Differentiating atypical meningioma from anaplastic meningioma using diffusion weighted imaging
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Published on: December 15, 2014
Tao Han1, Jing Zhang1, Xianwang Liu1
1Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730030, China; Second Clinical School, Lanzhou University, Lanzhou 730000, 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 explore the value of MRI conventional features and apparent diffusion coefficient (ADC) on the differential diagnosis of atypical meningioma (AtM) and anaplastic meningioma (AnM).
Materials And Methods:
This retrospective study analyzed the preoperative clinical data, MRI conventional features, and DWI data of 55 AtM and 25 AnM confirmed by pathology in our hospital. The clinical features, MRI conventional features, ADCmean, ADCmin, and relative ADC (rADC) values were compared between the two tumors by Chi-square test or an independent sample t-test. Receiver operating characteristic curve (ROC) and binary logistic regression analysis were used to evaluate the diagnostic efficacy of each parameter to differentiate between these tumors.
Results:
The MRI conventional features had a certain ability to distinguish AnM and AtM, with an area under the curve value (AUC) of 0.824 (95% CI, 0.723-0.900). The ADCmean, ADCmin, and rADC values were significantly higher in AtM compared to AnM (all P < 0.05). ADCmean had the best identification effect with an AUC of 0.867 (95% CI, 0.772-0.933) among them, at an cut-off of 0.817 × 10-3 mm2/s, the sensitivity and specificity of distinguishing AtM from AnM were 78.18% and 88.00%, respectively. A combination of ADCmean and MRI conventional features showed the optimum discrimination ability for the two tumors, the AUC, sensitivity, specificity, and accuracy were 0.918 (95% CI, 0.835-0.967), 80.00%, 94.55%, and 90.00%, respectively.
Conclusion:
MRI conventional features combined with ADCmean, as a non-invasive method, has potential clinical value in the preoperative diagnosis of AtM and AnM.
Insights
This study found that combining MRI conventional features with apparent diffusion coefficient (ADC) mean values significantly improves the non-invasive diagnosis of atypical meningioma (AtM) and anaplastic meningioma (AnM). This approach offers high accuracy in differentiating these brain tumors preoperatively.
Area of Science:
- Neuroradiology
- Oncology
- Medical Imaging
Background:
- Meningiomas are tumors arising from the meninges.
- Atypical meningioma (AtM) and anaplastic meningioma (AnM) are aggressive subtypes requiring accurate preoperative differentiation.
- Distinguishing between AtM and AnM using conventional MRI features alone can be challenging.
Purpose of the Study:
- To evaluate the diagnostic value of conventional MRI features and apparent diffusion coefficient (ADC) measurements for differentiating AtM from AnM.
- To determine the combined efficacy of MRI features and ADC values in preoperative diagnosis.
Main Methods:
- Retrospective analysis of 55 AtM and 25 AnM cases.
- Comparison of clinical data, conventional MRI features, and diffusion-weighted imaging (DWI) parameters including ADCmean, ADCmin, and relative ADC (rADC).
- Statistical analysis using Chi-square test, independent sample t-test, ROC curve analysis, and binary logistic regression.
Main Results:
- Conventional MRI features showed moderate diagnostic ability (AUC=0.824).
- ADCmean, ADCmin, and rADC values were significantly higher in AtM than AnM (P < 0.05).
- ADCmean demonstrated the best individual performance (AUC=0.867).
- The combination of ADCmean and conventional MRI features yielded optimal discrimination (AUC=0.918, sensitivity=80.00%, specificity=94.55%, accuracy=90.00%).
Conclusions:
- Conventional MRI features and ADCmean are valuable non-invasive tools for differentiating AtM and AnM.
- The combined approach offers high accuracy for preoperative diagnosis of these meningioma subtypes.
- This strategy can aid in surgical planning and patient management.
