MRI Imaging Data Research Articles

Locus coeruleus signal intensity and emotion regulation in agitation in Alzheimer’s disease

Abstract Hyperphosphorylated tau accumulation is seen in the noradrenergic locus coeruleus from the earliest stages of Alzheimer’s disease onwards and has been associated with symptoms of agitation. It is hypothesized that compensatory locus coeruleus-noradrenaline system overactivity and impaired emotion regulation could underlie agitation propensity, but to our knowledge this has not previously been investigated. A better understanding of the neurobiological underpinnings of agitation would help the development of targeted prevention and treatment strategies. Using a sample of individuals with amnestic mild cognitive impairment and probable mild Alzheimer’s disease dementia from the German Center for Neurodegenerative Diseases (DZNE)-Longitudinal Cognitive Impairment and Dementia (DELCODE) study cohort (N=309, aged 67-96 years, 51% female), we assessed cross-sectional relationships between a latent factor representing the functional integrity of an affect-related executive regulation network and agitation point prevalence and severity scores. In a subsample of individuals with locus coeruleus MRI imaging data (N=37, aged 68-93 years, 49% female), we also investigated preliminary associations between locus coeruleus MRI contrast ratios (a measure of structural integrity, whole or divided into rostral, middle, and caudal thirds) and individual affect-related regulation network factor scores and agitation measures. Regression models controlled for effects of age and clinical disease severity and, for models including resting state functional MRI connectivity variables, grey matter volume and education years. Agitation point prevalence showed a positive relationship with a latent factor representing the functional integrity (and a negative relationship with a corresponding structural measure) of the affect-related executive regulation network. Locus coeruleus MRI contrast ratios were positively associated with agitation severity (but only for the rostral third, in N=13) and negatively associated with the functional affect-related executive regulation latent factor scores. Resting-state functional connectivity between a medial prefrontal cortex region and left amygdala was related to locus coeruleus MRI contrast ratios. These findings implicate the involvement of locus coeruleus integrity and emotion dysregulation in agitation in Alzheimer’s disease and support the presence of potential compensatory processes. At the neural level, there may be a dissociation between mechanisms underlying agitation risk per se and symptom severity. Further studies are needed to replicate and extend these findings, incorporating longitudinal designs, measures of autonomic function and non-linear modeling approaches to explore potential causal and context-dependent relationships across Alzheimer’s disease stages.

Unsupervised definition of two clinical subtypes of Essential tremor and the underlying brain topology

BackgroundEssential tremor (ET) is one of the most prevalent neurological diseases varying considerably in clinical manifestations and prognosis, which indicates the existence of subtypes. Identifying ET subtypes is crucial for explaining clinical heterogeneity. This study aimed to identify ET subtypes using unsupervised clustering analysis based on clinical manifestations and explore underlying brain topology within both functional and structural networks. MethodsWe recruited 103 ET patients and 43 healthy control subjects. K-means clustering analysis was performed to identify ET subtypes based on age of onset, motor and non-motor symptoms. Functional MRI and diffusion tensor imaging data were used to construct functional and structural networks. Global attributes (clustering coefficient, characteristic path length, global efficiency, and local efficiency) and nodal attributes (nodal clustering coefficient, nodal efficiency, and nodal degree centrality) were calculated for topological analysis. ResultsWe identified two subtypes: Subtype 1 (earlier age of onset – without nonmotor symptoms subtype) and Subtype 2 (later age of onset – with nonmotor symptoms subtype). Decreased clustering coefficient and global efficiency, increased characteristic path length were observed in Subtype 2 compared to NC, while only decreased global efficiency was observed in Subtype 1. More widespread brain regions with decreased nodal clustering coefficient were specifically observed in Subtype 2. ConclusionWe identified two ET subtypes based on comprehensive clinical information and revealed that Subtype 2 may be a more malignant subtype. Our study firstly unsupervisedly identifies the clinical heterogeneity of ET and provides neuroimaging evidence for better understanding the underlying disease biology.

