| Title |
Histogram analysis of diffusion kurtosis imaging derived maps may distinguish between low and high grade gliomas before surgery
|
|---|---|
| Published in |
European Radiology, November 2017
|
| DOI | 10.1007/s00330-017-5108-1 |
| Pubmed ID | |
| Authors |
Xi-Xun Qi, Da-Fa Shi, Si-Xie Ren, Su-Ya Zhang, Long Li, Qing-Chang Li, Li-Ming Guan |
| Abstract |
To investigate the value of histogram analysis of diffusion kurtosis imaging (DKI) maps in the evaluation of glioma grading. A total of 39 glioma patients who underwent preoperative magnetic resonance imaging (MRI) were classified into low-grade (13 cases) and high-grade (26 cases) glioma groups. Parametric DKI maps were derived, and histogram metrics between low- and high-grade gliomas were analysed. The optimum diagnostic thresholds of the parameters, area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were achieved using a receiver operating characteristic (ROC). Significant differences were observed not only in 12 metrics of histogram DKI parameters (P<0.05), but also in mean diffusivity (MD) and mean kurtosis (MK) values, including age as a covariate (F=19.127, P<0.001 and F=20.894, P<0.001, respectively), between low- and high-grade gliomas. Mean MK was the best independent predictor of differentiating glioma grades (B=18.934, 22.237 adjusted for age, P<0.05). The partial correlation coefficient between fractional anisotropy (FA) and kurtosis fractional anisotropy (KFA) was 0.675 (P<0.001). The AUC of the mean MK, sensitivity, and specificity were 0.925, 88.5% and 84.6%, respectively. DKI parameters can effectively distinguish between low- and high-grade gliomas. Mean MK is the best independent predictor of differentiating glioma grades. • DKI is a new and important method. • DKI can provide additional information on microstructural architecture. • Histogram analysis of DKI may be more effective in glioma grading. |
X Demographics
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 37 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 37 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 6 | 16% |
| Other | 5 | 14% |
| Student > Doctoral Student | 4 | 11% |
| Student > Ph. D. Student | 4 | 11% |
| Student > Postgraduate | 4 | 11% |
| Other | 5 | 14% |
| Unknown | 9 | 24% |
| Readers by discipline | Count | As % |
|---|---|---|
| Neuroscience | 10 | 27% |
| Medicine and Dentistry | 8 | 22% |
| Engineering | 3 | 8% |
| Computer Science | 1 | 3% |
| Physics and Astronomy | 1 | 3% |
| Other | 4 | 11% |
| Unknown | 10 | 27% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 26 December 2017.
All research outputs
#20,456,235
of 23,012,811 outputs
Outputs from European Radiology
#3,349
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Outputs of similar age
#257,093
of 294,530 outputs
Outputs of similar age from European Radiology
#52
of 63 outputs
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