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Development and validation of a nomogram for predicting metachronous peritoneal metastasis in colorectal cancer: A retrospective study

BACKGROUND: Peritoneal metastasis (PM) after primary surgery for colorectal cancer (CRC) has the worst prognosis. Prediction and early detection of metachronous PM (m-PM) have an important role in improving postoperative prognosis of CRC. However, commonly used imaging methods have limited sensitivi...

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Autores principales: Ban, Bo, Shang, An, Shi, Jian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Baishideng Publishing Group Inc 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9850763/
https://www.ncbi.nlm.nih.gov/pubmed/36684053
http://dx.doi.org/10.4251/wjgo.v15.i1.112
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author Ban, Bo
Shang, An
Shi, Jian
author_facet Ban, Bo
Shang, An
Shi, Jian
author_sort Ban, Bo
collection PubMed
description BACKGROUND: Peritoneal metastasis (PM) after primary surgery for colorectal cancer (CRC) has the worst prognosis. Prediction and early detection of metachronous PM (m-PM) have an important role in improving postoperative prognosis of CRC. However, commonly used imaging methods have limited sensitivity to detect PM early. We aimed to establish a nomogram model to evaluate the individual probability of m-PM to facilitate early interventions for high-risk patients. AIM: To establish and validate a nomogram model for predicting the occurrence of m-PM in CRC within 3 years after surgery. METHODS: We used the clinical data of 878 patients at the Second Hospital of Jilin University, between January 1, 2014 and January 31, 2019. The patients were randomly divided into training and validation cohorts at a ratio of 2:1. The least absolute shrinkage and selection operator (LASSO) regression was performed to identify the variables with nonzero coefficients to predict the risk of m-PM. Multivariate logistic regression was used to verify the selected variables and to develop the predictive nomogram model. Harrell’s concordance index, receiver operating characteristic curve, Brier score, and decision curve analysis (DCA) were used to evaluate discrimination, distinctiveness, validity, and clinical utility of this nomogram model. The model was verified internally using bootstrapping method and verified externally using validation cohort. RESULTS: LASSO regression analysis identified six potential risk factors with nonzero coefficients. Multivariate logistic regression confirmed the risk factors to be independent. Based on the results of two regression analyses, a nomogram model was established. The nomogram included six predictors: Tumor site, histological type, pathological T stage, carbohydrate antigen 125, v-raf murine sarcoma viral oncogene homolog B mutation and microsatellite instability status. The model achieved good predictive accuracy on both the training and validation datasets. The C-index, area under the curve, and Brier scores were 0.796, 0.796 [95% confidence interval (CI) 0.735-0.856], and 0.081 for the training cohort and 0.782, 0.782 (95%CI 0.690-0.874), and 0.089 for the validation cohort, respectively. DCA showed that when the threshold probability was between 0.01 and 0.90, using this model to predict m-PM achieved a net clinical benefit. CONCLUSION: We have established and validated a nomogram model to predict m-PM in patients undergoing curative surgery, which shows good discrimination and high accuracy.
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spelling pubmed-98507632023-01-20 Development and validation of a nomogram for predicting metachronous peritoneal metastasis in colorectal cancer: A retrospective study Ban, Bo Shang, An Shi, Jian World J Gastrointest Oncol Retrospective Study BACKGROUND: Peritoneal metastasis (PM) after primary surgery for colorectal cancer (CRC) has the worst prognosis. Prediction and early detection of metachronous PM (m-PM) have an important role in improving postoperative prognosis of CRC. However, commonly used imaging methods have limited sensitivity to detect PM early. We aimed to establish a nomogram model to evaluate the individual probability of m-PM to facilitate early interventions for high-risk patients. AIM: To establish and validate a nomogram model for predicting the occurrence of m-PM in CRC within 3 years after surgery. METHODS: We used the clinical data of 878 patients at the Second Hospital of Jilin University, between January 1, 2014 and January 31, 2019. The patients were randomly divided into training and validation cohorts at a ratio of 2:1. The least absolute shrinkage and selection operator (LASSO) regression was performed to identify the variables with nonzero coefficients to predict the risk of m-PM. Multivariate logistic regression was used to verify the selected variables and to develop the predictive nomogram model. Harrell’s concordance index, receiver operating characteristic curve, Brier score, and decision curve analysis (DCA) were used to evaluate discrimination, distinctiveness, validity, and clinical utility of this nomogram model. The model was verified internally using bootstrapping method and verified externally using validation cohort. RESULTS: LASSO regression analysis identified six potential risk factors with nonzero coefficients. Multivariate logistic regression confirmed the risk factors to be independent. Based on the results of two regression analyses, a nomogram model was established. The nomogram included six predictors: Tumor site, histological type, pathological T stage, carbohydrate antigen 125, v-raf murine sarcoma viral oncogene homolog B mutation and microsatellite instability status. The model achieved good predictive accuracy on both the training and validation datasets. The C-index, area under the curve, and Brier scores were 0.796, 0.796 [95% confidence interval (CI) 0.735-0.856], and 0.081 for the training cohort and 0.782, 0.782 (95%CI 0.690-0.874), and 0.089 for the validation cohort, respectively. DCA showed that when the threshold probability was between 0.01 and 0.90, using this model to predict m-PM achieved a net clinical benefit. CONCLUSION: We have established and validated a nomogram model to predict m-PM in patients undergoing curative surgery, which shows good discrimination and high accuracy. Baishideng Publishing Group Inc 2023-01-15 2023-01-15 /pmc/articles/PMC9850763/ /pubmed/36684053 http://dx.doi.org/10.4251/wjgo.v15.i1.112 Text en ©The Author(s) 2023. Published by Baishideng Publishing Group Inc. All rights reserved. https://creativecommons.org/licenses/by-nc/4.0/This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial.
spellingShingle Retrospective Study
Ban, Bo
Shang, An
Shi, Jian
Development and validation of a nomogram for predicting metachronous peritoneal metastasis in colorectal cancer: A retrospective study
title Development and validation of a nomogram for predicting metachronous peritoneal metastasis in colorectal cancer: A retrospective study
title_full Development and validation of a nomogram for predicting metachronous peritoneal metastasis in colorectal cancer: A retrospective study
title_fullStr Development and validation of a nomogram for predicting metachronous peritoneal metastasis in colorectal cancer: A retrospective study
title_full_unstemmed Development and validation of a nomogram for predicting metachronous peritoneal metastasis in colorectal cancer: A retrospective study
title_short Development and validation of a nomogram for predicting metachronous peritoneal metastasis in colorectal cancer: A retrospective study
title_sort development and validation of a nomogram for predicting metachronous peritoneal metastasis in colorectal cancer: a retrospective study
topic Retrospective Study
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9850763/
https://www.ncbi.nlm.nih.gov/pubmed/36684053
http://dx.doi.org/10.4251/wjgo.v15.i1.112
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