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

Abstract

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.

Keywords: Apparent diffusion coefficient; Diffusion-weighted imaging; Histogram; Parotid tumor.

Figure 1.

The measurement method of the ADC histogram, taking a 32-year-old female with pleomorphic adenoma as an example. (a) original DWI image; (b) irregular ROIs are manually placed on all DW imaging (b1000 map) slices to encompass the whole tumor; (c) generated ADC Histogram, the highest ADC value is at count 52, the highest value is 1.103 × 10⁻³ mm²/s. ADC, apparent diffusion coefficient; DW, diffusion-weighted.

Figure 2.

ROC curve of mean ADC value performed in subgroup analysis of benign tumors. ADC, apparent diffusion coefficient; ROC, receiver operating characteristic.

Figure 3.

ROC curve of mean ADC value between low-risk parotid carcinoma and MALToma. ADC, apparent diffusion coefficient; ROC, receiver operating characteristic.