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Machine Learning Based on Diffusion Kurtosis Imaging Histogram Parameters for Glioma Grading
Glioma grading plays an important role in surgical resection. We investigated the ability of different feature reduction methods in support vector machine (SVM)-based diffusion kurtosis imaging (DKI) histogram parameters to distinguish glioma grades. A total of 161 glioma patients who underwent magn...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9105194/ https://www.ncbi.nlm.nih.gov/pubmed/35566437 http://dx.doi.org/10.3390/jcm11092310 |
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author | Jiang, Liang Zhou, Leilei Ai, Zhongping Xiao, Chaoyong Liu, Wen Geng, Wen Chen, Huiyou Xiong, Zhenyu Yin, Xindao Chen, Yu-Chen |
author_facet | Jiang, Liang Zhou, Leilei Ai, Zhongping Xiao, Chaoyong Liu, Wen Geng, Wen Chen, Huiyou Xiong, Zhenyu Yin, Xindao Chen, Yu-Chen |
author_sort | Jiang, Liang |
collection | PubMed |
description | Glioma grading plays an important role in surgical resection. We investigated the ability of different feature reduction methods in support vector machine (SVM)-based diffusion kurtosis imaging (DKI) histogram parameters to distinguish glioma grades. A total of 161 glioma patients who underwent magnetic resonance imaging (MRI) from January 2017 to January 2020 were included retrospectively. The patients were divided into low-grade (n = 61) and high-grade (n = 100) groups. Parametric DKI maps were derived, and 45 features from the DKI maps were extracted semi-automatically for analysis. Three feature selection methods [principal component analysis (PCA), recursive feature elimination (RFE) and least absolute shrinkage and selection operator (LASSO)] were used to establish the glioma grading model with an SVM classifier. To evaluate the performance of SVM models, the receiver operating characteristic (ROC) curves of SVM models for distinguishing glioma grades were compared with those of conventional statistical methods. The conventional ROC analysis showed that mean diffusivity (MD) variance, MD skewness and mean kurtosis (MK) C50 could effectively distinguish glioma grades, particularly MD variance. The highest classification distinguishing AUC was found using LASSO at 0.904 ± 0.069. In comparison, classification AUC by PCA was 0.866 ± 0.061, and 0.899 ± 0.079 by RFE. The SVM-PCA model with the lowest AUC among the SVM models was significantly better than the conventional ROC analysis (z = 1.947, p = 0.013). These findings demonstrate the superiority of DKI histogram parameters by LASSO analysis and SVM for distinguishing glioma grades. |
format | Online Article Text |
id | pubmed-9105194 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-91051942022-05-14 Machine Learning Based on Diffusion Kurtosis Imaging Histogram Parameters for Glioma Grading Jiang, Liang Zhou, Leilei Ai, Zhongping Xiao, Chaoyong Liu, Wen Geng, Wen Chen, Huiyou Xiong, Zhenyu Yin, Xindao Chen, Yu-Chen J Clin Med Article Glioma grading plays an important role in surgical resection. We investigated the ability of different feature reduction methods in support vector machine (SVM)-based diffusion kurtosis imaging (DKI) histogram parameters to distinguish glioma grades. A total of 161 glioma patients who underwent magnetic resonance imaging (MRI) from January 2017 to January 2020 were included retrospectively. The patients were divided into low-grade (n = 61) and high-grade (n = 100) groups. Parametric DKI maps were derived, and 45 features from the DKI maps were extracted semi-automatically for analysis. Three feature selection methods [principal component analysis (PCA), recursive feature elimination (RFE) and least absolute shrinkage and selection operator (LASSO)] were used to establish the glioma grading model with an SVM classifier. To evaluate the performance of SVM models, the receiver operating characteristic (ROC) curves of SVM models for distinguishing glioma grades were compared with those of conventional statistical methods. The conventional ROC analysis showed that mean diffusivity (MD) variance, MD skewness and mean kurtosis (MK) C50 could effectively distinguish glioma grades, particularly MD variance. The highest classification distinguishing AUC was found using LASSO at 0.904 ± 0.069. In comparison, classification AUC by PCA was 0.866 ± 0.061, and 0.899 ± 0.079 by RFE. The SVM-PCA model with the lowest AUC among the SVM models was significantly better than the conventional ROC analysis (z = 1.947, p = 0.013). These findings demonstrate the superiority of DKI histogram parameters by LASSO analysis and SVM for distinguishing glioma grades. MDPI 2022-04-21 /pmc/articles/PMC9105194/ /pubmed/35566437 http://dx.doi.org/10.3390/jcm11092310 Text en © 2022 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 Jiang, Liang Zhou, Leilei Ai, Zhongping Xiao, Chaoyong Liu, Wen Geng, Wen Chen, Huiyou Xiong, Zhenyu Yin, Xindao Chen, Yu-Chen Machine Learning Based on Diffusion Kurtosis Imaging Histogram Parameters for Glioma Grading |
title | Machine Learning Based on Diffusion Kurtosis Imaging Histogram Parameters for Glioma Grading |
title_full | Machine Learning Based on Diffusion Kurtosis Imaging Histogram Parameters for Glioma Grading |
title_fullStr | Machine Learning Based on Diffusion Kurtosis Imaging Histogram Parameters for Glioma Grading |
title_full_unstemmed | Machine Learning Based on Diffusion Kurtosis Imaging Histogram Parameters for Glioma Grading |
title_short | Machine Learning Based on Diffusion Kurtosis Imaging Histogram Parameters for Glioma Grading |
title_sort | machine learning based on diffusion kurtosis imaging histogram parameters for glioma grading |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9105194/ https://www.ncbi.nlm.nih.gov/pubmed/35566437 http://dx.doi.org/10.3390/jcm11092310 |
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