The impact of the combat method on radiomics feature compensation and analysis of scanners from different manufacturers.

BACKGROUND: This study investigated whether the Combat compensation method can remove the variability of radiomic features extracted from different scanners, while also examining its impact on the subsequent predictive performance of machine learning models. MATERIALS AND METHODS: 135 CT images of Credence Cartridge Radiomic phantoms were collected and screened from three scanners manufactured by Siemens, Philips, and GE. 100 radiomic features were extracted and 20 radiomic features were screened according to the Lasso regression method. The radiomic features extracted from the rubber and resin-filled regions in the cartridges were labeled into different categories for evaluating the performance of the machine learning model. Radiomics features were divided into three groups based on the different scanner manufacturers. The radiomic features were randomly divided into training and test sets with a ratio of 8:2. Five machine learning models (lasso, logistic regression, random forest, support vector machine, neural network) were employed to evaluate the impact of Combat on radiomic features. The variability among radiomic features were assessed using analysis of variance (ANOVA) and principal component analysis (PCA). Accuracy, precision, recall, and area under the receiver curve (AUC) were used as evaluation metrics for model classification. RESULTS: The principal component and ANOVA analysis results show that the variability of different scanner manufacturers in radiomic features was removed (P˃0.05). After harmonization with the Combat algorithm, the distributions of radiomic features were aligned in terms of location and scale. The performance of machine learning models for classification improved, with the Random Forest model showing the most significant enhancement. The AUC value increased from 0.88 to 0.92. CONCLUSIONS: The Combat algorithm has reduced variability in radiomic features from different scanners. In the phantom CT dataset, it appears that the machine learning model's classification performance may have improved after Combat harmonization. However, further investigation and validation are required to fully comprehend Combat's impact on radiomic features in medical imaging.

Prognostic Value of a Combined Nomogram Model Integrating 3-Dimensional Deep Learning and Radiomics for Head and Neck Cancer.

OBJECTIVE: The preoperative prediction of the overall survival (OS) status of patients with head and neck cancer (HNC) is significant value for their individualized treatment and prognosis. This study aims to evaluate the impact of adding 3D deep learning features to radiomics models for predicting 5-year OS status. METHODS: Two hundred twenty cases from The Cancer Imaging Archive public dataset were included in this study; 2212 radiomics features and 304 deep features were extracted from each case. The features were selected by univariate analysis and the least absolute shrinkage and selection operator, and then grouped into a radiomics model containing Positron Emission Tomography /Computed Tomography (PET/CT) radiomics features score, a deep model containing deep features score, and a combined model containing PET/CT radiomics features score +3D deep features score. TumorStage model was also constructed using initial patient tumor node metastasis stage to compare the performance of the combined model. A nomogram was constructed to analyze the influence of deep features on the performance of the model. The 10-fold cross-validation of the average area under the receiver operating characteristic curve and calibration curve were used to evaluate performance, and Shapley Additive exPlanations (SHAP) was developed for interpretation. RESULTS: The TumorStage model, radiomics model, deep model, and the combined model achieved areas under the receiver operating characteristic curve of 0.604, 0.851, 0.840, and 0.895 on the train set and 0.571, 0.849, 0.832, and 0.900 on the test set. The combined model showed better performance of predicting the 5-year OS status of HNC patients than the radiomics model and deep model. The combined model was shown to provide a favorable fit in calibration curves and be clinically useful in decision curve analysis. SHAP summary plot and SHAP The SHAP summary plot and SHAP force plot visually interpreted the influence of deep features and radiomics features on the model results. CONCLUSIONS: In predicting 5-year OS status in patients with HNC, 3D deep features could provide richer features for combined model, which showed outperformance compared with the radiomics model and deep model.

Design and Test of Spatial Cognitive Training and Evaluation System Based on Virtual Reality Head-Mounted Display With EEG Recording.

The field of spatial cognitive training and evaluation has rapidly evolved. However, the low learning motivation and engagement of the subjects hinder the widespread use of spatial cognitive training. This study designed a home-based spatial cognitive training and evaluation system (SCTES), which aimed to train subjects on spatial cognitive tasks for 20 days, and compared the brain activities before and after the training. This study also evaluated the feasibility of using a portable all-in-one prototype for cognitive training that combined a virtual reality (VR) head-mounted display with high-quality electroencephalogram (EEG) recording. During the course of training, the length of the navigation path and the distance between the starting position and the platform position revealed significant behavioral differences. In the testing sessions, the subjects showed significant behavioral differences in the time it took to complete the test task before and after training. After only four days of training, the subjects demonstrated significant differences in the Granger causality analysis (GCA) characteristics of brain regions in the δ , θ , α1 , ß2 , and γ frequency bands of the EEG, as well as significant differences in the GCA of the EEG in the ß1 , ß2 , and γ frequency bands between the two test sessions. The proposed SCTES used a compact and all-in-one form factor to train and evaluate spatial cognition and collect EEG signals and behavioral data simultaneously. The recorded EEG data can be used to quantitatively assess the efficacy of spatial training in patients with spatial cognitive impairments.

