Histogram analysis based on diffusion kurtosis imaging: Differentiating glioblastoma multiform from single brain metastasis and comparing the diagnostic performance of two regions of interest placements

2021 ◽  
pp. 110104
Author(s):  
Eryuan Gao ◽  
Ankang Gao ◽  
Wing Kit Kung ◽  
Lin Shi ◽  
Jie Bai ◽  
...  
Keyword(s):  
Brain Metastasis ◽  
Diagnostic Performance ◽  
Regions Of Interest ◽  
Histogram Analysis ◽  
Diffusion Kurtosis Imaging ◽  
Glioblastoma Multiform ◽  
Single Brain Metastasis ◽  
Diffusion Kurtosis

scholarly journals Preoperatively Grading Rectal Cancer with the Combination of Intravoxel Incoherent Motions Imaging and Diffusion Kurtosis Imaging

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Zhijun Geng ◽  
Yunfei Zhang ◽  
Shaohan Yin ◽  
Shanshan Lian ◽  
Haoqiang He ◽  
...  
Keyword(s):  
Rectal Cancer ◽  
Diagnostic Performance ◽  
Pearson Correlation ◽  
Correlation Coefficients ◽  
Diffusion Kurtosis Imaging ◽  
Low Grade ◽  
Characteristic Analysis ◽  
Microstructural Heterogeneity ◽  
Diffusion Kurtosis ◽  
And Diffusion

Purpose. To combine Intravoxel Incoherent Motions (IVIM) imaging and diffusion kurtosis imaging (DKI) which can aid in the quantification of different biological inspirations including cellularity, vascularity, and microstructural heterogeneity to preoperatively grade rectal cancer. Methods. A total of 58 rectal patients were included into this prospective study. MRI was performed with a 3T scanner. Different combinations of IVIM-derived and DKI-derived parameters were performed to grade rectal cancer. Pearson correlation coefficients were applied to evaluate the correlations. Binary logistic regression models were established via integrating different DWI parameters for screening the most sensitive parameter. Receiver operating characteristic analysis was performed for evaluating the diagnostic performance. Results. For individual DWI-derived parameters, all parameters except the pseudodiffusion coefficient displayed the capability of grading rectal cancer ( p < 0.05 ). The better discrimination between high- and low-grade rectal cancer was achieved with the combination of different DWI-derived parameters. Similarly, ROC analysis suggested the combination of D (true diffusion coefficient), f (perfusion fraction), and Kapp (apparent kurtosis coefficient) yielded the best diagnostic performance (AUC = 0.953, p < 0.001 ). According to the result of binary logistic analysis, cellularity-related D was the most sensitive predictor (odds ratio: 9.350 ± 2.239) for grading rectal cancer. Conclusion. The combination of IVIM and DKI holds great potential in accurately grading rectal cancer as IVIM and DKI can provide the quantification of different biological inspirations including cellularity, vascularity, and microstructural 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.

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 Application of Diffusion Kurtosis Imaging and Histogram Analysis for Assessing Preoperative Stages of Rectal Cancer

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Hui Xie ◽  
Guangyao Wu
Keyword(s):  
Rectal Cancer ◽  
Lymph Node ◽  
Rectal Adenocarcinoma ◽  
Area Under The Curve ◽  
Histogram Analysis ◽  
Diffusion Kurtosis Imaging ◽  
Diagnostic Efficiency ◽  
Significant Difference ◽  
Adc Values ◽  
Diffusion Kurtosis

Objective. To explore the value of diffusion kurtosis imaging (DKI) and histogram analysis for assessing preoperative stages and heterogeneity in rectal cancer. Methods. Fifty patients with pathologically confirmed rectal adenocarcinoma were enrolled. The value of DKI parameters and histogram metrics for assessing the preoperative stages and heterogeneity in rectal cancer was analyzed retrospectively. Results. (1) ADC-10th percentile and ADC-25th percentile were significantly higher in T1-2 than in the T3-4 rectal cancer (the ADC values were 0.65 ± 0.08 × 10−3 mm2/s versus 0.58 ± 0.11 × 10−3 mm2/s and 0.73 ± 0.11 × 10−3 mm2/s versus 0.65 ± 0.11 × 10−3 mm2/s; p values were 0.035 and 0.024, resp.). (2) D-10th percentile and D-25th percentile were also significantly higher in T1-2 than in T3-4 rectal cancer (the D values were 0.96 ± 0.19 × 10−3 mm2/s versus 0.84 ± 0.16 × 10−3 mm2/s and 1.15 ± 0.27 × 10−3 mm2/s versus 0.99 ± 0.18 × 10−3 mm2/s; p values were 0.017 and 0.044, resp.). (3) K value and its histogram metrics showed no statistically significant difference between T1-2 and T3-4. (4) D-10th had the largest area under the curve (AUC 0.799) among all the parameters; the sensitivity and specificity were 84.2 and 61.3%, respectively. (5) DKI combined with traditional MRI had an accuracy of 68% while assessing the lymph node of rectal cancer. Conclusion. DKI parameters and histogram metrics are rather valuable in assessing the preoperative stages of rectal cancer; D-10th percentile exhibits the highest diagnostic efficiency.

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