Abstract
BACKGROUND: This study aimed to identify cerebral radiomic features related to migraine diagnosis and subtyping into migraine with aura (MwA) and migraine without aura (MwoA) and to develop predictive models based on these markers. METHOD: We retrospectively analyzed MR imaging from 88 migraine patients (32 MwA and 56 MwoA) and 49 healthy control subjects (HCs). Features representing the gray matter morphometry and diffusion properties were extracted from participants via histogram analysis. These features were put through an all-relevant feature selection procedure within cross-validation loops to identify features with significant discriminative power for migraine diagnosis and subtyping. Based on the selected features, the predictive ability of the random forest models constructed from the previous sample was tested in an independent sample of 30 patients (10 MwA) and 17 HCs. RESULT: No overall differences in total brain volume or gray matter volume were revealed between patients and HCs, or between MwA and MwoA (all P values > 0.05). Six features significantly differed between patients and HCs for migraine diagnosis, and four features distinguished MwA from MwoA for subtyping (all P values < 0.001). Four features were significantly correlated with headache severity score (all P values < 0.01). Based on these relevant features, the random forest models achieved accuracies of 80.9% in distinguishing patients from HCs and 76.7% in differentiating MwA from MwoA in the testing cohort. CONCLUSION: Our findings suggest cerebral radiomic alterations in migraine patients may potentially serve as a biomarker to assist in migraine diagnosis and subtyping, contributing to personalized treatment strategy. CLINICAL TRIAL NUMBER: Not applicable.