Feature Extraction Method of EEG Signals Evaluating Spatial Cognition of Community Elderly With Permutation Conditional Mutual Information Common Space Model.

In order to improve the traditional common space pattern (CSP) algorithm pattern in EEG feature extraction, this study proposes a feature extraction method of EEG signals based on permutation conditional mutual information common space pattern (PCMICSP), which used the sum of the permutation condition mutual information matrices of each lead to replacing the mixed spatial covariance matrix in the traditional CSP algorithm, and its eigenvectors and eigenvalues are used to construct a new spatial filter. Then the spatial features in the different time domains and frequency domains are combined to construct the two-dimensional pixel map, Finally, a convolutional neural network (CNN) is used for binary classification. The EEG signals of 7 community elderly before and after spatial cognitive training in virtual reality (VR) scenes were used as the test data set. The average classification accuracy of the PCMICSP algorithm for pre-test and post-test EEG signals is 98%, which was higher than that of CSP based on CMI (conditional mutual information), CSP based on MI (mutual information), and traditional CSP in the combination of four frequency bands. Compared with the traditional CSP method, PCMICSP can be used as a more effective method to extract the spatial features of EEG signals. Therefore, this paper provides a new approach to solving the strict linear hypothesis of CSP and can be used as a valuable biomarker for the spatial cognitive evaluation of the elderly in the community.

Deep learning PET/CT-based radiomics integrates clinical data: A feasibility study to distinguish between tuberculosis nodules and lung cancer.

BACKGROUND: Radiomic diagnosis models generally consider only a single dimension of information, leading to limitations in their diagnostic accuracy and reliability. The integration of multiple dimensions of information into the deep learning model have the potential to improve its diagnostic capabilities. The purpose of study was to evaluate the performance of deep learning model in distinguishing tuberculosis (TB) nodules and lung cancer (LC) based on deep learning features, radiomic features, and clinical information. METHODS: Positron emission tomography (PET) and computed tomography (CT) image data from 97 patients with LC and 77 patients with TB nodules were collected. One hundred radiomic features were extracted from both PET and CT imaging using the pyradiomics platform, and 2048 deep learning features were obtained through a residual neural network approach. Four models included traditional machine learning model with radiomic features as input (traditional radiomics), a deep learning model with separate input of image features (deep convolutional neural networks [DCNN]), a deep learning model with two inputs of radiomic features and deep learning features (radiomics-DCNN) and a deep learning model with inputs of radiomic features and deep learning features and clinical information (integrated model). The models were evaluated using area under the curve (AUC), sensitivity, accuracy, specificity, and F1-score metrics. RESULTS: The results of the classification of TB nodules and LC showed that the integrated model achieved an AUC of 0.84 (0.82-0.88), sensitivity of 0.85 (0.80-0.88), and specificity of 0.84 (0.83-0.87), performing better than the other models. CONCLUSION: The integrated model was found to be the best classification model in the diagnosis of TB nodules and solid LC.

Prognostic value of 18F-FDG PET/CT-based radiomics combining dosiomics and dose volume histogram for head and neck cancer.

OBJECTIVES: By comparing the prognostic performance of 18F-FDG PET/CT-based radiomics combining dose features [Includes Dosiomics feature and the dose volume histogram (DVH) features] with that of conventional radiomics in head and neck cancer (HNC), multidimensional prognostic models were constructed to investigate the overall survival (OS) in HNC. MATERIALS AND METHODS: A total of 220 cases from four centres based on the Cancer Imaging Archive public dataset were used in this study, 2260 radiomics features and 1116 dosiomics features and 8 DVH features were extracted for each case, and classified into seven different models of PET, CT, Dose, PET+CT, PET+Dose, CT+Dose and PET+CT+Dose. Features were selected by univariate Cox and Spearman correlation coefficients, and the selected features were brought into the least absolute shrinkage and selection operator (LASSO)-Cox model. A nomogram was constructed to visually analyse the prognostic impact of the incorporated dose features. C-index and Kaplan-Meier curves (log-rank analysis) were used to evaluate and compare these models. RESULTS: The cases from the four centres were divided into three different training and validation sets according to the hospitals. The PET+CT+Dose model had C-indexes of 0.873 (95% CI 0.812-0.934), 0.759 (95% CI 0.663-0.855) and 0.835 (95% CI 0.745-0.925) in the validation set respectively, outperforming the rest models overall. The PET+CT+Dose model did well in classifying patients into high- and low-risk groups under all three different sets of experiments (p < 0.05). CONCLUSION: Multidimensional model of radiomics features combining dosiomics features and DVH features showed high prognostic performance for predicting OS in patients with HNC.

