Cargando…

The Performance of Different Machine Learning Algorithm and Regression Models in Predicting High-Grade Intracranial Meningioma

Meningioma is the most common primary tumor of the central nervous system (CNS). Individualized treatment strategies should be formulated for the patients according to the WHO (World Health Organization) grade. Our aim was to investigate the effectiveness of various machine learning and traditional...

Descripción completa

Detalles Bibliográficos
Autores principales: Teng, Haibo, Yang, Xiang, Liu, Zhiyong, Liu, Hao, Yan, Ouying, Jie, Danyang, Li, Xueying, Xu, Jianguo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10136416/
https://www.ncbi.nlm.nih.gov/pubmed/37190559
http://dx.doi.org/10.3390/brainsci13040594
_version_ 1785032212658257920
author Teng, Haibo
Yang, Xiang
Liu, Zhiyong
Liu, Hao
Yan, Ouying
Jie, Danyang
Li, Xueying
Xu, Jianguo
author_facet Teng, Haibo
Yang, Xiang
Liu, Zhiyong
Liu, Hao
Yan, Ouying
Jie, Danyang
Li, Xueying
Xu, Jianguo
author_sort Teng, Haibo
collection PubMed
description Meningioma is the most common primary tumor of the central nervous system (CNS). Individualized treatment strategies should be formulated for the patients according to the WHO (World Health Organization) grade. Our aim was to investigate the effectiveness of various machine learning and traditional statistical models in predicting the WHO grade of preoperative patients with meningioma. Patients diagnosed with meningioma after surgery in West China Hospital and Shangjin Hospital of Sichuan University from 2009 to 2016 were included in the study cohort. As the training cohort (n = 1975), independent risk factors associated with high-grade meningioma were used to establish the Nomogram model. which was validated in a subsequent cohort (n = 1048) from 2017 to 2019 in our hospital. Logistic regression (LR), XGboost, Adaboost, Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Random Forest (RF) models were determined using F1 score, recall, accuracy, the area under the curve (ROC), calibration plot and decision curve analysis (DCA) were used to evaluate the different models. Logistic regression showed better predictive performance and interpretability than machine learning. Gender, recurrence history, T1 signal intensity, enhanced signal degree, peritumoral edema, tumor diameter, cystic, location, and NLR index were identified as independent risk factors and added to the nomogram. The AUC (Area Under Curve) value of RF was 0.812 in the training set, 0.807 in the internal validation set, and 0.842 in the external validation set. The calibration curve and DCA (Decision Curve Analysis) indicated that it had better prediction efficiency of LR than others. The Nomogram preoperative prediction model of meningioma of WHO II and III grades showed effective prediction ability. While machine learning exhibits strong fitting ability, it performs poorly in the validation set.
format Online
Article
Text
id pubmed-10136416
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-101364162023-04-28 The Performance of Different Machine Learning Algorithm and Regression Models in Predicting High-Grade Intracranial Meningioma Teng, Haibo Yang, Xiang Liu, Zhiyong Liu, Hao Yan, Ouying Jie, Danyang Li, Xueying Xu, Jianguo Brain Sci Article Meningioma is the most common primary tumor of the central nervous system (CNS). Individualized treatment strategies should be formulated for the patients according to the WHO (World Health Organization) grade. Our aim was to investigate the effectiveness of various machine learning and traditional statistical models in predicting the WHO grade of preoperative patients with meningioma. Patients diagnosed with meningioma after surgery in West China Hospital and Shangjin Hospital of Sichuan University from 2009 to 2016 were included in the study cohort. As the training cohort (n = 1975), independent risk factors associated with high-grade meningioma were used to establish the Nomogram model. which was validated in a subsequent cohort (n = 1048) from 2017 to 2019 in our hospital. Logistic regression (LR), XGboost, Adaboost, Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Random Forest (RF) models were determined using F1 score, recall, accuracy, the area