Safety and aesthetic evaluation of autologous dermal fat grafts for lateral Skull base defect reconstruction

ObjectiveThe selection of appropriate materials and techniques for reconstructing skull base defects is crucial for restoring function, minimizing complications, and enhancing aesthetics. Dermal fat grafts (DFGs) have been established in many surgeries for contour correction. However, there is little literature describing the application of free dermal fat grafts for reconstruction of lateral skull base surgery defects. Therefore, the aim of this study was to evaluate the clinical effect of using free dermal fat grafts in the reconstruction of lateral skull base defects.MethodsIn this study, 32 patients who underwent primary autologous free dermal fat graft reconstruction for lateral skull base defects were enrolled. Postoperative complications both in the donor and recipient area were evaluated. Three-dimensional (3D) reconstruction techniques were applied to visualize the fat graft shape and volume variations for the assessment of postoperative restoring function. A telephone questionnaire was used for the assessment of postoperative facial aesthetics and patient satisfaction.ResultsA total of 32 patients who met the inclusion criteria were included in the study, showing satisfying results in safety and aesthetics. Participants’ ages averaged at 42.03 years, with the youngest being 18 and the oldest at 75 years old. No patient had postoperative complications of abdominal wounds in the donor area, only two patients had postoperative infections and one patient had a postoperative effusion in the recipient area. The volume of the grafts ranged from a minimum of 24.4 cm³ to a maximum of 131 cm³, with the highest recorded resorption volume being 33.63 cm³. The average resorption rate of the dermal fat graft was 24.36%, ranging from a minimum of 11% to a maximum of 43%. 3D reconstruction based on MRI imaging data indicated that DFGs have a lower resorption rate than traditional free fat grafts. The telephone questionnaire revealed high patient satisfaction with facial aesthetics, with the majority rating their outcomes positively.ConclusionsDermal fat grafts have fewer complication and can provide better resistance to infection and radiation aspect. Based on 3D reconstruction techniques via MRI imaging data, we found that dermal fat grafts have lower resorption rate than traditional free fat grafts, which can be a suitable material for reconstruction of lateral skull base defects.

Magnetic resonance imaging-based radiomics for predicting infiltration levels of CD68+ tumor-associated macrophages in glioblastomas.

Tumor-associated macrophages (TAMs) are important biomarkers of tumor invasion and prognosis in patients with glioblastoma. We combined the imaging and radiomics features of preoperative MRI to predict CD68+ macrophage infiltration. Clinical, MRI image, and pathology data of 188 patients with glioblastoma were analyzed. Overall, 143 patients were included in the training (n = 101) and validation (n = 42) sets, whereas 45patients were included in an independent test set. The optimal cut-off value (14.8%) was based on the minimum p-value formed by the Kaplan-Meier survival analysis and log-rank tests which divided patients into groups with high CD68+ TAMs(≥ 14.8%) and low CD68+ TAMs (< 14.8%). Regions of interest and radiomics features extraction were based on contrast-enhanced T1-weighted images (CE-T1WI) and T2WI. Multi-parameter stepwise regression was used to create the clinical, radiomics, and combined models, each evaluated using the receiver operating characteristic curve. Decision curve analysis was used to assess the clinical applicability of the nomogram. Aclinical model based on the minimum apparent diffusion coefficient (ADCmin) revealed an area under the curve (AUC) of 0.768, 0.764, and 0.624 for the training set, validation set, and test set, respectively. The 2D radiomics model, based on two features, revealed an AUC of 0.783, 0.724, and 0.789 for the training, validation, and test sets, respectively. The 3D radiomics model, based on three features, revealed AUCs of 0.823, 0.811, and 0.787 for the training, validation, and test sets, respectively. The combined model, with ADCmin and radiomics features, showed the best performance, with AUCs of 0.865, 0.822, and 0.776 for the training, validation, and test sets, respectively. The calibration curve of the combined model nomogram showed good agreement between the estimated and actual probabilities. The combined model constructed using ADCmin, aquantitative imaging parameter, combined with five key radiomics features can be used to evaluate the extent of CD68+ macrophages before surgery.

A Multi-Institutional Study of Magnetic Resonance/Ultrasound Fusion-Guided Nanoparticle-Directed Focal Therapy for Prostate Ablation.