Age-related positivity effect of emotional attention: time and frequency domain.

Compared with their younger counterparts, older adults are inclined to allocate more attentional resources to positive over negative materials. This age-related positivity effect has been reported in various experimental paradigms; however, studies have not investigated the attention stage at which it appears or its potential neural mechanism. Thus, we investigated the time and frequency domain dynamics of younger and older adults during emotional attention processes. We obtained electroencephalography oscillation and event-related potential data for 20 older and 20 younger participants while they performed an emotional dot-probe task. We focused our time and frequency domain dynamics analyses on the posterior regions as a key structure for facial emotion perception and the frontal regions as a crucial structure for cognitive control. In the time domain, older adults showed an initial attentional shift to happy-related stimuli, whereas their younger counterparts did not demonstrate emotional modulation, as reflected by the N2pc component. The time-frequency decomposition was analyzed for the N2pc time window. The results showed that compared with younger adults, older adults showed an increased alpha power for happy faces in the right-posterior regions. Moreover, a parallel pattern was seen in frontal theta activity. The current findings highlight how electrocortical activity of the brain might moderate the tendency to prioritize positive information among healthy older adults. The emergence of an age-related positivity effect may be related to frontal cognitive control processing. These findings provide insight into the prevention and treatment of unsuccessful aging, such as late-life depression and anxiety.

Modelling and Image Reconstruction Evaluation of a Scintillation Camera with Silicone Photomultiplier.

Background: Recent research on photon detection has led to the introduction of a silicone photomultiplier (SiPM) that operates at a low voltage and is insensitive to magnetic fields. Objective: This work aims to model a scintillation camera with a SiPM sensor and to evaluate the camera reconstructed images from gamma ray projection data. Material and Methods: The type of study in this research is experimental work and analytical. The scintillation camera, modelled from an SiPM sensor array SL4-30035, coupled with a scintillation material Caesium Iodide doped with Thallium (CsI(Tl)), is used in the experimental part. The performance of the camera was evaluated from reconstructed images by a back-projection technique of a radioactive source Caesium-137 (Cs-137). Results: The experiments conducted with a 1 µCi Cs-137 radioactive source have revealed that the bias voltage (Vbias ) of the SiPM needs to be set to 27.8 V at an operating temperature between 43 °C to 44 °C. The radioactive source has to be placed within a 1 cm distance from the sensor to obtain the optimum projection data. Finally, the back-projection technique for image reconstruction with linear interpolation pre-processing has revealed that the Ram-Lak filter produces a better image contrast ratio compared to others. Conclusion: This research has successfully modelled a scintillation camera with SiPM that was able to reconstruct images with an 86.4% contrast ratio from gamma ray projection data.

Task-State EEG Signal Classification for Spatial Cognitive Evaluation Based on Multiscale High-Density Convolutional Neural Network.

In this study, a multi-scale high-density convolutional neural network (MHCNN) classification method for spatial cognitive ability assessment was proposed, aiming at achieving the binary classification of task-state EEG signals before and after spatial cognitive training. Besides, the multi-dimensional conditional mutual information method was used to extract the frequency band features of the EEG data. And the coupling features under the combination of multi-frequency bands were transformed into multi-spectral images. At the same time, the idea of Densenet was introduced to improve the multi-scale convolutional neural network. Firstly, according to the discreteness of multispectral EEG image features, two-scale convolution kernels were used to calculate and learn useful channel and frequency band feature information in multispectral image data. Secondly, to enhance feature propagation and reduce the number of parameters, the dense network was connected after the multi-scale convolutional network, and the learning rate change function of the stochastic gradient descent algorithm was optimized to objectively evaluate the training effect. The experimental results showed that compared with the classical convolution neural network (CNN) and multi-scale convolution neural network, the proposed MHCNN had better classification performance in the six frequency band combinations with the highest accuracy of 98%: Theta-Alpha2-Gamma, Alpha2-Beta2-Gamma, Beta1-Beta2-Gamma, Theta-Beta2-Gamma, Theta- Alpha1-Gamma, and Alpha1-Alpha2-Gamma. By comparing the classification results of six frequency band combinations, it was found that the combination of the Theta-Beta2-Gamma band had the best classification effect. The MHCNN classification method proposed in this research could be used as an effective biological indicator of spatial cognitive training effect and could be extended to other brain function evaluations.

