The value of diffusion kurtosis imaging histogram combine with EphA2 grading in glioma grading
Objective To investigate the value of diffusion kurtosis imaging (DKI) histogram combined with Ephrin type⁃A receptor 2 (EphA2) in the evaluation of glioma grading. Methods A total of 183 patients with diffuse glioma [including 63 cases of low⁃grade glioma (LGG) and 120 cases of high⁃grade glioma (H...
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| Format: | Article |
| Language: | English |
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Tianjin Huanhu Hospital
2023-03-01
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| Series: | Chinese Journal of Contemporary Neurology and Neurosurgery |
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| Online Access: | http://www.cjcnn.org/index.php/cjcnn/article/view/2654 |
| _version_ | 1797848984970067968 |
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| author | LI Jian⁃rui LIU Xiao⁃xue XU Qiang LUO Zhong⁃qiang LU Guang⁃ming ZHANG Zhi⁃qiang |
| author_facet | LI Jian⁃rui LIU Xiao⁃xue XU Qiang LUO Zhong⁃qiang LU Guang⁃ming ZHANG Zhi⁃qiang |
| author_sort | LI Jian⁃rui |
| collection | DOAJ |
| description | Objective To investigate the value of diffusion kurtosis imaging (DKI) histogram combined with Ephrin type⁃A receptor 2 (EphA2) in the evaluation of glioma grading. Methods A total of 183 patients with diffuse glioma [including 63 cases of low⁃grade glioma (LGG) and 120 cases of high⁃grade glioma (HGG)] who underwent neurosurgical resection and were confirmed by pathology at General Hospital of Eastern Theater Command from December 2015 to December 2019 were enrolled. All patients underwent conventional MRI and DKI examination [including fractional anisotropy (FA), mean diffusivity (MD), kurtosis fractional anisotropy (KFA), mean kurtosis (MK), mean kurtosis tensor (MKT)], and DKI histogram parameters (including mean, variance, median, 25% quantile, 75% quantile, skewness, kurtosis) were obtained. Immunohistochemical staining of EphA2 was performed. Univariate and multivariate Logistic regression analysis were used to screen the predictive factors of glioma grading, and based on these factors, the DKI histogram and the DKI histogram combined with EphA2 grading diagnostic prediction model were constructed, and the receiver operating characteristic curve (ROC) was drawn to evaluate its diagnostic efficiency. Spearman rank correlation analysis was used to explore the correlation between the DKI histogram parameters and the EphA2 grading. Results For HGG, the variance (t=⁃2.050, P=0.042) and 75% quantile (t=⁃2.130, P=0.035) of FA value, the variance (t=⁃6.052, P=0.000) and skewness (Z=⁃3.326, P=0.001) of MD value, the mean (t=⁃3.094, P=0.002), variance (t=⁃2.228, P=0.027), median (Z=⁃3.444, P=0.001), 25% quantile (t=⁃3.022, P=0.003) and 75% quantile (t=⁃3.438, P=0.001) of MK value, the mean (t=⁃3.096, P=0.002), variance (t=⁃2.140, P=0.028), median (t=⁃3.701, P=0.000), 25% quantile (t=⁃3.033, P=0.003) and 75% quantile (t=⁃3.441, P=0.000) of MKT value were higher than those of LGG. The FA value (Z=4.489, P=0.000), MK value (Z=4.528, P=0.000) and MKT value (Z=4.528, P=0.000) were significantly lower than those of LGG. Logistic regression analysis showed the skewness of FA value (OR=0.484, 95%CI: 0.278-0.842; P=0.010), variance of MD value (OR=2.821, 95%CI: 1.231-6.466; P=0.014) and 75% quantile of MKT value (OR=2.581, 95%CI: 1.148-5.806; P=0.022) were the predictive factors for glioma grading. The ROC curve showed the area under the curve (AUC) of DKI histogram parameters combined with EphA2 grading was 0.90±0.02 (95%CI: 0.676-0.922, P=0.000), which was better than DKI histogram (0.86±0.02; 95%CI: 0.809-0.916, P=0.000; Z=1.114, P=0.041). Spearman rank correlation analysis showed only MD kurtosis was negatively correlated with EphA2 grading (rs=⁃0.267, P=0.002). Conclusions The prediction model of DKI histogram combined with EphA2 grading can effectively improve the efficiency of grading diagnosis of glioma. |
| first_indexed | 2024-04-09T18:37:36Z |
| format | Article |
| id | doaj.art-8eeee627ad4e4a8e881a08c0670752f1 |
| institution | Directory Open Access Journal |
| issn | 1672-6731 |
| language | English |
| last_indexed | 2024-04-09T18:37:36Z |
| publishDate | 2023-03-01 |
| publisher | Tianjin Huanhu Hospital |
| record_format | Article |
| series | Chinese Journal of Contemporary Neurology and Neurosurgery |