Focal therapy aims to provide a durable oncologic treatment option for men with prostate cancer (PCa), while preserving their quality of life. Most focal therapy modalities rely on the direct tissue effect, resulting in a possible nontargeted approach to ablation. Here, we report the results of the first human feasibility trial utilizing nanoparticle-directed focal photothermal ablation for PCa. A prospective, open-label, single-arm, multicenter study of men with localized PCa in Gleason Grade Group 1 to 3 was conducted. Men received a single infusion of gold nanoparticles (AuroShells), followed by magnetic resonance (MR)/ultrasound (US) fusion-guided laser excitation of the target tissue to induce photothermal ablation. MRI was used to assess the effectiveness of prostate tissue ablation at 48 to 96 hours, 3 months, and 12 months post treatment. At 3 months, a targeted fusion biopsy of the lesion(s) was conducted. At 12 months, a targeted fusion biopsy and standard templated biopsy were performed. Treatment success was determined based on a negative MR/US fusion biopsy outcome within the treated area. Forty-six men were enrolled in the study, and 44 men with 45 lesions completed nanoparticle infusion and laser treatment. Baseline mean PSA levels were 9.5 ng/mL, with a statistically significant decreaseof5.9 ng/mL at 3 months and 4.7 ng/mL at 12 months (P< .0001). The oncologic success rates at 3 and 12 months resulted in 29 (66%) and 32 (73%) of 44 patients, respectively, being successfully treated, confirmed with negative MR/US fusion biopsies within the ablation zone. Among Gleason Grade Group, maximum lesion diameter on MRI, prostate volume, and Prostate Imaging Reporting and Data System scoring, the maximum lesion diameter was significantly associated with the odds of treatment failure at 12 months (P = .046). Nanoparticle-directed focal laser ablation of neoplastic prostate tissue resulted in 73% of patients with successful treatment at 12 months post treatment, confirmed by negative MR/US fusion biopsy of the treated lesion and a systematic biopsy. 02680535.

Alzheimer's disease stage recognition from MRI and PET imaging data using Pareto-optimal quantum dynamic optimization

The threat posed by Alzheimer's disease (AD) to human health has grown significantly. However, the precise diagnosis and classification of AD stages remain a challenge. Neuroimaging methods such as structural magnetic resonance imaging (sMRI) and fluorodeoxyglucose positron emission tomography (FDG-PET) have been used to diagnose and categorize AD. However, feature selection approaches that are frequently used to extract additional data from multimodal imaging are prone to errors. This paper suggests using a static pulse-coupled neural network and a Laplacian pyramid to combine sMRI and FDG-PET data. After that, the fused images are used to train the Mobile Vision Transformer (MViT), optimized with Pareto-Optimal Quantum Dynamic Optimization for Neural Architecture Search, while the fused images are augmented to avoid overfitting and then classify unfused MRI and FDG-PET images obtained from the AD Neuroimaging Initiative (ADNI) and Open Access Series of Imaging Studies (OASIS) datasets into various stages of AD. The architectural hyperparameters of MViT are optimized using Quantum Dynamic Optimization, which ensures a Pareto-optimal solution. The Peak Signal-to-Noise Ratio (PSNR), the Mean Squared Error (MSE), and the Structured Similarity Indexing Method (SSIM) are used to measure the quality of the fused image. We found that the fused image was consistent in all metrics, having 0.64 SIMM, 35.60 PSNR, and 0.21 MSE for the FDG-PET image. In the classification of AD vs. cognitive normal (CN), AD vs. mild cognitive impairment (MCI), and CN vs. MCI, the precision of the proposed method is 94.73%, 92.98% and 89.36%, respectively. The sensitivity is 90. 70%, 90. 70%, and 90. 91% while the specificity is 100%, 100%, and 85. 71%, respectively, in the ADNI MRI test data.

Computer-aided diagnosis of Alzheimer’s disease and neurocognitive disorders with multimodal Bi-Vision Transformer (BiViT)

Cognitive disorders affect various cognitive functions that can have a substantial impact on individual’s daily life. Alzheimer’s disease (AD) is one of such well-known cognitive disorders. Early detection and treatment of cognitive diseases using artificial intelligence can help contain them. However, the complex spatial relationships and long-range dependencies found in medical imaging data present challenges in achieving the objective. Moreover, for a few years, the application of transformers in imaging has emerged as a promising area of research. A reason can be transformer’s impressive capabilities of tackling spatial relationships and long-range dependency challenges in two ways, i.e., (1) using their self-attention mechanism to generate comprehensive features, and (2) capture complex patterns by incorporating global context and long-range dependencies. In this work, a Bi-Vision Transformer (BiViT) architecture is proposed for classifying different stages of AD, and multiple types of cognitive disorders from 2-dimensional MRI imaging data. More specifically, the transformer is composed of two novel modules, namely Mutual Latent Fusion (MLF) and Parallel Coupled Encoding Strategy (PCES), for effective feature learning. Two different datasets have been used to evaluate the performance of proposed BiViT-based architecture. The first dataset contain several classes such as mild or moderate demented stages of the AD. The other dataset is composed of samples from patients with AD and different cognitive disorders such as mild, early, or moderate impairments. For comprehensive comparison, a multiple transfer learning algorithm and a deep autoencoder have been each trained on both datasets. The results show that the proposed BiViT-based model achieves an accuracy of 96.38% on the AD dataset. However, when applied to cognitive disease data, the accuracy slightly decreases below 96% which can be resulted due to smaller amount of data and imbalance in data distribution. Nevertheless, given the results, it can be hypothesized that the proposed algorithm can perform better if the imbalanced distribution and limited availability problems in data can be addressed.Graphical abstract

