scholarly journals Apparent diffusion coefficient (ADC) histogram analysis: differentiation of benign from malignant parotid gland tumors using readout-segmented diffusion-weighted imaging

2019 ◽  
Vol 48 (7) ◽  
pp. 20190100 ◽  
Author(s):  
Zanxia Zhang ◽  
Chengru Song ◽  
Yong Zhang ◽  
Baohong Wen ◽  
Jinxia Zhu ◽  
...  
Keyword(s):  
Diffusion Coefficient ◽  
Parotid Gland ◽  
Apparent Diffusion Coefficient ◽  
Diffusion Weighted Imaging ◽  
Operating Characteristic ◽  
Histogram Analysis ◽  
Diagnostic Efficiency ◽  
Parotid Gland Tumors ◽  
Pleomorphic Adenomas ◽  
Apparent Diffusion

Objectives To explore the utility of whole-lesion apparent diffusion coefficient (ADC) histogram analysis for differentiating parotid gland tumors following readout-segmented diffusion-weighted imaging (RESOLVE). Methods 80 patients (40 with pleomorphic adenomas, 14 with Warthin tumors, and 26 with malignant parotid gland tumors) who underwent routine head-and-neck MRI and RESOLVE examinations, were retrospectively evaluated. RESOLVE data were acquired from a MAGNETOM Skyra 3T MR system. Eleven whole-lesion histogram parameters derived from histogram analysis (ADC_mean, ADC_minimum, ADC_maximum, ADC_1th, ADC_10th, ADC_50th, ADC_90th, ADC_99th, skewness, variance and kurtosis) were calculated for each patient using MaZda. Receiver operating characteristic (ROC) curve analysis was used to assess the diagnostic performance of the ADC for distinguishing among the three groups. Results In total, nine parameters (ADC_minimum, ADC_maximum, ADC_mean, ADC_10th, ADC_50th, ADC_90th, ADC_99th, variance, skewness) were statistically significant (all p < 0.05) for all three groups, in the comparison of pleomorphic adenomas to Warthin tumors; the ADC_mean, ADC_50th, and skewness revealed high diagnostic efficiency with areas under the receiver operating characteristic curve of 0.976, 0.970, and 0.970, respectively. In the comparison of pleomorphic adenomas to malignant parotid gland tumors, these nine parameters were also found to be statistically different (all p < 0.05); the ADC_mean, ADC_10th and ADC_50th revealed high diagnostic efficiency with area under the curve of 0.851, 0.866, and 0.841, respectively. However, in the comparison of Warthin tumors to malignant parotid gland tumors, only three parameters (ADC_mean, ADC_50th, skewness) were statistically significant (all p < 0.05). Conclusions Whole-lesion ADC histograms are effective in differentiating common parotid gland tumors.

scholarly journals Diffusion-Weighted Imaging of the Parotid Gland: Can the Apparent Diffusion Coefficient Discriminate Between Normal and Abnormal Parotid Gland Tissues?

2017 ◽  
Vol 39 (3) ◽  
pp. 125-130 ◽  
Author(s):  
Ayman Mohamed Abdel Mottelb Aly Nada ◽  
Ayda Aly Youssef ◽  
Ayman Abdel Hamid El Basmy ◽  
Ayman Abdel Wahab Amin ◽  
Ahmed Mohamed Shokry
Keyword(s):  
Diffusion Coefficient ◽  
Parotid Gland ◽  
Apparent Diffusion Coefficient ◽  
Diffusion Weighted Imaging ◽  
Diffusion Weighted ◽  
Apparent Diffusion

scholarly journals Value of Apparent Diffusion Coefficient Histogram Analysis in the Differential Diagnosis of Nasopharyngeal Lymphoma and Nasopharyngeal Carcinoma Based on Readout-Segmented Diffusion-Weighted Imaging

2021 ◽  
Vol 11 ◽  
Author(s):  
Chengru Song ◽  
Peng Cheng ◽  
Jingliang Cheng ◽  
Yong Zhang ◽  
Shanshan Xie
Keyword(s):  
Diffusion Coefficient ◽  
Nasopharyngeal Carcinoma ◽  
Apparent Diffusion Coefficient ◽  
Diffusion Weighted Imaging ◽  
Diagnostic Performance ◽  
Validation Cohort ◽  
Statistical Significance ◽  
Histogram Analysis ◽  
Diffusion Weighted ◽  
Apparent Diffusion

