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Analysis of Prognostic Factors of Rectal Cancer and Construction of a Prognostic Prediction Model Based on Bayesian Network

BACKGROUND: The existing prognostic models of rectal cancer after radical resection ignored the relationships among prognostic factors and their mutual effects on prognosis. Thus, a new modeling method is required to remedy this defect. The present study aimed to construct a new prognostic predictio...

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Autores principales: Li, Ruikai, Zhang, Chi, Du, Kunli, Dan, Hanjun, Ding, Ruxin, Cai, Zhiqiang, Duan, Lili, Xie, Zhenyu, Zheng, Gaozan, Wu, Hongze, Ren, Guangming, Dou, Xinyu, Feng, Fan, Zheng, Jianyong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9247333/
https://www.ncbi.nlm.nih.gov/pubmed/35784233
http://dx.doi.org/10.3389/fpubh.2022.842970
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author Li, Ruikai
Zhang, Chi
Du, Kunli
Dan, Hanjun
Ding, Ruxin
Cai, Zhiqiang
Duan, Lili
Xie, Zhenyu
Zheng, Gaozan
Wu, Hongze
Ren, Guangming
Dou, Xinyu
Feng, Fan
Zheng, Jianyong
author_facet Li, Ruikai
Zhang, Chi
Du, Kunli
Dan, Hanjun
Ding, Ruxin
Cai, Zhiqiang
Duan, Lili
Xie, Zhenyu
Zheng, Gaozan
Wu, Hongze
Ren, Guangming
Dou, Xinyu
Feng, Fan
Zheng, Jianyong
author_sort Li, Ruikai
collection PubMed
description BACKGROUND: The existing prognostic models of rectal cancer after radical resection ignored the relationships among prognostic factors and their mutual effects on prognosis. Thus, a new modeling method is required to remedy this defect. The present study aimed to construct a new prognostic prediction model based on the Bayesian network (BN), a machine learning tool for data mining, clinical decision-making, and prognostic prediction. METHODS: From January 2015 to December 2017, the clinical data of 705 patients with rectal cancer who underwent radical resection were analyzed. The entire cohort was divided into training and testing datasets. A new prognostic prediction model based on BN was constructed and compared with a nomogram. RESULTS: A univariate analysis showed that age, Carcinoembryonic antigen (CEA), Carbohydrate antigen19-9 (CA19-9), Carbohydrate antigen 125 (CA125), preoperative chemotherapy, macropathology type, tumor size, differentiation status, T stage, N stage, vascular invasion, KRAS mutation, and postoperative chemotherapy were associated with overall survival (OS) of the training dataset. Based on the above-mentioned variables, a 3-year OS prognostic prediction BN model of the training dataset was constructed using the Tree Augmented Naïve Bayes method. In addition, age, CEA, CA19-9, CA125, differentiation status, T stage, N stage, KRAS mutation, and postoperative chemotherapy were identified as independent prognostic factors of the training dataset through multivariate Cox regression and were used to construct a nomogram. Then, based on the testing dataset, the two models were evaluated using the receiver operating characteristic (ROC) curve. The results showed that the area under the curve (AUC) of ROC of the BN model and nomogram was 80.11 and 74.23%, respectively. CONCLUSION: The present study established a BN model for prognostic prediction of rectal cancer for the first time, which was demonstrated to be more accurate than a nomogram.
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spelling pubmed-92473332022-07-02 Analysis of Prognostic Factors of Rectal Cancer and Construction of a Prognostic Prediction Model Based on Bayesian Network Li, Ruikai Zhang, Chi Du, Kunli Dan, Hanjun Ding, Ruxin Cai, Zhiqiang Duan, Lili Xie, Zhenyu Zheng, Gaozan Wu, Hongze Ren, Guangming Dou, Xinyu Feng, Fan Zheng, Jianyong Front Public Health Public Health BACKGROUND: The existing prognostic models of rectal cancer after radical resection ignored the relationships among prognostic factors and their mutual effects on prognosis. Thus, a new modeling method is required to remedy this defect. The present study aimed to construct a new prognostic prediction model based on the Bayesian network (BN), a machine learning tool for data mining, clinical decision-making, and prognostic prediction. METHODS: From January 2015 to December 2017, the clinical data of 705 patients with rectal cancer who underwent radical resection were analyzed. The entire cohort was divided into training and testing datasets. A new prognostic prediction model based on BN was constructed and compared with a nomogram. RESULTS: A univariate analysis showed that age, Carcinoembryonic antigen (CEA), Carbohydrate antigen19-9 (CA19-9), Carbohydrate antigen 125 (CA125), preoperative chemotherapy, macropathology type, tumor size, differentiation status, T stage, N stage, vascular invasion, KRAS mutation, and postoperative chemotherapy were associated with overall survival (OS) of the training dataset. Based on the above-mentioned variables, a 3-year OS prognostic prediction BN model of the training dataset was constructed using the Tree Augmented Naïve Bayes method. In addition, age, CEA, CA19-9, CA125, differentiation status, T stage, N stage, KRAS mutation, and postoperative chemotherapy were identified as independent prognostic factors of the training dataset through multivariate Cox regression and were used to construct a nomogram. Then, based on the testing dataset, the two models were evaluated using the receiver operating characteristic (ROC) curve. The results showed that the area under the curve (AUC) of ROC of the BN model and nomogram was 80.11 and 74.23%, respectively. CONCLUSION: The present study established a BN model for prognostic prediction of rectal cancer for the first time, which was demonstrated to be more accurate than a nomogram. Frontiers Media S.A. 2022-06-17 /pmc/articles/PMC9247333/ /pubmed/35784233 http://dx.doi.org/10.3389/fpubh.2022.842970 Text en Copyright © 2022 Li, Zhang, Du, Dan, Ding, Cai, Duan, Xie, Zheng, Wu, Ren, Dou, Feng and Zheng. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Public Health
Li, Ruikai
Zhang, Chi
Du, Kunli
Dan, Hanjun
Ding, Ruxin
Cai, Zhiqiang
Duan, Lili
Xie, Zhenyu
Zheng, Gaozan
Wu, Hongze
Ren, Guangming
Dou, Xinyu
Feng, Fan
Zheng, Jianyong
Analysis of Prognostic Factors of Rectal Cancer and Construction of a Prognostic Prediction Model Based on Bayesian Network
title Analysis of Prognostic Factors of Rectal Cancer and Construction of a Prognostic Prediction Model Based on Bayesian Network
title_full Analysis of Prognostic Factors of Rectal Cancer and Construction of a Prognostic Prediction Model Based on Bayesian Network
title_fullStr Analysis of Prognostic Factors of Rectal Cancer and Construction of a Prognostic Prediction Model Based on Bayesian Network
title_full_unstemmed Analysis of Prognostic Factors of Rectal Cancer and Construction of a Prognostic Prediction Model Based on Bayesian Network
title_short Analysis of Prognostic Factors of Rectal Cancer and Construction of a Prognostic Prediction Model Based on Bayesian Network
title_sort analysis of prognostic factors of rectal cancer and construction of a prognostic prediction model based on bayesian network
topic Public Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9247333/
https://www.ncbi.nlm.nih.gov/pubmed/35784233
http://dx.doi.org/10.3389/fpubh.2022.842970
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