Gao Ma1, Liu-Ning Zhu2, Guo-Yi Su1, Hao Hu1, Wen Qian1, Shou-Shan Bu2, Xiao-Quan Xu3, Fei-Yun Wu4. 1. Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, No. 300, Guangzhou Rd., Gulou District, Nanjing, People's Republic of China. 2. Department of Stomatology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, People's Republic of China. 3. Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, No. 300, Guangzhou Rd., Gulou District, Nanjing, People's Republic of China. xiaoquanxu_1987@163.com. 4. Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, No. 300, Guangzhou Rd., Gulou District, Nanjing, People's Republic of China. wfy_njmu@163.com.
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 (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.
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.
Entities:
Keywords:
Diffusion-weighted imaging; Histogram; Magnetic resonance imaging; Parotid gland tumor
Authors: C R Habermann; C Arndt; J Graessner; L Diestel; K U Petersen; F Reitmeier; J O Ussmueller; G Adam; M Jaehne Journal: AJNR Am J Neuroradiol Date: 2009-01-08 Impact factor: 3.825