Wang Tong-Xing, Huang Xiao-Bin, Fu Tong, Gao Yu-Jia, Zhang Di, Liu Lin-Dong, Zhang Ya-Mei, Lin Hai, Yuan Jian-Min, Mao Cun-Nan, Wu Xin-Ying
Department of Radiology, Nanjing First Hospital, Nanjing Medical University, No. 68, Changle Road, Nanjing, Jiangsu Province, 210006, China.
Central Research Institute, United Imaging Healthcare, Shanghai, China.
BMC Med Imaging. 2025 Apr 7;25(1):110. doi: 10.1186/s12880-025-01645-w.
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.
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.
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.
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.
Not applicable.
本研究旨在识别与偏头痛诊断及分为有先兆偏头痛（MwA）和无先兆偏头痛（MwoA）相关的脑影像组学特征，并基于这些标志物开发预测模型。
我们回顾性分析了88例偏头痛患者（32例MwA和56例MwoA）及49例健康对照者（HCs）的磁共振成像。通过直方图分析从参与者中提取代表灰质形态学和扩散特性的特征。这些特征在交叉验证循环中经过全相关特征选择程序，以识别对偏头痛诊断和分型具有显著判别力的特征。基于所选特征，在30例患者（10例MwA）和17例HCs的独立样本中测试了从前一个样本构建的随机森林模型的预测能力。
患者与HCs之间，或MwA与MwoA之间，在全脑体积或灰质体积上均未发现总体差异（所有P值>0.05）。有6个特征在患者与HCs之间对于偏头痛诊断有显著差异，4个特征在MwA与MwoA之间对于分型有显著差异（所有P值<0.001）。4个特征与头痛严重程度评分显著相关（所有P值<0.01）。基于这些相关特征，随机森林模型在测试队列中区分患者与HCs的准确率为80.9%，区分MwA与MwoA的准确率为76.7%。
我们的研究结果表明，偏头痛患者的脑影像组学改变可能潜在地作为一种生物标志物，有助于偏头痛的诊断和分型，为个性化治疗策略提供依据。
不适用。
