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Development and validation of prediction model for early warning of ovarian metastasis risk of endometrial carcinoma

Ovarian metastasis of endometrial carcinoma (EC) patients not only affects the decision of the surgeon, but also has a fatal impact on the fertility and prognosis of patients. This study aimed build a prediction model of ovarian metastasis of EC based on machine learning algorithm for clinical diagn...

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Autores principales: Zhao, Qin, Li, Yinuo, Wang, Tiejun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Lippincott Williams & Wilkins 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10578755/
https://www.ncbi.nlm.nih.gov/pubmed/37832099
http://dx.doi.org/10.1097/MD.0000000000035439
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author Zhao, Qin
Li, Yinuo
Wang, Tiejun
author_facet Zhao, Qin
Li, Yinuo
Wang, Tiejun
author_sort Zhao, Qin
collection PubMed
description Ovarian metastasis of endometrial carcinoma (EC) patients not only affects the decision of the surgeon, but also has a fatal impact on the fertility and prognosis of patients. This study aimed build a prediction model of ovarian metastasis of EC based on machine learning algorithm for clinical diagnosis and treatment management guidance. We retrospectively collected 536 EC patients treated in Hubei Cancer Hospital from January 2017 to October 2022 and 487 EC patients from Tongji Hospital (January 2017 to December 2020) as an external validation queue. The random forest model, gradient elevator model, support vector machine model, artificial neural network model (ANNM), and decision tree model were used to build ovarian metastasis prediction model for EC patients. The predictive efficacy of 5 machine learning models was evaluated by receiver operating characteristic curve and decision curve analysis. For screening of candidate predictors of ovarian metastasis of EC, the degree of tumor differentiation, lymph node metastasis, CA125, HE4, Alb, LH can be used as a potential predictor of ovarian metastasis prediction model in EC patients. The effectiveness of the prediction model constructed by the 5 machine learning algorithms was between (area under curve [AUC]: 0.729, 95% confidence interval [CI]: 0.674–0.784) and (AUC: 0.899, 95% CI: 0.844–0.954) in the training set and internal verification set, respectively. Among them, the ANNM was equipped with the best prediction effectiveness (training set: AUC: 0.899, 95% CI: 0.844–0.954) and (internal verification set: AUC: 0.892, 95% CI: 0.837–0.947). The prediction model of ovarian metastasis of EC patients based on machine learning algorithm can achieve satisfactory prediction efficiency, among which ANNM is the best, which can be used to guide clinicians in diagnosis and treatment and improve the prognosis of EC patients.
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spelling pubmed-105787552023-10-17 Development and validation of prediction model for early warning of ovarian metastasis risk of endometrial carcinoma Zhao, Qin Li, Yinuo Wang, Tiejun Medicine (Baltimore) 5600 Ovarian metastasis of endometrial carcinoma (EC) patients not only affects the decision of the surgeon, but also has a fatal impact on the fertility and prognosis of patients. This study aimed build a prediction model of ovarian metastasis of EC based on machine learning algorithm for clinical diagnosis and treatment management guidance. We retrospectively collected 536 EC patients treated in Hubei Cancer Hospital from January 2017 to October 2022 and 487 EC patients from Tongji Hospital (January 2017 to December 2020) as an external validation queue. The random forest model, gradient elevator model, support vector machine model, artificial neural network model (ANNM), and decision tree model were used to build ovarian metastasis prediction model for EC patients. The predictive efficacy of 5 machine learning models was evaluated by receiver operating characteristic curve and decision curve analysis. For screening of candidate predictors of ovarian metastasis of EC, the degree of tumor differentiation, lymph node metastasis, CA125, HE4, Alb, LH can be used as a potential predictor of ovarian metastasis prediction model in EC patients. The effectiveness of the prediction model constructed by the 5 machine learning algorithms was between (area under curve [AUC]: 0.729, 95% confidence interval [CI]: 0.674–0.784) and (AUC: 0.899, 95% CI: 0.844–0.954) in the training set and internal verification set, respectively. Among them, the ANNM was equipped with the best prediction effectiveness (training set: AUC: 0.899, 95% CI: 0.844–0.954) and (internal verification set: AUC: 0.892, 95% CI: 0.837–0.947). The prediction model of ovarian metastasis of EC patients based on machine learning algorithm can achieve satisfactory prediction efficiency, among which ANNM is the best, which can be used to guide clinicians in diagnosis and treatment and improve the prognosis of EC patients. Lippincott Williams & Wilkins 2023-10-13 /pmc/articles/PMC10578755/ /pubmed/37832099 http://dx.doi.org/10.1097/MD.0000000000035439 Text en Copyright © 2023 the Author(s). Published by Wolters Kluwer Health, Inc. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License 4.0 (CCBY) (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle 5600
Zhao, Qin
Li, Yinuo
Wang, Tiejun
Development and validation of prediction model for early warning of ovarian metastasis risk of endometrial carcinoma
title Development and validation of prediction model for early warning of ovarian metastasis risk of endometrial carcinoma
title_full Development and validation of prediction model for early warning of ovarian metastasis risk of endometrial carcinoma
title_fullStr Development and validation of prediction model for early warning of ovarian metastasis risk of endometrial carcinoma
title_full_unstemmed Development and validation of prediction model for early warning of ovarian metastasis risk of endometrial carcinoma
title_short Development and validation of prediction model for early warning of ovarian metastasis risk of endometrial carcinoma
title_sort development and validation of prediction model for early warning of ovarian metastasis risk of endometrial carcinoma
topic 5600
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10578755/
https://www.ncbi.nlm.nih.gov/pubmed/37832099
http://dx.doi.org/10.1097/MD.0000000000035439
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