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A nomogram model for predicting distant metastasis of newly diagnosed colorectal cancer based on clinical features

OBJECTIVE: Colorectal cancer is one of the most common primary malignancies and the third most common cause of cancer death in both men and women in the United States. Among people diagnosed with initial colorectal cancer, 22% had metastatic colorectal cancer, while the 5-year survival rate was less...

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Autores principales: He, Jiang-Hua, Cao, Cong, Ding, Yang, Yi, Yun, Lv, Yu-Qing, Wang, Chun, Chang, Ying
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10311479/
https://www.ncbi.nlm.nih.gov/pubmed/37397373
http://dx.doi.org/10.3389/fonc.2023.1186298
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author He, Jiang-Hua
Cao, Cong
Ding, Yang
Yi, Yun
Lv, Yu-Qing
Wang, Chun
Chang, Ying
author_facet He, Jiang-Hua
Cao, Cong
Ding, Yang
Yi, Yun
Lv, Yu-Qing
Wang, Chun
Chang, Ying
author_sort He, Jiang-Hua
collection PubMed
description OBJECTIVE: Colorectal cancer is one of the most common primary malignancies and the third most common cause of cancer death in both men and women in the United States. Among people diagnosed with initial colorectal cancer, 22% had metastatic colorectal cancer, while the 5-year survival rate was less than 20%. The purpose of this study is to develop a nomogram for predicting distant metastasis in newly diagnosed colorectal cancer patients and to identify high-risk groups. METHODS: We retrospectively reviewed the data of patients who were diagnosed with colorectal cancer at Zhong nan Hospital of Wuhan University and People’s Hospital of Gansu Province between January 2016 and December 2021. Risk predictors for distant metastasis from colorectal patients were determined by the univariate and multivariate logistic regression analyses. Nomograms were developed to predict the probabilities of distant metastatic sites of colorectal cancer patients and evaluated by calibration curves, receiver operating characteristic curves, and decision curve analysis (DCA). RESULTS: A total of 327 cases were included in this study: 224 colorectal cancer patients from Zhong nan Hospital of Wuhan University were incorporated into the training set, and 103 colorectal cancer patients from Gansu Provincial People’s Hospital were incorporated into the testing set. By univariate logistic regression analysis, platelet (PLT) level (p = 0.009), carcinoembryonic antigen (CEA) level (p = 0.032), histological grade (p < 0.001), colorectal cancer tumor markers (p < 0.001), N stage (p < 0.001), and tumor site (p = 0.005) were associated with distant metastasis in colorectal cancer patients. Multivariate logistic regression analysis showed that N stage (p < 0.001), histological grade (p = 0.026), and colorectal cancer markers (p < 0.001) were independent predictors of distant metastasis in patients initially diagnosed with colorectal cancer. The above six risk factors were used to predict distant metastasis of newly diagnosed colorectal cancer. The C-indexes for the prediction of the nomogram were 0.902 (95% confidence interval (CI), 0.857–0.948). CONCLUSION: The nomogram showed excellent accuracy in predicting distant metastatic sites, and clinical utility may facilitate clinical decision-making.
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spelling pubmed-103114792023-07-01 A nomogram model for predicting distant metastasis of newly diagnosed colorectal cancer based on clinical features He, Jiang-Hua Cao, Cong Ding, Yang Yi, Yun Lv, Yu-Qing Wang, Chun Chang, Ying Front Oncol Oncology OBJECTIVE: Colorectal cancer is one of the most common primary malignancies and the third most common cause of cancer death in both men and women in the United States. Among people diagnosed with initial colorectal cancer, 22% had metastatic colorectal cancer, while the 5-year survival rate was less than 20%. The purpose of this study is to develop a nomogram for predicting distant metastasis in newly diagnosed colorectal cancer patients and to identify high-risk groups. METHODS: We retrospectively reviewed the data of patients who were diagnosed with colorectal cancer at Zhong nan Hospital of Wuhan University and People’s Hospital of Gansu Province between January 2016 and December 2021. Risk predictors for distant metastasis from colorectal patients were determined by the univariate and multivariate logistic regression analyses. Nomograms were developed to predict the probabilities of distant metastatic sites of colorectal cancer patients and evaluated by calibration curves, receiver operating characteristic curves, and decision curve analysis (DCA). RESULTS: A total of 327 cases were included in this study: 224 colorectal cancer patients from Zhong nan Hospital of Wuhan University were incorporated into the training set, and 103 colorectal cancer patients from Gansu Provincial People’s Hospital were incorporated into the testing set. By univariate logistic regression analysis, platelet (PLT) level (p = 0.009), carcinoembryonic antigen (CEA) level (p = 0.032), histological grade (p < 0.001), colorectal cancer tumor markers (p < 0.001), N stage (p < 0.001), and tumor site (p = 0.005) were associated with distant metastasis in colorectal cancer patients. Multivariate logistic regression analysis showed that N stage (p < 0.001), histological grade (p = 0.026), and colorectal cancer markers (p < 0.001) were independent predictors of distant metastasis in patients initially diagnosed with colorectal cancer. The above six risk factors were used to predict distant metastasis of newly diagnosed colorectal cancer. The C-indexes for the prediction of the nomogram were 0.902 (95% confidence interval (CI), 0.857–0.948). CONCLUSION: The nomogram showed excellent accuracy in predicting distant metastatic sites, and clinical utility may facilitate clinical decision-making. Frontiers Media S.A. 2023-06-15 /pmc/articles/PMC10311479/ /pubmed/37397373 http://dx.doi.org/10.3389/fonc.2023.1186298 Text en Copyright © 2023 He, Cao, Ding, Yi, Lv, Wang and Chang 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 Oncology
He, Jiang-Hua
Cao, Cong
Ding, Yang
Yi, Yun
Lv, Yu-Qing
Wang, Chun
Chang, Ying
A nomogram model for predicting distant metastasis of newly diagnosed colorectal cancer based on clinical features
title A nomogram model for predicting distant metastasis of newly diagnosed colorectal cancer based on clinical features
title_full A nomogram model for predicting distant metastasis of newly diagnosed colorectal cancer based on clinical features
title_fullStr A nomogram model for predicting distant metastasis of newly diagnosed colorectal cancer based on clinical features
title_full_unstemmed A nomogram model for predicting distant metastasis of newly diagnosed colorectal cancer based on clinical features
title_short A nomogram model for predicting distant metastasis of newly diagnosed colorectal cancer based on clinical features
title_sort nomogram model for predicting distant metastasis of newly diagnosed colorectal cancer based on clinical features
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10311479/
https://www.ncbi.nlm.nih.gov/pubmed/37397373
http://dx.doi.org/10.3389/fonc.2023.1186298
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