Multi-dimensional conditional mutual information with application on the EEG signal analysis for spatial cognitive ability evaluation.

This study aims to explore an effective method to evaluate spatial cognitive ability, which can effectively extract and classify the feature of EEG signals collected from subjects participating in the virtual reality (VR) environment; and evaluate the training effect objectively and quantitatively to ensure the objectivity and accuracy of spatial cognition evaluation, according to the classification results. Therefore, a multi-dimensional conditional mutual information (MCMI) method is proposed, which could calculate the coupling strength of two channels considering the influence of other channels. The coupled characteristics of the multi-frequency combination were transformed into multi-spectral images, and the image data were classified employing the convolutional neural networks (CNN) model. The experimental results showed that the multi-spectral image transform features based on MCMI are better in classification than other methods, and among the classification results of six band combinations, the best classification accuracy of Beta1-Beta2-Gamma combination is 98.3%. The MCMI characteristics on the Beta1-Beta2-Gamma band combination can be a biological marker for the evaluation of spatial cognition. The proposed feature extraction method based on MCMI provides a new perspective for spatial cognitive ability assessment and analysis.

Effect of Cognitive Control on Age-Related Positivity Effects in Attentional Processing - Evidence From an Event-Related Brain Potential Study.

Studies investigating age-related positivity effects during facial emotion processing have yielded contradictory results. The present study aimed to elucidate the mechanisms of cognitive control during attentional processing of emotional faces among older adults. We used go/no-go detection tasks combined with event-related potentials and source localization to examine the effects of response inhibition on age-related positivity effects. Data were obtained from 23 older and 23 younger healthy participants. Behavioral results showed that the discriminability index (d') of older adults on fear trials was significantly greater than that of younger adults [t(44)=2.37, p=0.024, Cohen's d=0.70], whereas an opposite pattern was found in happy trials [t(44)=2.56, p=0.014, Cohen's d=0.75]. The electroencephalography results on the amplitude of the N170 at the left electrode positions showed that the fear-neutral face pairs were larger than the happy-neutral ones for the younger adults [t(22)=2.32, p=0.030, Cohen's d=0.48]; the older group's right hemisphere presented similar tendency, although the results were not statistically significant [t(22)=1.97, p=0.061, Cohen's d=0.41]. Further, the brain activity of the two hemispheres in older adults showed asymmetrical decrement. Our study demonstrated that the age-related "positivity effect" was not observed owing to the depletion of available cognitive resources at the early attentional stage. Moreover, bilateral activation of the two hemispheres may be important signals of normal aging.

Coupling feature extraction method of resting state EEG Signals from amnestic mild cognitive impairment with type 2 diabetes mellitus based on weight permutation conditional mutual information.

This study aimed to find a good coupling feature extraction method to effectively analyze resting state EEG signals (rsEEG) of amnestic mild cognitive impairment(aMCI) with type 2 diabetes mellitus(T2DM) and normal control (NC) with T2DM. A method of EEG signal coupling feature extraction based on weight permutation conditional mutual information (WPCMI) was proposed in this research. With the WPCMI method, coupling feature strength of two time series in Alpha1, Alpha2, Beta1, Beta2 and Gamma bands for aMCI with T2DM and NC with T2DM could be extracted respectively. Then selected three frequency bands coupling feature matrix with the help of multi-spectral image transformation method to map it as spectral image characteristics. And finally classified these characteristics through the convolution neural network method(CNN). For aMCI with T2DM and NC with T2DM, the highest classification accuracy of 96%, 95%, 95% could be achieved respectively in the combination of three frequency bands (Alpha1, Alpha2, Gamma), (Beta1, Beta2 and Gamma) and (Alpha2, Beta1, Beta2). This WPCMI method highlighted the coupling dynamic characteristics of EEG signals, and its classification performance was better than all previous methods in aMCI with T2DM diagnosis field. WPCMI method could be used as an effective biomarker to distinguish EEG signals of aMCI with T2DM and NC with T2DM. The coupling feature extraction method used in this paper provided a new perspective for the EEG analysis of aMCI with T2DM.

Classification of ERP signal from amnestic mild cognitive impairment with type 2 diabetes mellitus using single-scale multi-input convolution neural network.