MRI manifestations of 40 cases with the hepatic epithelioid hemangioendothelioma classification based on the morphology and size

Objective: To explore the MRI characteristics of the hepatic epithelioid hemangioendothelioma (HEHE) classification according to morphology and size. Methods: The clinical, pathological, and MRI imaging data of 40 cases with HEHE confirmed pathologically from December 2009 to September 2021 were retrospectively analyzed. A paired sample t-test was used for comparison between the two groups. Results: There were 40 cases (5 solitary, 24 multifocal, 9 local fusion, and 2 diffuse fusion) and 214 lesions (163 nodules, 31 masses, and 20 fusion foci). The most common features of lesions were subcapsular growth and capsular depression. The signal intensity of lesions ≤1cm was usually uniform with whole or ring enhancement. Nodules and mass-like lesions ≥1cm on a T1-weighted image had slightly reduced signal intensity or manifested as a halo sign. Target signs on a T2-weighted image were characterized by: target or centripetal enhancement; fusion-type lesions; irregular growth and hepatic capsular retraction, with ring or target-like enhancement in the early stage of fusion and patchy irregular enhancement in the late stage; blood vessels traversing or accompanied by malformed blood vessels; focal bleeding; an increasing proportion of extrahepatic metastases and abnormal liver function with the type of classified manifestation; primarily portal vein branches traversing; and reduced overall intralesional bleeding rate (17%). Lollipop signs were presented in 19 cases, with a high expression rate in mass-type lesions (42%). The fusion lesions were expressed, but the morphological manifestation was atypical. The diffusion-weighted imaging mostly showed high signal or target-like high signal. An average apparent diffusion coefficient of lesions was (1.56±0.36) ×10(-3)mm(2)/s, which was statistically significantly different compared with that of adjacent normal liver parenchyma (t=8.28, P<0.001). Conclusion: The MRI manifestations for the HEHE classification are closely related to the morphology and size of the lesions and have certain differences and characteristics that are helpful for the diagnosis of the disease when combined with clinical and laboratory examinations.

Association of Loneliness with Functional Connectivity MRI, Amyloid-β PET, and Tau PET Neuroimaging Markers of Vulnerability for Alzheimer's Disease.

Loneliness has been declared an "epidemic" associated with negative physical, mental, and cognitive health outcomes such as increased dementia risk. Less is known about the relationship between loneliness and advanced neuroimaging correlates of Alzheimer's disease (AD). To assess whether loneliness was associated with advanced neuroimaging markers of AD using neuroimaging data from Framingham Heart Study (FHS) participants without dementia. In this cross-sectional observational analysis, we used functional connectivity MRI (fcMRI), amyloid-β (Aβ) PET, and tau PET imaging data collected between 2016 and 2019 on eligible FHS cohort participants. Loneliness was defined as feeling lonely at least one day in the past week. The primary fcMRI marker was Default Mode Network intra-network connectivity. The primary PET imaging markers were Aβ deposition in precuneal and FLR (frontal, lateral parietal and lateral temporal, retrosplenial) regions, and tau deposition in the amygdala, entorhinal, and rhinal regions. Of 381 participants (mean age 58 [SD 10]) who met inclusion criteria for fcMRI analysis, 5% were classified as lonely (17/381). No association was observed between loneliness status and network changes. Of 424 participants (mean age 58 [SD = 10]) meeting inclusion criteria for PET analyses, 5% (21/424) were lonely; no associations were observed between loneliness and either Aβ or tau deposition in primary regions of interest. In this cross-sectional study, there were no observable associations between loneliness and select fcMRI, Aβ PET, and tau PET neuroimaging markers of AD risk. These findings merit further investigation in prospective studies of community-based cohorts.

