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Journal Article
Glioma Grading Using Apparent Diffusion Coefficient Map: Application of Histogram Analysis Based on Automatic Segmentation
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- Authors
- Jeongwon Lee, Seung Hong Choi, Ji-Hoon Kim, Chul-Ho Sohn, Sooyeul Lee, Jaeseung Jeong
- Issue Date
- 2014-09
- Citation
- NMR in Biomedicine, v.27, no.9, pp.1046-1052
- ISSN
- 0952-3480
- Publisher
- John Wiley & Sons
- Language
- English
- Type
- Journal Article
- Abstract
- The accurate diagnosis of glioma subtypes is critical for appropriate treatment, but conventional histopathologic diagnosis often exhibits significant intra-observer variability and sampling error. The aim of this study was to investigate whether histogram analysis using an automatically segmented region of interest (ROI), excluding cystic or necrotic portions, could improve the differentiation between low-grade and high-grade gliomas. Thirty-two patients (nine low-grade and 23 high-grade gliomas) were included in this retrospective investigation. The outer boundaries of the entire tumors were manually drawn in each section of the contrast-enhanced T1-weighted MR images. We excluded cystic or necrotic portions from the entire tumor volume. The histogram analyses were performed within the ROI on normalized apparent diffusion coefficient (ADC) maps. To evaluate the contribution of the proposed method to glioma grading, we compared the area under the receiver operating characteristic (ROC) curves. We found that an ROI excluding cystic or necrotic portions was more useful for glioma grading than was an entire tumor ROI. In the case of the fifth percentile values of the normalized ADC histogram, the area under the ROC curve for the tumor ROIs excluding cystic or necrotic portions was significantly higher than that for the entire tumor ROIs (p<0.005). The automatic segmentation of a cystic or necrotic area probably improves the ability to differentiate between high- and low-grade gliomas on an ADC map. © 2014 John Wiley & Sons, Ltd.
- KSP Keywords
- Apparent diffusion coefficient, Automatic Segmentation, Diffusion coefficient(D0), Histogram Analysis, Low-grade, MR Images, ROC Curve, Receiver Operating Characteristic (ROC) curves, Region of interest(ROI), Sampling error, T1-weighted