Altered brain structural topological properties and its correlations with clinical characteristics in episodic migraine without aura.

PURPOSE: To investigate the topological alterations of the whole-brain white matter structural networks in episodic migraine (EM) without aura. METHODS: Forty-five EM patients without aura and 35 age- and sex-matched healthy controls were registered, and underwent diffusion tensor MRI acquisition at interictal. Graph theory-based analyses were then performed for the characterization of brain structural network properties. Pearson correlation analysis was performed on each network metric between the EM patients and healthy controls. RESULTS: The EM patients exhibited abnormal global network properties and local network topology that were characterized by more strongly integrated, more efficient, and faster information transferring. These network differences were widely located in the occipital, temporal, and parietal regions. Additionally, the local efficient of global parameters showed positive correlation with visual analogue scale, and along with prolonging disease duration, the nodal efficiency would be reduced, and the nodal shortest path length would be increased. Headache Impact Test version 6 scores have negative correlation with the nodal shortest path length, and positive correlations with the nodal efficiency. CONCLUSION: The results indicate that EM patients had aberrant topological structure and make a better understanding of structural connectivity in EM; it may provide imaging evidence for clinical study of migraine pathogenesis.

Deep learning-assisted diagnosis of large vessel occlusion in acute ischemic stroke based on four-dimensional computed tomography angiography.

Purpose: To develop deep learning models based on four-dimensional computed tomography angiography (4D-CTA) images for automatic detection of large vessel occlusion (LVO) in the anterior circulation that cause acute ischemic stroke. Methods: This retrospective study included 104 LVO patients and 105 non-LVO patients for deep learning models development. Another 30 LVO patients and 31 non-LVO patients formed the time-independent validation set. Four phases of 4D-CTA (arterial phase P1, arterial-venous phase P2, venous phase P3 and late venous phase P4) were arranged and combined and two input methods was used: combined input and superimposed input. Totally 26 models were constructed using a modified HRNet network. Assessment metrics included the areas under the curve (AUC), accuracy, sensitivity, specificity and F1 score. Kappa analysis was performed to assess inter-rater agreement between the best model and radiologists of different seniority. Results: The P1 + P2 model (combined input) had the best diagnostic performance. In the internal validation set, the AUC was 0.975 (95%CI: 0.878-0.999), accuracy was 0.911, sensitivity was 0.889, specificity was 0.944, and the F1 score was 0.909. In the time-independent validation set, the model demonstrated consistently high performance with an AUC of 0.942 (95%CI: 0.851-0.986), accuracy of 0.902, sensitivity of 0.867, specificity of 0.935, and an F1 score of 0.901. The best model showed strong consistency with the diagnostic efficacy of three radiologists of different seniority (k = 0.84, 0.80, 0.70, respectively). Conclusion: The deep learning model, using combined arterial and arterial-venous phase, was highly effective in detecting LVO, alerting radiologists to speed up the diagnosis.

Prediction of unenhanced lesion evolution in multiple sclerosis using radiomics-based models: a machine learning approach.

BACKGROUND: The volume change of multiple sclerosis (MS) lesion is related to its activity and can be used to assess disease progression. Therefore, the purpose of this study was to develop radiomics models for predicting the evolution of unenhanced MS lesions by using different kinds of machine learning algorithms and explore the optimal model. METHODS: In this prospective observation, 45 follow-up MR images obtained in 36 patients with MS (mean age 32.53±10.91; 23 women, 13 men) were evaluated. The lesions will be defined as interval activity and interval inactivity, respectively, based on the percentage of enlargement or reduction of the lesion >20% in the follow-up MR images. We extracted radiomic features of lesions on FLAIR images, and used recursive feature elimination (RFE), ReliefF algorithm and least absolute shrinkage and selection operator (LASSO) for feature selection, then three classification models including logistic regression, random forest and support vector machine (SVM) were used to build predictive models. The performance of the models were evaluated based on the sensitivity, specificity, precision, negative predictive value (NPV) and receiver operating characteristic curve (ROC) curves analyses. RESULTS: 135 interval inactivity lesions and 110 interval activity lesions were registered in our study. A total of 972 radiomics features were extracted, of which 265 were robust. The consistency and effectiveness of model performance were compared and verified by different combinations of feature selection and machine learning methods in different K-fold cross-validation strategies where K ranges from 5 to 10, thus demonstrating the stability and robustness. SVM classifier with ReliefF algorithm had the best prediction performance with an average accuracy of 0.827, sensitivity of 0.809, specificity of 0.841, precision of 0.921, NPV of 0.948 and the areas under the ROC curves (AUC) of 0.857 (95% CI: 0.812-0.902) in the cohorts. CONCLUSION: The results demonstrated that the radiomics-based machine learning model has potential in predicting the evolution of MS lesions.