Histogram analysis of diffusion kurtosis imaging derived maps may distinguish between low and high grade gliomas before surgery

2017 ◽  
Vol 28 (4) ◽  
pp. 1748-1755 ◽  
Author(s):  
Xi-Xun Qi ◽  
Da-Fa Shi ◽  
Si-Xie Ren ◽  
Su-Ya Zhang ◽  
Long Li ◽  
...  
Keyword(s):  
Histogram Analysis ◽  
Diffusion Kurtosis Imaging ◽  
High Grade ◽  
High Grade Gliomas ◽  
Diffusion Kurtosis

scholarly journals RADI-18. DIFFUSION KURTOSIS IMAGING CAN HELP DIFFERENTIATE LOW- AND HIGH-GRADE GLIOMAS IN PEDIATRIC PATIENTS: A PROSPECTIVE SINGLE CENTRE STUDY

2018 ◽  
Vol 20 (suppl_2) ◽  
pp. i173-i173
Author(s):  
Ioan Paul Voicu ◽  
Antonio Napolitano ◽  
Chiara Carducci ◽  
Lorenzo Lattavo ◽  
Maria Camilla Rossi Espagnet ◽  
...  
Keyword(s):  
Pediatric Patients ◽  
Diffusion Kurtosis Imaging ◽  
High Grade ◽  
Single Centre ◽  
Centre Study ◽  
Single Centre Study ◽  
High Grade Gliomas ◽  
Diffusion Kurtosis

Histogram analysis in predicting the grade and histological subtype of meningiomas based on diffusion kurtosis imaging

2020 ◽  
Vol 61 (9) ◽  
pp. 1228-1239
Author(s):  
Xiaodan Chen ◽  
Lin Lin ◽  
Jie Wu ◽  
Guang Yang ◽  
Tianjin Zhong ◽  
...  
Keyword(s):  
Pearson Correlation ◽  
Mean Diffusivity ◽  
Labeling Index ◽  
Histogram Analysis ◽  
Diffusion Kurtosis Imaging ◽  
Low Grade ◽  
High Grade ◽  
Ki 67 ◽  
Diffusion Kurtosis ◽  
Median Maximum

Background Presurgical grading is particularly important for selecting the best therapeutic strategy for meningioma patients. Purpose To investigate the value of histogram analysis of diffusion kurtosis imaging (DKI) maps in the differentiation of grades and histological subtypes of meningiomas. Material and Methods A total of 172 patients with histopathologically proven meningiomas underwent preoperative magnetic resonance imaging (MRI) and were classified into low-grade and high-grade groups. Mean kurtosis (MK), fractional anisotropy (FA), and mean diffusivity (MD) histograms were generated based on solid components of the whole tumor. The following parameters of each histogram were obtained: 10th, 25th, 75th, and 90th percentiles, mean, median, maximum, minimum, and kurtosis, skewness, and variance. Comparisons of different grades and subtypes were made by Mann–Whitney U test, Kruskal–Wallis test, ROC curves analysis, and multiple logistic regression. Pearson correlation was used to evaluate correlations between histogram parameters and the Ki-67 labeling index. Results Significantly higher maximum, skewness, and variance of MD, mean, median, maximum, variance, 10th, 25th, 75th, and 90th percentiles of MK were found in high-grade than low-grade meningiomas (all P < 0.05). DKI histogram parameters differentiated 7/10 pairs of subtype pairs. The 90th percentile of MK yielded the highest AUC of 0.870 and was the only independent indicator for grading meningiomas. Various DKI histogram parameters were correlated with the Ki-67 labeling index ( P < 0.05). Conclusion The histogram analysis of DKI is useful for differentiating meningioma grades and subtypes. The 90th percentile of MK may serve as an optimal parameter for predicting the grade of meningiomas.

