Cerebral morphometric alterations predict the outcome of migraine diagnosis and subtyping: a radiomics analysis
Tong-Xing Wang1, Xiao-Bin Huang1, Tong Fu1
1Department of Radiology, Nanjing First Hospital, Nanjing Medical University, No. 68, Changle Road, Nanjing, Jiangsu Province, 210006, China.
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.
Insights
Cerebral radiomic features can help diagnose migraine and differentiate between migraine with aura (MwA) and migraine without aura (MwoA). These imaging markers show potential for personalized migraine treatment strategies.
Area of Science:
- Neuroimaging
- Radiomics
- Medical Diagnostics
Background:
- Migraine diagnosis and subtyping remain challenging.
- Cerebral radiomic features offer potential biomarkers for migraine.
Purpose of the Study:
- Identify cerebral radiomic features for migraine diagnosis.
- Differentiate between migraine with aura (MwA) and migraine without aura (MwoA).
- Develop predictive models for migraine diagnosis and subtyping.
Main Methods:
- Retrospective analysis of MR imaging from 88 migraine patients and 49 healthy controls.
- Extraction of gray matter morphometry and diffusion properties using histogram analysis.
- Random forest models developed and tested for diagnostic and subtype prediction.
Main Results:
- Six radiomic features significantly differed between migraine patients and healthy controls.
- Four features distinguished MwA from MwoA.
- Random forest models achieved 80.9% accuracy for migraine diagnosis and 76.7% for subtyping in the testing cohort.
Conclusions:
- Cerebral radiomic alterations may serve as biomarkers for migraine diagnosis and subtyping.
- These findings could aid in developing personalized treatment strategies for migraine patients.
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