Enhanced detection of headache presentation in unruptured brain arteriovenous malformation through combined radiologic features: A cross-sectional study

Abstract

Background

Although determining angioarchitecture provide qualitative insights into headache-susceptible brain arteriovenous malformation (BAVM), the potential of quantitative radiomics to detect headache in unruptured BAVM remains unclear. We developed classification models that integrate radiomic features and angioarchitecture to assist unruptured BAVM headache treatment decision-making.

Methods

We considered patients with unruptured BAVM who underwent magnetic resonance imaging between 2010 and 2023. 146 radiomic features were assessed. Radiomic features were delineated, and angioarchitecture was analyzed. Statistical analyses, including least absolute shrinkage and selection operator regression and logistic regression, were used to select features and develop models. Receiver operating characteristic and decision curve analyses were performed to evaluate performance.

Results

The clinical model based on age, sex, and parieto-occipital lesion location achieved an area under the curve (AUC) of 0.741. Adding two significant radiomic features and one angioarchitecture feature enhanced the models. The radiomic and angioarchitecture models achieved an AUC of 0.763. The combined model, with an AUC of 0.799, significantly outperformed the clinical model ($P=0.046$). Decision curve analysis indicated that the combined model performed best at threshold probabilities between 15% and 40%.

Conclusion

Integrating radiomic features and angioarchitecture enhances the identification of unruptured BAVM headache.