongyao
Liu, Yang
author_sort Zhou, Yehan
collection PubMed
description BACKGROUND: A recurrence-free survival (RFS) prediction model was developed and validated for patients with locally advanced esophageal squamous cell carcinoma treated with neoadjuvant chemoradiotherapy (NCRT) in combination with surgery. PATIENTS AND METHODS: We included 282 patients with esophageal squamous cell carcinoma who received neoadjuvant chemoradiotherapy (NCRT) combined with surgery, constructed three models incorporating pathological factors, investigated the discrimination and calibration of each model, and compared the clinical utility of each model using the net reclassification index (NRI) and the integrated discrimination index (IDI). RESULTS: Multivariable analysis showed that pathologic complete response (pCR) and lymph node tumor regression grading (LN–TRG) (p < 0.05) were independent prognostic factors for RFS. LASSO regression screened six correlates of LN-TRG, vascular invasion, nerve invasion, degree of differentiation, platelet grade, and a total diameter of residual cancer in lymph nodes to build model three, which was consistent in terms of efficacy in the training set and validation set. Kaplan–Meier (K–M) curves showed that all three models were able to distinguish well between high- and low-risk groups (p < 0.01). The NRI and IDI showed that the clinical utility of model 2 was slightly better than that of model 1 (p > 0.05), and model 3 was significantly better than that of model 2 (p < 0.05). CONCLUSIONS: Clinical prediction models incorporating LN-TRG factors have high predictive efficacy, can help identify patients at high risk of recurrence after neoadjuvant therapy, and can be used as a supplement to the  AJCC/TNM staging system while offering a scientific rationale for early postoperative intervention. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1245/s10434-023-14308-3.
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spelling pubmed-106958952023-12-06 Development and Validation of a Recurrence-Free Survival Prediction Model for Locally Advanced Esophageal Squamous Cell Carcinoma with Neoadjuvant Chemoradiotherapy Zhou, Yehan He, Wenwu Guo, Peng Zhou, Chengmin Luo, Min Liu, Ying Yang, Hong Qin, Sheng Leng, Xuefeng Huang, Zongyao Liu, Yang Ann Surg Oncol Thoracic Oncology BACKGROUND: A recurrence-free survival (RFS) prediction model was developed and validated for patients with locally advanced esophageal squamous cell carcinoma treated with neoadjuvant chemoradiotherapy (NCRT) in combination with surgery. PATIENTS AND METHODS: We included 282 patients with esophageal squamous cell carcinoma who received neoadjuvant chemoradiotherapy (NCRT) combined with surgery, constructed three models incorporating pathological factors, investigated the discrimination and calibration of each model, and compared the clinical utility of each model using the net reclassification index (NRI) and the integrated discrimination index (IDI). RESULTS: Multivariable analysis showed that pathologic complete response (pCR) and lymph node tumor regression grading (LN–TRG) (p < 0.05) were independent prognostic factors for RFS. LASSO regression screened six correlates of LN-TRG, vascular invasion, nerve invasion, degree of differentiation, platelet grade, and a total diameter of residual cancer in lymph nodes to build model three, which was consistent in terms of efficacy in the training set and validation set. Kaplan–Meier (K–M) curves showed that all three models were able to distinguish well between high- and low-risk groups (p < 0.01). The NRI and IDI showed that the clinical utility of model 2 was slightly better than that of model 1 (p > 0.05), and model 3 was significantly better than that of model 2 (p < 0.05). CONCLUSIONS: Clinical prediction models incorporating LN-TRG factors have high predictive efficacy, can help identify patients at high risk of recurrence after neoadjuvant therapy, and can be used as a supplement to the  AJCC/TNM staging system while offering a scientific rationale for early postoperative intervention. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1245/s10434-023-14308-3. Springer International Publishing 2023-09-26 2024 /pmc/articles/PMC10695895/ /pubmed/37751117 http://dx.doi.org/10.1245/s10434-023-14308-3 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 Thoracic Oncology
Zhou, Yehan
He, Wenwu
Guo, Peng
Zhou, Chengmin
Luo, Min
Liu, Ying
Yang, Hong
Qin, Sheng
Leng, Xuefeng
Huang, Zongyao
Liu, Yang
Development and Validation of a Recurrence-Free Survival Prediction Model for Locally Advanced Esophageal Squamous Cell Carcinoma with Neoadjuvant Chemoradiotherapy
title Development and Validation of a Recurrence-Free Survival Prediction Model for Locally Advanced Esophageal Squamous Cell Carcinoma with Neoadjuvant Chemoradiotherapy
title_full Development and Validation of a Recurrence-Free Survival Prediction Model for Locally Advanced Esophageal Squamous Cell Carcinoma with Neoadjuvant Chemoradiotherapy
title_fullStr Development and Validation of a Recurrence-Free Survival Prediction Model for Locally Advanced Esophageal Squamous Cell Carcinoma with Neoadjuvant Chemoradiotherapy
title_full_unstemmed Development and Validation of a Recurrence-Free Survival Prediction Model for Locally Advanced Esophageal Squamous Cell Carcinoma with Neoadjuvant Chemoradiotherapy
title_short Development and Validation of a Recurrence-Free Survival Prediction Model for Locally Advanced Esophageal Squamous Cell Carcinoma with Neoadjuvant Chemoradiotherapy
title_sort development and validation of a recurrence-free survival prediction model for locally advanced esophageal squamous cell carcinoma with neoadjuvant chemoradiotherapy
topic Thoracic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10695895/
https://www.ncbi.nlm.nih.gov/pubmed/37751117
http://dx.doi.org/10.1245/s10434-023-14308-3
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