| spelling | doaj.art-8eeee627ad4e4a8e881a08c0670752f12023-04-11T08:37:43ZengTianjin Huanhu HospitalChinese Journal of Contemporary Neurology and Neurosurgery1672-67312023-03-01230325426310.3969/j.issn.1672⁃6731.2023.03.016The value of diffusion kurtosis imaging histogram combine with EphA2 grading in glioma gradingLI Jian⁃rui0LIU Xiao⁃xue1 XU Qiang2 LUO Zhong⁃qiang3LU Guang⁃ming4 ZHANG Zhi⁃qiang5Department of Diagnostic Radiology, Jinling Hospital, Nanjing University School of Medicine, General Hospital of Eastern Theater CommandDepartment of Diagnostic Radiology, Jinling Hospital, Nanjing University School of Medicine, General Hospital of Eastern Theater CommandDepartment of Diagnostic Radiology, Jinling Hospital, Nanjing University School of Medicine, General Hospital of Eastern Theater CommandDepartment of Diagnostic Radiology, Jinling Hospital, Nanjing University School of Medicine, General Hospital of Eastern Theater CommandDepartment of Diagnostic Radiology, Jinling Hospital, Nanjing University School of Medicine, General Hospital of Eastern Theater CommandDepartment of Diagnostic Radiology, Jinling Hospital, Nanjing University School of Medicine, General Hospital of Eastern Theater CommandObjective To investigate the value of diffusion kurtosis imaging (DKI) histogram combined with Ephrin type⁃A receptor 2 (EphA2) in the evaluation of glioma grading. Methods A total of 183 patients with diffuse glioma [including 63 cases of low⁃grade glioma (LGG) and 120 cases of high⁃grade glioma (HGG)] who underwent neurosurgical resection and were confirmed by pathology at General Hospital of Eastern Theater Command from December 2015 to December 2019 were enrolled. All patients underwent conventional MRI and DKI examination [including fractional anisotropy (FA), mean diffusivity (MD), kurtosis fractional anisotropy (KFA), mean kurtosis (MK), mean kurtosis tensor (MKT)], and DKI histogram parameters (including mean, variance, median, 25% quantile, 75% quantile, skewness, kurtosis) were obtained. Immunohistochemical staining of EphA2 was performed. Univariate and multivariate Logistic regression analysis were used to screen the predictive factors of glioma grading, and based on these factors, the DKI histogram and the DKI histogram combined with EphA2 grading diagnostic prediction model were constructed, and the receiver operating characteristic curve (ROC) was drawn to evaluate its diagnostic efficiency. Spearman rank correlation analysis was used to explore the correlation between the DKI histogram parameters and the EphA2 grading. Results For HGG, the variance (t=⁃2.050, P=0.042) and 75% quantile (t=⁃2.130, P=0.035) of FA value, the variance (t=⁃6.052, P=0.000) and skewness (Z=⁃3.326, P=0.001) of MD value, the mean (t=⁃3.094, P=0.002), variance (t=⁃2.228, P=0.027), median (Z=⁃3.444, P=0.001), 25% quantile (t=⁃3.022, P=0.003) and 75% quantile (t=⁃3.438, P=0.001) of MK value, the mean (t=⁃3.096, P=0.002), variance (t=⁃2.140, P=0.028), median (t=⁃3.701, P=0.000), 25% quantile (t=⁃3.033, P=0.003) and 75% quantile (t=⁃3.441, P=0.000) of MKT value were higher than those of LGG. The FA value (Z=4.489, P=0.000), MK value (Z=4.528, P=0.000) and MKT value (Z=4.528, P=0.000) were significantly lower than those of LGG. Logistic regression analysis showed the skewness of FA value (OR=0.484, 95%CI: 0.278-0.842; P=0.010), variance of MD value (OR=2.821, 95%CI: 1.231-6.466; P=0.014) and 75% quantile of MKT value (OR=2.581, 95%CI: 1.148-5.806; P=0.022) were the predictive factors for glioma grading. The ROC curve showed the area under the curve (AUC) of DKI histogram parameters combined with EphA2 grading was 0.90±0.02 (95%CI: 0.676-0.922, P=0.000), which was better than DKI histogram (0.86±0.02; 95%CI: 0.809-0.916, P=0.000; Z=1.114, P=0.041). Spearman rank correlation analysis showed only MD kurtosis was negatively correlated with EphA2 grading (rs=⁃0.267, P=0.002). Conclusions The prediction model of DKI histogram combined with EphA2 grading can effectively improve the efficiency of grading diagnosis of glioma.http://www.cjcnn.org/index.php/cjcnn/article/view/2654gliomamembrane proteinsdiffusion magnetic resonance imagingforecastinglogistic models |
| spellingShingle | LI Jian⁃rui LIU Xiao⁃xue XU Qiang LUO Zhong⁃qiang LU Guang⁃ming ZHANG Zhi⁃qiang The value of diffusion kurtosis imaging histogram combine with EphA2 grading in glioma grading Chinese Journal of Contemporary Neurology and Neurosurgery glioma membrane proteins diffusion magnetic resonance imaging forecasting logistic models |
| title | The value of diffusion kurtosis imaging histogram combine with EphA2 grading in glioma grading |
| title_full | The value of diffusion kurtosis imaging histogram combine with EphA2 grading in glioma grading |
| title_fullStr | The value of diffusion kurtosis imaging histogram combine with EphA2 grading in glioma grading |
| title_full_unstemmed | The value of diffusion kurtosis imaging histogram combine with EphA2 grading in glioma grading |
| title_short | The value of diffusion kurtosis imaging histogram combine with EphA2 grading in glioma grading |
| title_sort | value of diffusion kurtosis imaging histogram combine with epha2 grading in glioma grading |
| topic | glioma membrane proteins diffusion magnetic resonance imaging forecasting logistic models |
| url | http://www.cjcnn.org/index.php/cjcnn/article/view/2654 |
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