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Construction and validation of a nomogram model for predicting the overall survival of colorectal cancer patients

BACKGROUND: Colorectal cancer (CRC) is a frequent cancer worldwide with varied survival outcomes. OBJECTIVE: We aimed to develop a nomogram model to predict the overall survival (OS) of CRC patients after surgery. DESIGN: This is a retrospective study. SETTING: This study was conducted from 2015 to...

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Autores principales: Peiyuan, Guo, xuhua, Hu, Ganlin, Guo, Xu, Yin, Zining, Liu, Jiachao, Han, Bin, Yu, Guiying, Wang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10311858/
https://www.ncbi.nlm.nih.gov/pubmed/37386397
http://dx.doi.org/10.1186/s12893-023-02018-2
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author Peiyuan, Guo
xuhua, Hu
Ganlin, Guo
Xu, Yin
Zining, Liu
Jiachao, Han
Bin, Yu
Guiying, Wang
author_facet Peiyuan, Guo
xuhua, Hu
Ganlin, Guo
Xu, Yin
Zining, Liu
Jiachao, Han
Bin, Yu
Guiying, Wang
author_sort Peiyuan, Guo
collection PubMed
description BACKGROUND: Colorectal cancer (CRC) is a frequent cancer worldwide with varied survival outcomes. OBJECTIVE: We aimed to develop a nomogram model to predict the overall survival (OS) of CRC patients after surgery. DESIGN: This is a retrospective study. SETTING: This study was conducted from 2015 to 2016 in a single tertiary center for CRC. PATIENTS: CRC patients who underwent surgery between 2015 and 2016 were enrolled and randomly assigned into the training (n = 480) and validation (n = 206) groups. The risk score of each subject was calculated based on the nomogram. All participants were categorized into two subgroups according to the median value of the score. MAIN OUTCOME MEASURES: The clinical characteristics of all patients were collected, significant prognostic variables were determined by univariate analysis. Least absolute shrinkage and selection operator (LASSO) regression was applied for variable selection. The tuning parameter (λ) for LASSO regression was determined by cross-validation. Independent prognostic variables determined by multivariable analysis were used to establish the nomogram. The predictive capacity of the model was assessed by risk group stratification. RESULTS: Infiltration depth, macroscopic classification, BRAF, carbohydrate antigen 19 − 9 (CA-199) levels, N stage, M stage, TNM stage, carcinoembryonic antigen levels, number of positive lymph nodes, vascular tumor thrombus, and lymph node metastasis were independent prognostic factors. The nomogram established based on these factors exhibited good discriminatory capacity. The concordance indices for the training and validation groups were 0.796 and 0.786, respectively. The calibration curve suggested favorable agreement between predictions and observations. Moreover, the OS of different risk subgroups was significantly different. LIMITATIONS: The limitations of this work included small sample size and single-center design. Also, some prognostic factors could not be included due to the retrospective design. CONCLUSIONS: A prognostic nomogram for predicting the OS of CRC patients after surgery was developed, which might be helpful for evaluating the prognosis of CRC patients.
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spelling pubmed-103118582023-07-01 Construction and validation of a nomogram model for predicting the overall survival of colorectal cancer patients Peiyuan, Guo xuhua, Hu Ganlin, Guo Xu, Yin Zining, Liu Jiachao, Han Bin, Yu Guiying, Wang BMC Surg Research BACKGROUND: Colorectal cancer (CRC) is a frequent cancer worldwide with varied survival outcomes. OBJECTIVE: We aimed to develop a nomogram model to predict the overall survival (OS) of CRC patients after surgery. DESIGN: This is a retrospective study. SETTING: This study was conducted from 2015 to 2016 in a single tertiary center for CRC. PATIENTS: CRC patients who underwent surgery between 2015 and 2016 were enrolled and randomly assigned into the training (n = 480) and validation (n = 206) groups. The risk score of each subject was calculated based on the nomogram. All participants were categorized into two subgroups according to the median value of the score. MAIN OUTCOME MEASURES: The clinical characteristics of all patients were collected, significant prognostic variables were determined by univariate analysis. Least absolute shrinkage and selection operator (LASSO) regression was applied for variable selection. The tuning parameter (λ) for LASSO regression was determined by cross-validation. Independent prognostic variables determined by multivariable analysis were used to establish the nomogram. The predictive capacity of the model was assessed by risk group stratification. RESULTS: Infiltration depth, macroscopic classification, BRAF, carbohydrate antigen 19 − 9 (CA-199) levels, N stage, M stage, TNM stage, carcinoembryonic antigen levels, number of positive lymph nodes, vascular tumor thrombus, and lymph node metastasis were independent prognostic factors. The nomogram established based on these factors exhibited good discriminatory capacity. The concordance indices for the training and validation groups were 0.796 and 0.786, respectively. The calibration curve suggested favorable agreement between predictions and observations. Moreover, the OS of different risk subgroups was significantly different. LIMITATIONS: The limitations of this work included small sample size and single-center design. Also, some prognostic factors could not be included due to the retrospective design. CONCLUSIONS: A prognostic nomogram for predicting the OS of CRC patients after surgery was developed, which might be helpful for evaluating the prognosis of CRC patients. BioMed Central 2023-06-29 /pmc/articles/PMC10311858/ /pubmed/37386397 http://dx.doi.org/10.1186/s12893-023-02018-2 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/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Peiyuan, Guo
xuhua, Hu
Ganlin, Guo
Xu, Yin
Zining, Liu
Jiachao, Han
Bin, Yu
Guiying, Wang
Construction and validation of a nomogram model for predicting the overall survival of colorectal cancer patients
title Construction and validation of a nomogram model for predicting the overall survival of colorectal cancer patients
title_full Construction and validation of a nomogram model for predicting the overall survival of colorectal cancer patients
title_fullStr Construction and validation of a nomogram model for predicting the overall survival of colorectal cancer patients
title_full_unstemmed Construction and validation of a nomogram model for predicting the overall survival of colorectal cancer patients
title_short Construction and validation of a nomogram model for predicting the overall survival of colorectal cancer patients
title_sort construction and validation of a nomogram model for predicting the overall survival of colorectal cancer patients
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10311858/
https://www.ncbi.nlm.nih.gov/pubmed/37386397
http://dx.doi.org/10.1186/s12893-023-02018-2
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