Application of whole-tumor histogram analysis based on the apparent diffusion coefficient map of readout-segmented echo-planar diffusion-weighted imaging in differentiating common parotid neoplasms

Abstract

Objective The purpose of this study was to evaluate whether whole-tumor histogram-based analysis of readout-segmented echo-planar diffusion-weighted imaging (RESOLVE-DWI) ADC map can help in the discrimination of parotid gland tumors(pleomorphic adenoma, Warthin tumor, malignant parotid gland tumor). Methods The MR images(pre / post-contrast enhanced MRI, RESOLVE-DWI, ADC map) of 47 patients with a biopsy-or surgery-proven pleomorphic adenomas, 25 patients with Warthin tumors and 36 patients with malignant parotid gland tumors were retrospectively analyzed. Histogram-based analysis was performed with the software MaZda. ROIs were drawn on every section of the ADC map containing the tumor, then 12 Parameters(Area, MinNorm, MaxNorm, Mean, Variance, Skewness, Kurtosis, Perc.1%, Perc.10%, Perc.50%, Perc.90%, Perc.99%) derived from histogram were calculated. Statistical analysis among the three groups (One-way ANOVA or Kruskal-Wallis test) were performed to find out the statistical significance of each histogram parameter. Then LSD test or Mann-Whitney U test was used for pairwise comparison between groups. And the differential efficiency of each parameter was determined using ROC analysis. Results Overall, 9 parameters (MinNorm, MaxNorm, Mean, Variance, Skewness, Perc.10%, Perc.50%, Perc.90%, Perc.99%) among three groups were shown to be statistically significant (P<0.05). Between the pleomorphic adenomas and Warthin tumors, these 9 parameters were of statistical significance. Perc.50% revealed the highest diagnostic efficiency, followed by Mean and Perc.10%. Between the pleomorphic adenomas and malignant parotid gland tumors, also all these 9 parameters were of statistical significance. Mean was revealed to have the highest diagnostic efficiency, followed by Perc.10% and Perc.90%. However, between the Warthin tumors and malignant parotid gland tumors, only 5 parameters (MinNorm, Mean, Skewness, Perc.10%, Perc.50%) were statistically significant. MinNorm was revealed to have the highest diagnostic efficiency, followed by Perc.50% and Perc.10%. Generally, Mean, Perc.10% and Perc.50% were more effective in the differential diagnosis of these three types common parotid neoplasms. Conclusion Whole-tumor histogram analysis of ADC maps are effective in differentiating common parotid neoplasms. Key words: Parotid neoplasms; Diagnostic techniques and procedures; Diffusion magnetic resonance imaging