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Apparent diffusion coefficient histogram analysis for differentiating fibroblastic meningiomas from non-fibroblastic
Tao Han1, Changyou Long2, Xianwang Liu1
1Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730030, China; Second Clinical School, Lanzhou University, Lanzhou 730030, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou 730030, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730030, China.
Purpose:
To investigate the role of apparent diffusion coefficient (ADC) histogram analysis in differentiating fibroblastic meningiomas (FM) from non-fibroblastic WHO grade 1 meningiomas (nFM).
Methods:
This retrospective study analyzed the histopathological and diagnostic imaging data of 220 patients with histopathologically confirmed FM and nFM. The whole tumors were delineated on axial ADC images, and histogram parameters (mean, variance, skewness, kurtosis, as well as the 1st, 10th, 50th, 90th, and 99th percentile ADC [ADCp1, ADCp10, ADCp50, ADCp90, and ADCp99, respectively]) were obtained. Multivariate logistic regression analysis was used to identify the most valuable variables for discriminating FM from nFM WHO grade 1 meningiomas, and their diagnostic efficacy in differentiating FM from nFM before surgery was assessed using receiver operating characteristic (ROC) curves.
Results:
The mean, variance, ADCp50, ADCp90, and ADCp99 of the FM group were all lower than those of the nFM group (P < 0.05), there was significant difference in location and sex (P < 0.05). Multivariate logistic regression showed ADCp99 (P < 0.001) and location (P = 0.007) were the most valuable parameters in the discrimination of FM and nFM WHO grade 1 meningiomas. The diagnostic efficacy was achieved an AUC of 0.817(95% CI, 0.759-0.866), the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were 66.4%, 83.6%, 75.0%, 80.2%, and 71.3%, respectively.
Conclusion:
ADC histogram analysis is helpful in noninvasive differentiation of FM and nFM WHO grade 1 meningiomas, and combined ADCp99 and location have the best diagnostic efficacy.
Insights
Apparent diffusion coefficient (ADC) histogram analysis can help distinguish fibroblastic meningiomas (FM) from non-fibroblastic WHO grade 1 meningiomas (nFM). The 99th percentile ADC value (ADCp99) and tumor location showed the best diagnostic performance.
Area of Science:
- Neuroradiology
- Oncology
- Medical Imaging
Background:
- Meningiomas are common primary tumors of the central nervous system.
- Differentiating fibroblastic meningiomas (FM) from non-fibroblastic WHO grade 1 meningiomas (nFM) preoperatively is challenging.
- Accurate preoperative differentiation can aid in treatment planning and patient management.
Purpose of the Study:
- To evaluate the utility of apparent diffusion coefficient (ADC) histogram analysis in differentiating FM from nFM.
- To identify key histogram parameters that can discriminate between these meningioma subtypes.
- To assess the diagnostic performance of ADC histogram analysis for noninvasive meningioma subtyping.
Main Methods:
- Retrospective analysis of 220 patients with histopathologically confirmed FM and nFM.
- Whole-tumor segmentation on axial ADC images for histogram parameter extraction (mean, variance, skewness, kurtosis, percentiles).
- Multivariate logistic regression and receiver operating characteristic (ROC) curve analysis to determine discriminatory parameters and diagnostic efficacy.
Main Results:
- Lower mean, variance, ADCp50, ADCp90, and ADCp99 values were observed in the FM group compared to the nFM group (P < 0.05).
- Multivariate analysis identified ADCp99 (P < 0.001) and tumor location (P = 0.007) as the most significant discriminators.
- The combined model achieved an AUC of 0.817, with 66.4% sensitivity and 83.6% specificity.
Conclusions:
- ADC histogram analysis provides a valuable noninvasive method for differentiating FM from nFM.
- ADCp99 and tumor location are the most effective parameters for this differentiation.
- This technique can assist in preoperative diagnosis and management of WHO grade 1 meningiomas.