Prediction of CCL2 in breast cancer based on enhanced MRI radiomics model with integrated bioinformatics analysis and machine learning.

e12509 Background: Breast cancer is the most common malignant tumor in women. According to the 2023 Cancer Statistics Report, breast cancer has surpassed lung cancer to become the most common malignant tumor. Its incidence and mortality both rank first among female malignant tumors. This study proposes non-invasive prediction of CCL2 mRNA expression in breast cancer tissue through enhanced MRI radiomics, while integrating bioinformatics analysis to explore the underlying molecular mechanisms and their association with the immune microenvironment. Methods: Excluding criteria were applied to select primary solid tumor samples with RNA-seq data (n = 840) from the TCGA-BRCA database. Enhanced MRI imaging data (n = 98) intersecting with the TCGA-BRCA data were obtained from TCIA-BRCA. The imaging data were randomly divided into training (n = 70) and testing sets (n = 28) in a 7:3 ratio. A feature subset was selected, and a model was constructed using the support vector machine (SVM) algorithm in the training set. The predictive performance of the model was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and the Hosmer-Lemeshow goodness-of-fit test. The calibration of the radiomics prediction model was assessed using the Brier score, and the clinical utility of the radiomics prediction model was demonstrated by drawing decision curves (DCA). External validation of the model was performed using a validation set. The radiomics score (RS) was calculated using the LR radiomics model and categorized as a binary variable (Low/High RS). Subsequently, KEGG pathway, immune-related genes, and gene mutation analysis were conducted based on the CCL2 high and low expression levels. Results: A total of 1810 radiomic features were obtained with ICC values ≥ 0.75. Subsequently, the mRMR (Maximum relevance, minimum redundancy) method, and the optimal 7 feature subsets were further identified using the RFE (Recursive feature elimination) algorithm. The SVM model demonstrated favorable predictive performance, with an AUC value of 0.822 for the training set and 0.779 for the validation set, as indicated by the ROC curve. Calibration curve and Hosmer-Lemeshow goodness-of-fit test revealed good consistency between the radiomic predictive model for the probability of high gene expression and the actual values (P &gt; 0.05). Additionally, the decision curve analysis (DCA) demonstrated high clinical utility of the model. The TNF-α signaling pathway was found to influence the occurrence and development of breast cancer. Furthermore, the degree of Plasma Cell infiltration was observed to be highest in breast cancer. Conclusions: The CCL2 model constructed using a combination of machine learning and radiomic features, can non-invasively predict the prognosis of breast cancer treatment and exhibits good generalizability.

MRI-based radiomics feature combined with tumor markers to predict TN staging of rectal cancer.

The aim of this study is to evaluate the predictive ability of MRI-based radiomics combined with tumor markers for TN staging in patients with rectal cancer and to develop a prediction model for TN staging. A total of 190 patients with rectal adenocarcinoma who underwent total mesorectal excision at the First Affiliated Hospital of the Air Force Medical University between January 2016 and December 2020 were included in the study. An additional 54 patients from a prospective validation cohort were included between August 2022 and August 2023. Preoperative tumor markers and MRI imaging data were collected from all enrolled patients. The 190 patients were divided into a training cohort (n = 133) and a validation cohort (n = 57). Radiomics features were extracted by outlining the region of interest (ROI) on T2WI sequence images. Feature selection and radiomics score (Rad-score) construction were performed using least absolute shrinkage and selection operator regression analysis (LASSO). The postoperative pathology TNM stage was used to differentiate locally advanced rectal cancer (T3/4 or N1/2) from locally early rectal cancer (T1/2, N0). Logistic regression was used to construct separate prediction models for T stage and N stage. The models' predictive performance was evaluated using DCA curves and calibration curves. The T staging model showed that Rad-score, based on 8 radiomics features, was an independent predictor of T staging. When combined with CEA, tumor diameter, mesoretal fascia (MRF), and extramural venous invasion (EMVI), it effectively differentiated between T1/2 and T3/4 stage rectal cancers in the training cohort (AUC 0.87 [95% CI: 0.81-0.93]). The N-staging model found that Rad-score, based on 10 radiomics features, was an independent predictor of N-staging. When combined with CA19.9, degree of differentiation, and EMVI, it effectively differentiated between N0 and N1/2 stage rectal cancers. The training cohort had an AUC of 0.84 (95% CI: 0.77-0.91). The calibration curves demonstrated good precision between the predicted and actual results. The DCA curves indicated that both sets of predictive models could provide net clinical benefits for diagnosis. MRI-based radiomics features are independent predictors of T staging and N staging. When combined with tumor markers, they have good predictive efficacy for TN staging of rectal cancer.

