Differentiation between nasopharyngeal carcinoma and lymphoma at the primary site using whole-tumor histogram analysis of apparent diffusion coefficient maps

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 < 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.

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The Histogram Analysis of Apparent Diffusion Coefficient Maps with Standard- or Ultrahigh-b Value Diffusion-Weighted MR Imaging for Differentiating the Gleason Grade of Prostate Cancer

Journal of Medical Imaging and Health Informatics ◽

10.1166/jmihi.2018.2363 ◽

2018 ◽

Vol 8 (3) ◽

pp. 577-582 ◽

Cited By ~ 2

Author(s):

Zhi-Hua Lu ◽

Wen-Lu Zhao ◽

Li-Biao Ji ◽

Ru Wen ◽

Yong-Sheng Zhang ◽

...

Keyword(s):

Prostate Cancer ◽

Diffusion Coefficient ◽

Mr Imaging ◽

Apparent Diffusion Coefficient ◽

B Value ◽

Histogram Analysis ◽

Gleason Grade ◽

Diffusion Weighted ◽

Apparent Diffusion ◽

Apparent Diffusion Coefficient Maps

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

Chinese Journal of Academic Radiology ◽

10.1007/s42058-019-00019-w ◽

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.

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