European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2018
Histogram analysis of apparent diffusion coefficient maps for differentiating malignant from benign parotid gland tumors.
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Ma Gao, Zhu Liu-Ning, Su Guo-Yi, Hu Hao, Qian Wen, Bu Shou-Shan, Xu Xiao-Quan, Wu Fei-Yun
Аннотация
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.
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.