Discrimination between microcystic meningioma and atypical meningioma using whole-lesion apparent diffusion coefficient histogram analysis. Issue 11 (November 2022)

• Record Type: Journal Article
• Authors: Liu, X.; Huang, X.; Han, T.; Li, S.; Xue, C.; Deng, J.; Zhou, Q.; Sun, Q.; Zhou, J.
• Is Part Of: Clinical radiology. Volume 77:Issue 11(2022)
• Page Start: 864
• Page End: 869
• Publication Date: 2022-11
• Subjects: Medical radiology -- Periodicals; Radiotherapy -- Periodicals; Radiotherapy -- Periodicals; Radiology -- Periodicals; Societies, Medical -- Periodicals; Medical radiology; Radiotherapy; Electronic journals; Periodicals; 616.0757
• DOI: 10.1016/j.crad.2022.07.004
• Languages: English
• ISSNs: 0009-9260

Abstract

Abstract : AIM: To explore the value of whole-lesion apparent diffusion coefficient (ADC) histogram analysis in discriminating microcystic meningioma (MCM) from atypical meningioma (AM). MATERIALS AND METHODS: Clinical and preoperative MRI data of 20 patients with MCM and 26 patients with AM were analysed retrospectively. Whole-lesion apparent diffusion coefficient (ADC) histogram analysis was performed on each patient's lesion to obtain histogram parameters, including mean, variance, skewness, kurtosis, the 1st (ADCp1), 10th (ADCp10), 50th (ADCp50), 90th (ADCp90), and 99th (ADCp99) percentiles of ADC. The differences between the ADC histogram parameters of the two tumours were compared, and the receiver operating characteristic (ROC) curve was used to assess the diagnostic performance of statistically significant parameters in distinguishing the two tumours. RESULTS: The mean, ADCp1, ADCp10, ADCp50, and ADCp90 of MCM were greater than those of AM, and significant differences were observed in these parameters between MCM and AM (all p <0.05). ROC analysis showed that the mean had the highest area under the curve value (AUC) in distinguishing the two tumours (AUC = 0.852), when using 120.46 × 10 −6 mm 2 /s as the optimal threshold, the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value for discriminating the two groups were 84.6%, 75%, 80.4%, 81.5%, and 78.9%, respectively. CONCLUSION: Histogram analysis based on whole-lesion ADC maps was useful for discriminating between MCM from AM preoperatively, with the mean being the most promising potential parameter. Highlights: ADC histogram analysis can distinguishing MCM from AM. The mean showed the best differential diagnostic performance. This is the first study to discriminate MCM and AM using ADC histogram analysis.