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MRI morphological features combined with apparent diffusion coefficient can predict brain invasion in meningioma
Xiaoyu Huang1, Yuntai Cao2, Guojin Zhang3
1Department of Radiology, The Fifth Affiliated Hospital of Zunyi Medical University, Zhuhai, China.
Objectives:
Accurately predicting meningioma brain invasion preoperatively helps to select the appropriate surgical approach and predict prognosis, but there are few imaging features that are sufficient for discriminating it alone. We investigate the joint MR imaging features and apparent diffusion coefficient (ADC) to predict the risk of brain invasion of meningiomas preoperatively.
Methods:
In this retrospective study, 143 patients (invasion group:51, non-invasion group: 92) diagnosed with meningioma by histopathology were included. The maximum (ADCmax), minimum (ADCmin) and mean (ADCmean) values of ADC and the mean ADC values of a comparative ROI in the normal appearing white matter (ADCNAWM) were calculated. Differences between clinical features, MRI morphological features, and all ADC values were assessed by Pearson's chi-square test and Kruskal-Wallis rank-sum test. Stepwise logistic regression analysis was used to select the optimal features and construct a prediction model. Furthermore, A nomogram was used to predict the risk of brain invasion, and a decision curve was used to verify the clinical utility of the nomogram.
Results:
According to stepwise logistic regression analysis, we found that sex, maximum diameter, peritumoral edema and ADCmin were closely related to brain invasion in meningioma. The model of the above four variables has the optimal discriminative ability to predict brain invasion, with an AUC of 0.924 (95 % CI, 0.879-0.969) and a sensitivity of 92.2 % (95 % CI, 74.5%-98.0 %).
Conclusions:
Combining clinical features, MRI morphological characteristics and ADCmin, the model exhibits excellent discriminatory ability and high sensitivity, which can be used for predicting the risk of brain invasion of meningiomas.
Insights
Predicting meningioma brain invasion preoperatively is crucial. Combining clinical data, MRI features, and minimum apparent diffusion coefficient (ADCmin) values accurately identifies invasion risk.
Area of Science:
- Neurosurgery
- Radiology
- Oncology
Background:
- Accurate preoperative prediction of meningioma brain invasion is essential for surgical planning and prognosis.
- Existing imaging features alone are often insufficient for definitive discrimination.
- Investigating combined imaging and diffusion parameters can improve predictive accuracy.
Purpose of the Study:
- To evaluate the combined utility of magnetic resonance imaging (MRI) features and apparent diffusion coefficient (ADC) values for predicting preoperative meningioma brain invasion.
- To develop and validate a predictive model for meningioma brain invasion risk.
Main Methods:
- Retrospective analysis of 143 meningioma patients (51 invasion, 92 non-invasion) with histopathological confirmation.
- Calculation of ADC values (ADCmax, ADCmin, ADCmean) and normal white matter ADC (ADCNAWM).
- Statistical analysis including logistic regression to identify predictive features and construct a nomogram for risk prediction.
Main Results:
- Stepwise logistic regression identified sex, maximum tumor diameter, peritumoral edema, and ADCmin as significant predictors of brain invasion.
- The combined model achieved an area under the curve (AUC) of 0.924, demonstrating excellent discriminative ability.
- The model showed high sensitivity (92.2%) for predicting brain invasion.
Conclusions:
- A predictive model integrating clinical factors, MRI morphology, and ADCmin offers high accuracy and sensitivity for preoperative meningioma brain invasion risk assessment.
- This model can aid in surgical decision-making and prognostic evaluation.
- The findings highlight the value of combining quantitative diffusion metrics with conventional imaging and clinical data.
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