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

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 (ADCHS-ROI )-based and histogram-based approach. Histogram parameters included mean (ADCmean ), median (ADCmedian ), 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: ADCHS-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 ADCHS-ROI , ADCmean , ADCmedian , ADC10 and ADC90 value, followed by malignant tumors and pleomorphic adenomas (All Ps < 0.05). ADC10 out-performed ADCHS-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.