Histogram analysis of T2*-based pharmacokinetic imaging in cerebral glioma grading

2018 ◽  
Vol 155 ◽  
pp. 19-27 ◽  
Author(s):  
Hua-Shan Liu ◽  
Shih-Wei Chiang ◽  
Hsiao-Wen Chung ◽  
Ping-Huei Tsai ◽  
Fei-Ting Hsu ◽  
...  
Keyword(s):  
Histogram Analysis ◽  
Cerebral Glioma ◽  
Glioma Grading ◽  
Pharmacokinetic Imaging

Glioma grading using apparent diffusion coefficient map: application of histogram analysis based on automatic segmentation

2014 ◽  
Vol 27 (9) ◽  
pp. 1046-1052 ◽  
Author(s):  
Jeongwon Lee ◽  
Seung Hong Choi ◽  
Ji-Hoon Kim ◽  
Chul-Ho Sohn ◽  
Sooyeul Lee ◽  
...  
Keyword(s):  
Diffusion Coefficient ◽  
Apparent Diffusion Coefficient ◽  
Automatic Segmentation ◽  
Histogram Analysis ◽  
Glioma Grading ◽  
Apparent Diffusion

Combination of MRI and dynamic FET PET for initial glioma grading

2014 ◽  
Vol 53 (04) ◽  
pp. 155-161 ◽  
Author(s):  
P. Maeder ◽  
M. Nicod-Lalonde ◽  
B. Lhermitte ◽  
C. Pollo ◽  
J. Bloch ◽  
...  
Keyword(s):  
Multivariate Logistic Regression Analysis ◽  
Histogram Analysis ◽  
Negative Slope ◽  
High Grade ◽  
Time Activity ◽  
Time Activity Curve ◽  
Fet Pet ◽  
Glioma Grading ◽  
Activity Curve ◽  
Dynamic Fet Pet

Summary Aim: MRI and PET with 18F-fluoro-ethyl-tyro- sine (FET) have been increasingly used to evaluate patients with gliomas. Our purpose was to assess the additive value of MR spectroscopy (MRS), diffusion imaging and dynamic FET-PET for glioma grading. Patients, methods: 38 patients (42 ± 15 aged, F/M: 0.46) with untreated histologically proven brain gliomas were included. All underwent conventional MRI, MRS, diffusion sequences, and FET-PET within 3±4 weeks. Performances of tumour FET time-activity-curve, early-to-middle SUVmax ratio, choline / creatine ratio and ADC histogram distribution pattern for gliomas grading were assessed, as compared to histology. Combination of these parameters and respective odds were also evaluated. Results: Tumour time-activity- curve reached the best accuracy (67%) when taken alone to distinguish between low and high-grade gliomas, followed by ADC histogram analysis (65%). Combination of time-activity-curve and ADC histogram analysis improved the sensitivity from 67% to 86% and the specificity from 63-67% to 100% (p < 0.008). On multivariate logistic regression analysis, negative slope of the tumour FET time-activity-curve however remains the best predictor of high-grade glioma (odds 7.6, SE 6.8, p = 0.022). Conclusion: Combination of dynamic FET-PET and diffusion MRI reached good performance for gliomas grading. The use of FET-PET/MR may be highly relevant in the initial assessment of primary brain tumours.

scholarly journals Glioma Grading by Using Histogram Analysis of Blood Volume Heterogeneity from MR-derived Cerebral Blood Volume Maps

Radiology ◽  
2008 ◽  
Vol 247 (3) ◽  
pp. 808-817 ◽  
Author(s):  
Kyrre E. Emblem ◽  
Baard Nedregaard ◽  
Terje Nome ◽  
Paulina Due-Tonnessen ◽  
John K. Hald ◽  
...  
Keyword(s):  
Blood Volume ◽  
Cerebral Blood Volume ◽  
Histogram Analysis ◽  
Glioma Grading

Resting state fMRI feature-based cerebral glioma grading by support vector machine

