MRI morphological features combined with apparent diffusion coefficient can predict brain invasion in meningioma

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.