Automatic classification of cardioembolic and arteriosclerotic ischemic strokes from apparent diffusion coefficient datasets using texture analysis and deep learning

2017 ◽  
Author(s):  
Javier Villafruela ◽  
Sebastian Crites ◽  
Bastian Cheng ◽  
Christian Knaack ◽  
Götz Thomalla ◽  
...  
Keyword(s):  
Diffusion Coefficient ◽  
Deep Learning ◽  
Apparent Diffusion Coefficient ◽  
Texture Analysis ◽  
Automatic Classification ◽  
Apparent Diffusion

scholarly journals The histogram analysis of apparent diffusion coefficient in differential diagnosis of parotid tumor

2020 ◽  
Vol 49 (5) ◽  
pp. 20190420 ◽  
Author(s):  
Peiqian Chen ◽  
Bing Dong ◽  
Chunye Zhang ◽  
Xiaofeng Tao ◽  
Pingzhong Wang ◽  
...  
Keyword(s):  
Diffusion Coefficient ◽  
Apparent Diffusion Coefficient ◽  
Pleomorphic Adenoma ◽  
Area Under Curve ◽  
Malignant Tumors ◽  
Basal Cell Adenoma ◽  
Cell Adenoma ◽  
Apparent Diffusion ◽  
Subgroup Classification

Objectives: Use apparent diffusion coefficient (ADC) histogram to investigate whether the parameters of ADC histogram can distinguish between benign and malignant tumors and further differentiate the tumor subgroups. Methods and materials: This study retrospectively enrolls 161 patients with parotid gland tumors. Histogram parameters including mean, inhomogeneity, skewness, kurtosis and 10th, 25th, 50th, 75th, 90th percentiles are derived from ADC mono-exponential model. Mann–Whitney U test is used to compare the differences between benign and malignant groups. Kruskal–Wallis test with post-hoc Dunn–Bonferroni method is used for subgroup classification, then receiver operating characteristic curve analysis is performed in mean ADC value to obtain the appropriate cutoff values. Results: Except for kurtosis and 90th percentile, there are significant differences in all other ADC parameters between benign and malignant groups. In subgroup classification of benign tumors, there are significant differences in all ADC parameters between pleomorphic adenoma and Warthin’s tumor (area under curve 0.988; sensitivity 93.8%; specificity 94.7%; all ps < 0.05). Pleomorphic adenoma has high value in mean than basal cell adenoma (area under curve 0.819; sensitivity 76.9%; specificity 76.9%; p < 0.05). Basal cell adenoma has high values in mean (area under curve 0.897; sensitivity 92.3%; specificity 78.9%; all ps < 0.05) and 10th, 25th, 50th percentiles than Warthin’s tumor. In subgroup classification of malignant tumors, low-risk parotid carcinomas have higher values than hematolymphoid tumors in mean (area under curve 0.912; sensitivity 84.6%; specificity 100%, all ps < 0.05) and 10th, 25th percentiles. Conclusion: ADC histogram parameters, especially mean and 10th, 25th percentiles, can potentially be an effective indicator for identifying and classifying parotid tumors.

scholarly journals Grading of meningioma tumors based on analyzing tumor volumetric histograms obtained from conventional MRI and apparent diffusion coefficient images

2021 ◽  
Vol 52 (1) ◽  
Author(s):  
Meysam Haghighi Borujeini ◽  
Masoume Farsizaban ◽  
Shiva Rahbar Yazdi ◽  
Alaba Tolulope Agbele ◽  
Gholamreza Ataei ◽  
...  
Keyword(s):  
Diffusion Coefficient ◽  
Apparent Diffusion Coefficient ◽  
Predictive Value ◽  
Statistical Test ◽  
Post Contrast ◽  
Parametric Regression ◽  
Conventional Mri ◽  
Apparent Diffusion ◽  
Grade Ii

Abstract Background Our purpose was to evaluate the application of volumetric histogram parameters obtained from conventional MRI and apparent diffusion coefficient (ADC) images for grading the meningioma tumors. Results Tumor volumetric histograms of preoperative MRI images from 45 patients with the diagnosis of meningioma at different grades were analyzed to find the histogram parameters. Kruskal-Wallis statistical test was used for comparison between the parameters obtained from different grades. Multi-parametric regression analysis was used to find the model and parameters with high predictive value for the classification of meningioma. Mode; standard deviation on post-contrast T1WI, T2-FLAIR, and ADC images; kurtosis on post-contrast T1WI and T2-FLAIR images; mean and several percentile values on ADC; and post-contrast T1WI images showed significant differences among different tumor grades (P < 0.05). The multi-parametric linear regression showed that the ADC histogram parameters model had a higher predictive value, with cutoff values of 0.212 (sensitivity = 79.6%, specificity = 84.3%) and 0.180 (sensitivity = 70.9%, specificity = 80.8%) for differentiating the grade I from II, and grade II from III, respectively. Conclusions The multi-parametric model of volumetric histogram parameters in some of the conventional MRI series (i.e., post-contrast T1WI and T2-FLAIR images) along with the ADC images are appropriate for predicting the meningioma tumors’ grade.

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