2014 ◽  
Vol 10 (7) ◽  
pp. 1167-1174 ◽  
Author(s):  
Jiangfen Wu ◽  
Zhiyu Qian ◽  
Ling Tao ◽  
Jianhua Yin ◽  
Shangwen Ding ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Resting State ◽  
Resting State Fmri ◽  
Support Vector ◽  
Cerebral Glioma ◽  
Glioma Grading ◽  
Feature Based

scholarly journals Histogram Analysis of MR Imaging–Derived Cerebral Blood Volume Maps: Combined Glioma Grading and Identification of Low-Grade Oligodendroglial Subtypes

2008 ◽  
Vol 29 (9) ◽  
pp. 1664-1670 ◽  
Author(s):  
K.E. Emblem ◽  
D. Scheie ◽  
P. Due-Tonnessen ◽  
B. Nedregaard ◽  
T. Nome ◽  
...  
Keyword(s):  
Mr Imaging ◽  
Blood Volume ◽  
Cerebral Blood Volume ◽  
Histogram Analysis ◽  
Low Grade ◽  
Glioma Grading

scholarly journals Two Diffusion Kurtosis Imaging Post-processing Methods for Differentiating Glioma Grades, IDH Mutation Statuses, and Heterogeneity

2020 ◽  
Author(s):  
Jie Bai ◽  
Ankang Gao ◽  
Yuan Hong ◽  
GuoHua Zhao ◽  
Yong Zhang ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Wilcoxon Test ◽  
Histogram Analysis ◽  
Diffusion Kurtosis Imaging ◽  
Low Grade ◽  
Idh Mutation ◽  
Post Processing ◽  
Processing Methods ◽  
Glioma Grading ◽  
Idh Mutations

Abstract Background: To compare the application of two DKI post-processing methods that DKE software and DKI histogram analysis in glioma grading, IDH mutation typing, and evaluation of tumor heterogeneity.Methods: Patients who underwent surgery and were pathologically diagnosed with glioma after MR DKI scan. DKE software was used to calculate diffusion parameters, including fractional anisotropy, mean kurtosis (MK), radial kurtosis, and axial kurtosis. Histogram parameters were calculated, including minimum, maximum, mean, standard deviation, percentile values (25th, 50th, 75th, 95th), kurtosis, and skewness of Kapp and Dapp. The ROIs of the two post-processing methods were consistently and manually selected in continuous solid tumor regions. According to the result of Kolmogorov-Smirnov (K-S) test, Independent-samples T test or Mann - Whitney - Wilcoxon test was used to distinguish glioma grads. The parameters with the best percentile were identified by analysis of the area under the curve (AUC) of the receiver operating characteristic (ROC) analysis.Results: Seventy-three patients with glioma were observed, including 21 with low-grade gliomas (WHO II) and 52 with high-grade gliomas (WHO III, n=13; WHO IV, n=39), 38 of whom had IDH mutation status. There were significant differences between the high- and low-grade glioma groups regarding the maximum, mean, standard deviation, C75, and C95 of the Kapp values and the minimum, mean, C25, C50, C75, C95, and skewness of the Dapp values. The MK values were significantly different among the WHO II, III, and IV grades. MK, mean Kapp, and C75 and C95 of the Kapp could be used to predict IDH mutations in patients with glioma. Conclusions: Several quantitative DKI parameters obtained from the DKE software and histogram analysis could be used for glioma grading and predicting IDH mutations. However, DKI histogram analysis was useful for glioma heterogeneity.

In Vivo Single Voxel 1H MR Spectroscopy in Cerebral Glioma

1996 ◽  
Vol 35 (3) ◽  
pp. 307 ◽  
Author(s):  
In Chan Song ◽  
Kee Hyun Chang ◽  
Moon Hee Han ◽  
Hee Won Jung ◽  
Dong Sung Kim ◽  
...  
Keyword(s):  
Mr Spectroscopy ◽  
Cerebral Glioma ◽  
1H Mr Spectroscopy ◽  
Single Voxel
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