Histogram analysis of apparent diffusion coefficient maps for differentiating malignant from benign parotid gland tumors

Abstract

Purpose

To evaluate the diagnostic performance of histogram parameters derived from diffusion-weighted imaging (DWI) for differentiating malignant from benign parotid gland tumors compared with that of hotspot region of interest (ROI)-based apparent diffusion coefficient (ADC) measurement.

Methods

Our study retrospectively enrolled 60 patients with parotid gland tumors who had undergone DWI scan for pre-treatment evaluation. ADC measurements were performed using hotspot ROI (ADC_HS-ROI)-based and histogram-based approach. Histogram parameters included mean (ADC_mean), median (ADC_median), 10th (ADC₁₀), 90th (ADC₉₀) percentiles, skewness and kurtosis of ADC. Mann–Whitney U test, Kruskal–Wallis test with post hoc Dunn–Bonferroni method and receiver operating characteristic (ROC) curve analyses were used for statistical analyses.

Results

ADC_HS-ROI and ADC histogram parameters showed no significant differences between malignant and benign parotid gland tumors (All Ps > 0.05). Within the sub-group analyses, Warthin's tumors showed the lowest ADC_HS-ROI, ADC_mean, ADC_median, ADC₁₀ and ADC₉₀ value, followed by malignant tumors and pleomorphic adenomas (All Ps < 0.05). ADC₁₀ out-performed ADC_HS-ROI in differentiating malignant tumors from pleomorphic adenomas (area under curve, 0.890 vs 0.821; sensitivity, 79.31 vs 82.76%; specificity, 90.91 vs 72.73%; P = 0.016), and improved the diagnostic performance in differentiating malignant tumors from Warthin's tumors (area under curve, 1.000 vs 0.965; sensitivity, 100.00 vs 90.91%), although the difference was not significant (P = 0.348).

Conclusions

ADC histogram analysis, especially ADC₁₀, might be a promising imaging biomarker for characterizing parotid gland tumors.

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