Diffusion kurtosis imaging as an imaging biomarker for predicting prognosis of the patients with high-grade gliomas

2019 ◽  
Vol 63 ◽  
pp. 131-136 ◽  
Author(s):  
Xiao Wang ◽  
Wenjing Gao ◽  
Fuyan Li ◽  
Wenqi Shi ◽  
Hongxia Li ◽  
...  
Keyword(s):  
Imaging Biomarker ◽  
Diffusion Kurtosis Imaging ◽  
High Grade ◽  
High Grade Gliomas ◽  
Diffusion Kurtosis

scholarly journals Feasibility of generalised DKI approach for brain glioma grading

2020 ◽  
Author(s):  
E Pogosbekian ◽  
I N Pronin ◽  
N Zakharova ◽  
A Batalov ◽  
A Turkin ◽  
...  
Keyword(s):  
Diffusion Tensor ◽  
Diffusion Kurtosis Imaging ◽  
High Grade ◽  
Brain Glioma ◽  
Tumour Heterogeneity ◽  
Clinical Issue ◽  
Invasive Approach ◽  
Glioma Grading ◽  
High Grade Gliomas ◽  
Glioma Differentiation

Purpose: An accurate differentiation of brain glioma grade constitutes an important clinical issue. Powerful non-invasive approach based on diffusion MRI has already demonstrated its feasibility in glioma grade stratification. However, the conventional diffusion tensor (DTI) and kurtosis imaging (DKI) demonstrated moderate sensitivity and performance in glioma grading. In the present work, we apply generalised DKI (gDKI) approach in order to assess its diagnostic accuracy and potential application in glioma grading. Methods: Diffusion scalar metrics were obtained from 50 patients with different glioma grades confirmed by histological tests following biopsy or surgery. All patients were divided into two groups with low- and high-grade gliomas as II grade versus III and IV grades, respectively. For a comparison, trained radiologists segmented the brain tissue into three regions with solid tumour, oedema, and normal appearing white matter. For each region we estimated the conventional and gDKI metrics including DTI maps. Results: We found high correlations between DKI and gDKI metrics in high-grade glioma. Further, gDKI metrics enabled introduction of a complementary measure for glioma differentiation based on correlations between the conventional and generalised approaches. Both conventional and generalised DKI metrics showed quantitative maps of tumour heterogeneity and oedema behaviour. gDKI approach demonstrated largely similar sensitivity and specificity in low-high glioma differentiation as in the case of conventional DKI method. Conclusion: The generalised diffusion kurtosis imaging enables differentiation of low and high grade gliomas at the same level as the conventional DKI. Additionally, gDKI exhibited higher tissue contrast between tumour and healthy tissue and, thus, may contribute as a complementary source of information on tumour heterogeneity.

Histogram analysis in prostate cancer: a comparison of diffusion kurtosis imaging model versus monoexponential model

2020 ◽  
Vol 61 (10) ◽  
pp. 1431-1440
Author(s):  
Yuwei Jiang ◽  
Chunmei Li ◽  
Ying Liu ◽  
Kaining Shi ◽  
Wei Zhang ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Histogram Analysis ◽  
Diffusion Kurtosis Imaging ◽  
Single Shot ◽  
Diagnostic Efficiency ◽  
Planar Imaging ◽  
Significant Difference ◽  
Monoexponential Model ◽  
The Mean ◽  
Diffusion Kurtosis

Background There is still little research about histogram analysis of diffusion kurtosis imaging (DKI) using in prostate cancer at present. Purpose To verify the utility of histogram analysis of DKI model in detection and assessment of aggressiveness of prostate cancer, compared with monoexponential model (MEM). Material and Methods Twenty-three patients were enrolled in this study. For DKI model and MEM, the Dapp, Kapp, and apparent diffusion coefficient (ADC) were obtained by using single-shot echo-planar imaging sequence. The pathologies were confirmed by in-bore magnetic resonance (MR)-guided biopsy. Regions of interest (ROI) were drawn manually in the position where biopsy needle was put. The mean values and histogram parameters in cancer and noncancerous foci were compared using independent-samples T test. Receiver operating characteristic curves were used to investigate the diagnostic efficiency. Spearman’s test was used to evaluate the correlation of parameters and Gleason scores. Results The mean, 10th, 25th, 50th, 75th, and 90th percentiles of ADC and Dapp were significantly lower in prostate cancer than non-cancerous foci ( P < 0.001). The mean, 50th, 75th, and 90th percentiles of Kapp were significantly higher in prostate cancer ( P < 0.05). There was no significant difference between the AUCs of two models (0.971 vs. 0.963, P > 0.05). With the increasing Gleason scores, the 10th ADC decreased ( ρ = −0.583, P = 0.018), but the 90th Kapp increased ( ρ = 0.642, P = 0.007). Conclusion Histogram analysis of DKI model is feasible for diagnosing and grading prostate cancer, but it has no significant advantage over MEM.

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