BACKGROUND: The application of deep learning models to electroencephalogram (EEG) signal classification has recently become a popular research topic. Several deep learning models have been proposed to classify EEG signals in patients with various neurological diseases. However, no effective deep learning model for event-related potential (ERP) signal classification is yet available for amnestic mild cognitive impairment (aMCI) with type 2 diabetes mellitus (T2DM). METHOD: This study proposed a single-scale multi-input convolutional neural network (SSMICNN) method to classify ERP signals between aMCI patients with T2DM and the control group. Firstly, the 18-electrode ERP signal on alpha, beta, and theta frequency bands was extracted by using the fast Fourier transform, and then the mean, sum of squares, and absolute value feature of each frequency band were calculated. Finally, these three features are converted into multispectral images respectively and used as the input of the SSMICNN network to realize the classification task. RESULTS: The results show that the SSMICNN can fuse MSI formed by different features, SSMICNN enriches the feature quantity of the neural network input layer and has excellent robustness, and the errors of SSMICNN can be simultaneously transmitted to the three convolution channels in the back-propagation phase. Comparison with Existing Method(s): SSMICNN could more effectively identify ERP signals from aMCI with T2DM from the control group compared to existing classification methods, including convolution neural network, support vector machine, and logistic regression. CONCLUSIONS: The combination of SSMICNN and MSI can be used as an effective biological marker to distinguish aMCI patients with T2DM from the control group.

Edible Bird's Nest Attenuates Menopause-Related Bone Degeneration in Rats via Increaing Bone Estrogen-Receptor Expression.

OBJECTIVE: To investigate the mechanistic basis for the attenuation of bone degeneration by edible bird's nest (EBN) in ovariectomized rats. METHODS: Forty-two female Sprage-Dawley rats were randomized into 7 groups (6 in each group). The ovariectomized (OVX) and OVX + 6%, 3%, and 1.5% EBN and OVX +estrogen groups were given standard rat chow alone, standard rat chow +6%, 3%, and 1.5% EBN, or standard rat chow +estrogen therapy (0.2mg/kg per day), respectively. The sham-operation group was surgically opened without removing the ovaries. The control group did not have any surgical intervention. After 12 weeks of intervention, blood samples were taken for serum estrogen, osteocalcin, and osteoprotegerin, as well as the measurement of magnesium, calcium abd zinc concentrations. While femurs were removed from the surrounding muscles to measure bone mass density using the X-ray edge detection technique, then collected for histology and estrogen receptor (ER) immunohistochemistry. RESULTS: Ovariectomy altered serum estrogen levels resulting in increased food intake and weight gain, while estrogen and EBN supplementation attenuated these changes. Ovariectomy also reduced bone ER expression and density, and the production of osteopcalcin and osteorotegerin, which are important pro-osteoplastic hormones that promote bone mineraliztion and density. Conversely, estrogen and EBN increased serum estrogen levels leading to increased bone ER expression, pro-osteoplastic hormone production and bone density (all P<0.05). CONCLUSION: EBN could be used as a safe alternative to hormone replacement therapys for managing menopausal complications like bone degeneration.

Multi-view secondary input collaborative deep learning for lung nodule 3D segmentation.

BACKGROUND: Convolutional neural networks (CNNs) have been extensively applied to two-dimensional (2D) medical image segmentation, yielding excellent performance. However, their application to three-dimensional (3D) nodule segmentation remains a challenge. METHODS: In this study, we propose a multi-view secondary input residual (MV-SIR) convolutional neural network model for 3D lung nodule segmentation using the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) dataset of chest computed tomography (CT) images. Lung nodule cubes are prepared from the sample CT images. Further, from the axial, coronal, and sagittal perspectives, multi-view patches are generated with randomly selected voxels in the lung nodule cubes as centers. Our model consists of six submodels, which enable learning of 3D lung nodules sliced into three views of features; each submodel extracts voxel heterogeneity and shape heterogeneity features. We convert the segmentation of 3D lung nodules into voxel classification by inputting the multi-view patches into the model and determine whether the voxel points belong to the nodule. The structure of the secondary input residual submodel comprises a residual block followed by a secondary input module. We integrate the six submodels to classify whether voxel points belong to nodules, and then reconstruct the segmentation image. RESULTS: The results of tests conducted using our model and comparison with other existing CNN models indicate that the MV-SIR model achieves excellent results in the 3D segmentation of pulmonary nodules, with a Dice coefficient of 0.926 and an average surface distance of 0.072. CONCLUSION: our MV-SIR model can accurately perform 3D segmentation of lung nodules with the same segmentation accuracy as the U-net model.