Vascular tortuosity is related to reduced thalamic volume after middle cerebral artery occlusion.

The mechanisms underlying secondary brain injury in remote areas remains unclear. This study aimed to investigate the relationship between vascular tortuosity and thalamic volume. METHODS: In this study, we retrospectively analyzed sixty-five patients with unilateral middle cerebral artery occlusion (MCAO) who underwent magnetic resonance angiography. We compared the vascular tortuosity in patients with MCAO and controls, and analyzed the relationship between vascular tortuosity and thalamic volume. RESULTS: Compared with controls, the MCAO group exhibited a significantly smaller thalamus volume on the affected side (5874 ± 183 mm3 vs. 5635 ± 383 mm3, p < 0.0001). The vascular tortuosity of the posterior cerebral artery (PCA) was higher in the MCAO group than in the controls (82.8 ± 17.3 vs. 76.7 ± 17.3, p = 0.040). Logistic regression analysis revealed that PCA tortuosity was an independent risk factor for reduced thalamic volume after MCAO (p = 0.034). In the subgroup analysis, only the 4-7-day group was not statistically different in thalamic volume between the MCAO and control groups. In the MCAO group, patients older than 60 years and female patients had a more tortuous PCA. CONCLUSION: Reduced thalamic volume after MCAO was associated with a tortuous PCA. After MCAO, PCA tortuosity increased more significantly in patients aged >60 years and in female patients.

Diagnosis of Alzheimer's disease using hypergraph p-Laplacian regularized multi-task feature learning.

Multimodal data-based classification methods have been widely used in the diagnosis of Alzheimer's disease (AD) and have achieved better performance than single-modal-based methods. However, most classification methods based on multimodal data tend to consider only the correlation between different modal data and ignore the inherent non-linear higher-order relationships between similar data, which can improve the robustness of the model. Therefore, this study proposes a hypergraph p-Laplacian regularized multi-task feature selection (HpMTFS) method for AD classification. Specifically, feature selection for each modal data is considered as a distinct task and the common features of multimodal data are extracted jointly by group-sparsity regularizer. In particular, two regularization terms are introduced in this study, namely (1) a hypergraph p-Laplacian regularization term to retain higher-order structural information for similar data, and (2) a Frobenius norm regularization term to improve the noise immunity of the model. Finally, using a multi-kernel support vector machine to fuse multimodal features and perform the final classification. We used baseline sMRI, FDG-PET, and AV-45 PET imaging data from 528 subjects in the Alzheimer's Disease Neuroimaging Initiative (ADNI) to evaluate our approach. Experimental results show that our HpMTFS method outperforms existing multimodal-based classification methods.

Machine learning algorithms for integrating clinical features to predict intracranial hemorrhage in patients with acute leukemia.

BACKGROUND: Intracranial hemorrhage (ICH) in acute leukemia (AL) patients leads to high morbidity and mortality, treatment approaches for ICH are generally ineffective. Thus, early identification of which subjects are at high risk of ICH is of key importance. Currently, machine learning can achieve well predictive capability through constructing algorithms that simultaneously exploit the information coming from clinical features. METHODS: After rigid data preprocessing, 42 different clinical features from 948 AL patients were used to train different machine learning algorithms. We used the feature selection algorithms to select the top 10 features from 42 clinical features. To test the performance of the machine learning algorithms, we calculated area under the curve (AUC) values from receiver operating characteristic (ROC) curves along with 95% confidence intervals (CIs) by cross-validation. RESULTS: With the 42 features, RF exhibited the best predictive power. After feature selection, the top 10 features were international normalized ratio (INR), prothrombin time (PT), creatinine (Cr), indirect bilirubin (IBIL), albumin (ALB), monocyte (MONO), platelet (PLT), lactic dehydrogenase (LDH), fibrinogen (FIB) and prealbumin (PA). Among the top 10 features, INR, PT, Cr, IBIL and ALB had high predictive performance with an AUC higher than 0.8 respectively. CONCLUSIONS: The RF algorithm exhibited a higher cross-validated performance compared with the classical algorithms, and the selected important risk features should help in individualizing aggressive treatment in AL patients to prevent ICH. Efforts that will be made to test and optimize in independent samples will warrant the application of such algorithm and predictors in the future.

Diagnosis of Alzheimer's disease using structure highlighting key slice stacking and transfer learning.

BACKGROUND: In recent years, two-dimensional convolutional neural network (2D CNN) have been widely used in the diagnosis of Alzheimer's disease (AD) based on structural magnetic resonance imaging (sMRI). However, due to the lack of targeted processing of the key slices of sMRI images, the classification performance of the CNN model needs to be improved. PURPOSE: Therefore, in this paper, we propose a key slice processing technique called the structural highlighting key slice stacking (SHKSS) technique, and we apply it to a 2D transfer learning model for AD classification. METHODS: Specifically, first, 3D MR images were preprocessed. Second, the 2D axial middle-layer image was extracted from the MR image as a key slice. Then, the image was normalized by intensity and mapped to the red, green, and blue (RGB) space, and histogram specification was performed on the obtained RGB image to generate the final three-channel image. The final three-channel image was input into a pretrained CNN model for AD classification. Finally, classification and generalization experiments were conducted to verify the validity of the proposed method. RESULTS: The experimental results on the Alzheimer's Disease Neuroimaging Initiative (ADNI) data set show that our SHKSS method can effectively highlight the structural information in MRI slices. Compared with existing key slice processing techniques, our SHKSS method has an average accuracy improvement of at least 26% on the same test data set, and it has better performance and generalization ability. CONCLUSIONS: Our SHKSS method not only converts single-channel images into three-channel images to match the input requirements of the 2D transfer learning model but also highlights the structural information of MRI slices to improve the accuracy of AD diagnosis.

Regression and Classification of Alzheimer's Disease Diagnosis Using NMF-TDNet Features From 3D Brain MR Image.

With the development of deep learning and medical imaging technology, many researchers use convolutional neural network(CNN) to obtain deep-level features of medical image in order to better classify Alzheimer's disease (AD) and predict clinical scores. The principal component analysis network (PCANet) is a lightweight deep-learning network that mainly uses principal component analysis (PCA) to generate multilevel filter banks for the centralized learning of samples and then performs binarization and generates blockwise histograms to obtain image features. However, the dimensions of the extracted PCANet features reaching tens of thousands or even hundreds of thousands, and the formation of the multilevel filter banks is sample data dependent, reducing the flexibility of PCANet. In order to solve these problems, in this paper, we propose a data-independent network based on the idea of PCANet, called the nonnegative matrix factorization tensor decomposition network (NMF-TDNet). Specifically, we use nonnegative matrix factorization (NMF) instead of PCA to create multilevel filter banks for sample learning, then uses the learning results to build a higher-order tensor and perform tensor decomposition (TD) to achieve data dimensionality reduction, producing the final image features. Finally, our method use these features as the input of the support vector machine (SVM) for AD classification diagnosis and clinical score prediction. The performance of our method is extensively evaluated on the ADNI-1, ADNI-2 and OASIS datasets. The experimental results show that NMF-TDNet can achieve data dimensionality reduction and the NMF-TDNet features as input achieved superior performance than using PCANet features as input.