Syoji Kobashi

BACKGROUND: Hematoma expansion (HE) is a significant risk factor for poor prognosis in patients with intracerebral hemorrhage (ICH). Accurately predicting HE is crucial for determining optimal treatment strategies. METHODS: This study enrolled 452 patients with ICH from 10 hospitals. To predict HE, 28 clinical variables available on patient arrival (including medical history, ICH location, and ICH volume) and 1142 radiomics features extracted from noncontrast computed tomography images of the ICH regions were used. Clinical variables and radiomics features were selected using gradient boosting and the least absolute shrinkage and selection operator. Three HE prediction models were built on clinical variables alone, radiomics features alone, and a third combining both. The models were compared using 5-fold cross-validation, and the mean area under the receiver operating characteristic curve was calculated for each. Additionally, the important features of HE prediction in the combined model were explored. RESULTS: The combined model demonstrated the highest performance for predicting HE with a 5-fold mean area under the receiver operating characteristic curve of 0.77±0.05, compared with 0.70±0.06 for the clinical variables alone and 0.73±0.04 for the radiomics features alone. Permutation feature importance analysis suggested that anticoagulant treatment was the most predictive of HE. CONCLUSIONS: A predictive model for HE was developed using the medical history, clinical features available on the patient's arrival, imaging, and radiomics features extracted from computed tomography images. This prediction model will assist non-stroke care specialists in making treatment decisions for ICH in emergency settings.

PURPOSE: Distinguishing idiopathic normal pressure hydrocephalus (iNPH) from progressive supranuclear palsy (PSP) presents a clinical challenge due to overlapping clinical symptoms such as gait disturbances and cognitive decline. This study presents a novel multi-scale deep learning framework that integrates global and local magnetic resonance imaging (MRI) features using a mixture of experts (MoE) mechanism, enhancing diagnostic accuracy and minimizing interobserver variability. METHODS: The proposed framework combines a 3D convolutional neural network (CNN) for capturing global volumetric features with a 2.5D recurrent CNN focusing on disease-specific regions of interest (ROIs), including the lateral ventricles, high convexity sulci, midbrain, and Sylvian fissures. The MoE mechanism dynamically weights global and local features, optimizing the classification process. Model performance was assessed using stratified fivefold cross-validation on T1-weighted MRI from 118 patients (53 iNPH, 65 PSP) to ensure balanced class distributions across training folds. RESULTS: The MoE model using ResNet-34 achieved an accuracy of 0.983 (95% CI 0.875-1.000), a recall of 0.985 (95% CI 0.750-1.000), a precision of 0.986 (95% CI 0.769-1.000), and an area under the curve (AUC) of 1.000 (95% CI 1.000-1.000), outperforming traditional morphological markers and single-branch deep learning models. The MoE mechanism allowed adaptive weighting of global and local features, contributing to both improved robustness and interpretability. Grad-CAM visualizations highlighted disease-specific regions, demonstrating that the model focused on relevant features in both successful and failure modes of the 3D CNN expert for iNPH and PSP. CONCLUSION: The dynamic integration of global and local MRI features through the MoE framework offers a powerful, robust, and interpretable tool for differentiating iNPH from PSP. This approach reduces reliance on subjective visual assessments and has the potential for broader clinical application through dataset expansion and multicenter validation.