BackgroundThis study aims to explore the utility of whole-lesion apparent diffusion coefficient (ADC) histogram analysis for differentiating nasopharyngeal lymphoma (NPL) from nasopharyngeal carcinoma (NPC) following readout-segmented echo-planar diffusion-weighted imaging (RESOLVE sequence).MethodsThirty-eight patients with NPL and 62 patients with NPC, who received routine head-and-neck MRI and RESOLVE (b-value: 0 and 1,000 s/mm2) examinations, were retrospectively evaluated as derivation cohort (February 2015 to August 2018); another 23 patients were analyzed as validation cohort (September 2018 to December 2019). The RESOLVE data were obtained from the MAGNETOM Skyra 3T MR system (Siemens Healthcare, Erlangen, Germany). Fifteen parameters derived from the whole-lesion histogram analysis (ADCmean, variance, skewness, kurtosis, ADC1, ADC10, ADC20, ADC30, ADC40, ADC50, ADC60, ADC70, ADC80, ADC90, and ADC99) were calculated for each patient. Then, statistical analyses were performed between the two groups to determine the statistical significance of each histogram parameter. A receiver operating characteristic curve (ROC) analysis was conducted to assess the diagnostic performance of each histogram parameter for distinguishing NPL from NPC and further tested in the validation cohort; calibration of the selected parameter was tested with Hosmer–Lemeshow test.ResultsNPL exhibited significantly lower ADCmean, variance, ADC1, ADC10, ADC20, ADC30, ADC40, ADC50, ADC60, ADC70, ADC80, ADC90 and ADC99, when compared to NPC (all, P &lt; 0.05), while no significant differences were found on skewness and kurtosis. Furthermore, ADC99 revealed the highest diagnostic efficiency, followed by ADC10 and ADC20. Optimal diagnostic performance (AUC = 0.790, sensitivity = 91.9%, and specificity = 63.2%) could be achieved when setting ADC99 = 1,485.0 × 10−6 mm2/s as the threshold value. The predictive performance was maintained in the validation cohort (AUC = 0.817, sensitivity = 94.6%, and specificity = 56.2%)ConclusionWhole-lesion ADC histograms based on RESOLVE are effective in differentiating NPC from NPL.

scholarly journals Whole-tumor histogram analysis of apparent diffusion coefficient maps in grading diagnosis of ependymoma

2020 ◽  
Vol 2 (3-4) ◽  
pp. 41-46
Author(s):  
Huiyu Huang ◽  
Yong Zhang ◽  
Jingliang Cheng ◽  
Mengmeng Wen
Keyword(s):  
Diffusion Coefficient ◽  
Apparent Diffusion Coefficient ◽  
Sensitivity And Specificity ◽  
Histogram Analysis ◽  
Diagnostic Efficiency ◽  
Diagnostic Efficacy ◽  
Who Grade ◽  
Significant Difference ◽  
Apparent Diffusion ◽  
Apparent Diffusion Coefficient Maps

Abstract Objective To study the value of whole-tumor histogram analysis which is based on apparent diffusion coefficient maps in grading diagnosis of ependymoma. Methods 71 patients with ependymal tumors were retrospectively analyzed, including 13 cases of WHO grade I, 28 cases of WHO grade II, and 30 cases of WHO grade III. Mazda software was used to draw the region of interest (ROI) in the apparent diffusion coefficient maps of three groups on every layer of tumor level. The whole-tumor gray histogram analysis was carried to obtained nine characteristic parameters, including mean, variance, kurtosis, skewness, Perc.01%, Perc.10%, Perc.50%, Perc.90%, and Perc.99%. When the parameters satisfy the test of normal distribution and homogeneity of variance, single factor analysis of variance (ANOVA) was carried to compare the three groups and LSD t test was performed to compare the two groups. Besides, the ROC curve was used to analyze the diagnostic efficacy of the parameters. Results Variance, Perc.01%, and Perc.10% had significant differences among the three groups (all P < 0.05). The remaining six parameters had no significant difference among the three groups (all P > 0.05). And, between WHO I and WHO II, the sensitivity and specificity of the Perc.10% were 85.7% and 100.0%, the AUC was 0.872, and the cut-off was 126.5. Between WHO I and WHO III, the sensitivity and specificity of the Perc.10% were 85.7% and 87.7%, the AUC was 0.835, and the optimum critical value was 131.33. Besides, the sensitivity, specificity, and AUC of variance between WHO II and WHO III are 68.4%, 76.9%, 0.794, and 2645.7, respectively. They had higher identification efficiency. Conclusion Whole-tumor histogram analysis of apparent diffusion coefficient (ADC) maps could provide ancillary diagnostic value in grading diagnosis of ependymoma. Perc.10% had a high diagnostic efficiency.

Value of apparent diffusion coefficient calculation in the differential diagnosis of parotid gland tumors

2007 ◽  
Vol 48 (9) ◽  
pp. 980-987 ◽  
Author(s):  
H. Yerli ◽  
A. M. Agildere ◽  
E. Aydin ◽  
E. Geyik ◽  
N. Haberal ◽  
...  
Keyword(s):  
Differential Diagnosis ◽  
Diffusion Coefficient ◽  
Parotid Gland ◽  
Apparent Diffusion Coefficient ◽  
Parotid Gland Tumors ◽  
Apparent Diffusion
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