under the curve (ROC), calibration plot and decision curve analysis (DCA) were used to evaluate the different models. Logistic regression showed better predictive performance and interpretability than machine learning. Gender, recurrence history, T1 signal intensity, enhanced signal degree, peritumoral edema, tumor diameter, cystic, location, and NLR index were identified as independent risk factors and added to the nomogram. The AUC (Area Under Curve) value of RF was 0.812 in the training set, 0.807 in the internal validation set, and 0.842 in the external validation set. The calibration curve and DCA (Decision Curve Analysis) indicated that it had better prediction efficiency of LR than others. The Nomogram preoperative prediction model of meningioma of WHO II and III grades showed effective prediction ability. While machine learning exhibits strong fitting ability, it performs poorly in the validation set. MDPI 2023-03-31 /pmc/articles/PMC10136416/ /pubmed/37190559 http://dx.doi.org/10.3390/brainsci13040594 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Teng, Haibo
Yang, Xiang
Liu, Zhiyong
Liu, Hao
Yan, Ouying
Jie, Danyang
Li, Xueying
Xu, Jianguo
The Performance of Different Machine Learning Algorithm and Regression Models in Predicting High-Grade Intracranial Meningioma
title The Performance of Different Machine Learning Algorithm and Regression Models in Predicting High-Grade Intracranial Meningioma
title_full The Performance of Different Machine Learning Algorithm and Regression Models in Predicting High-Grade Intracranial Meningioma
title_fullStr The Performance of Different Machine Learning Algorithm and Regression Models in Predicting High-Grade Intracranial Meningioma
title_full_unstemmed The Performance of Different Machine Learning Algorithm and Regression Models in Predicting High-Grade Intracranial Meningioma
title_short The Performance of Different Machine Learning Algorithm and Regression Models in Predicting High-Grade Intracranial Meningioma
title_sort performance of different machine learning algorithm and regression models in predicting high-grade intracranial meningioma
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10136416/
https://www.ncbi.nlm.nih.gov/pubmed/37190559
http://dx.doi.org/10.3390/brainsci13040594
work_keys_str_mv AT tenghaibo theperformanceofdifferentmachinelearningalgorithmandregressionmodelsinpredictinghighgradeintracranialmeningioma
AT yangxiang theperformanceofdifferentmachinelearningalgorithmandregressionmodelsinpredictinghighgradeintracranialmeningioma
AT liuzhiyong theperformanceofdifferentmachinelearningalgorithmandregressionmodelsinpredictinghighgradeintracranialmeningioma
AT liuhao theperformanceofdifferentmachinelearningalgorithmandregressionmodelsinpredictinghighgradeintracranialmeningioma
AT yanouying theperformanceofdifferentmachinelearningalgorithmandregressionmodelsinpredictinghighgradeintracranialmeningioma
AT jiedanyang theperformanceofdifferentmachinelearningalgorithmandregressionmodelsinpredictinghighgradeintracranialmeningioma
AT lixueying theperformanceofdifferentmachinelearningalgorithmandregressionmodelsinpredictinghighgradeintracranialmeningioma
AT xujianguo theperformanceofdifferentmachinelearningalgorithmandregressionmodelsinpredictinghighgradeintracranialmeningioma
AT tenghaibo performanceofdifferentmachinelearningalgorithmandregressionmodelsinpredictinghighgradeintracranialmeningioma
AT yangxiang performanceofdifferentmachinelearningalgorithmandregressionmodelsinpredictinghighgradeintracranialmeningioma
AT liuzhiyong performanceofdifferentmachinelearningalgorithmandregressionmodelsinpredictinghighgradeintracranialmeningioma
AT liuhao performanceofdifferentmachinelearningalgorithmandregressionmodelsinpredictinghighgradeintracranialmeningioma
AT yanouying performanceofdifferentmachinelearningalgorithmandregressionmodelsinpredictinghighgradeintracranialmeningioma
AT jiedanyang performanceofdifferentmachinelearningalgorithmandregressionmodelsinpredictinghighgradeintracranialmeningioma
AT lixueying performanceofdifferentmachinelearningalgorithmandregressionmodelsinpredictinghighgradeintracranialmeningioma
AT xujianguo performanceofdifferentmachinelearningalgorithmandregressionmodelsinpredictinghighgradeintracranialmeningioma