Characterizing the distinct imaging phenotypes, clinical behavior, and genetic vulnerability of brain maturational subtypes in mood disorders.

Mood disorders are characterized by great heterogeneity in clinical manifestation. Uncovering such heterogeneity using neuroimaging-based individual biomarkers, clinical behaviors, and genetic risks, might contribute to elucidating the etiology of these diseases and support precision medicine. We recruited 174 drug-naïve and drug-free patients with major depressive disorder and bipolar disorder, as well as 404 healthy controls. T1 MRI imaging data, clinical symptoms, and neurocognitive assessments, and genetics were obtained and analyzed. We applied regional gray matter volumes (GMV) and quantile normative modeling to create maturation curves, and then calculated individual deviations to identify subtypes within the patients using hierarchical clustering. We compared the between-subtype differences in GMV deviations, clinical behaviors, cell-specific transcriptomic associations, and polygenic risk scores. We also validated the GMV deviations based subtyping analysis in a replication cohort. Two subtypes emerged: subtype 1, characterized by increased GMV deviations in the frontal cortex, cognitive impairment, a higher genetic risk for Alzheimer's disease, and transcriptionally associated with Alzheimer's disease pathways, oligodendrocytes, and endothelial cells; and subtype 2, displaying globally decreased GMV deviations, more severe depressive symptoms, increased genetic vulnerability to major depressive disorder and transcriptionally related to microglia and inhibitory neurons. The distinct patterns of GMV deviations in the frontal, cingulate, and primary motor cortices between subtypes were shown to be replicable. Our current results provide vital links between MRI-derived phenotypes, spatial transcriptome, genetic vulnerability, and clinical manifestation, and uncover the heterogeneity of mood disorders in biological and behavioral terms.

HAMMF: Hierarchical attention-based multi-task and multi-modal fusion model for computer-aided diagnosis of Alzheimer’s disease

Alzheimer’s disease (AD) is a progressive neurodegenerative condition, and early intervention can help slow its progression. However, integrating multi-dimensional information and deep convolutional networks increases the model parameters, affecting diagnosis accuracy and efficiency and hindering clinical diagnostic model deployment. Multi-modal neuroimaging can offer more precise diagnostic results, while multi-task modeling of classification and regression tasks can enhance the performance and stability of AD diagnosis. This study proposes a Hierarchical Attention-based Multi-task Multi-modal Fusion model (HAMMF) that leverages multi-modal neuroimaging data to concurrently learn AD classification tasks, cognitive score regression, and age regression tasks using attention-based techniques. Firstly, we preprocess MRI and PET image data to obtain two modal data, each containing distinct information. Next, we incorporate a novel Contextual Hierarchical Attention Module (CHAM) to aggregate multi-modal features. This module employs channel and spatial attention to extract fine-grained pathological features from unimodal image data across various dimensions. Using these attention mechanisms, the Transformer can effectively capture correlated features of multi-modal inputs. Lastly, we adopt multi-task learning in our model to investigate the influence of different variables on diagnosis, with a primary classification task and a secondary regression task for optimal multi-task prediction performance. Our experiments utilized MRI and PET images from 720 subjects in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. The results show that our proposed model achieves an overall accuracy of 93.15% for AD/NC recognition, and the visualization results demonstrate its strong pathological feature recognition performance.

AN EVALUATION OF THE POWER SUPPORT INTERNET INFRASTRUCTURE OF MAKASSAR CITY IN TELEMEDICINE FRAME

AN EVALUATION OF THE POWER SUPPORT INTERNET INFRASTRUCTURE OF MAKASSAR CITY IN TELEMEDICINE FRAME

Image-based biomarkers for engineering neuroblastoma patient-specific computational models

AbstractChildhood cancer is a devastating disease that requires continued research and improved treatment options to increase survival rates and quality of life for those affected. The response to cancer treatment can vary significantly among patients, highlighting the need for a deeper understanding of the underlying mechanisms involved in tumour growth and recovery to improve diagnostic and treatment strategies. Patient-specific models have emerged as a promising alternative to tackle the challenges in tumour mechanics through individualised simulation. In this study, we present a methodology to develop subject-specific tumour models, which incorporate the initial distribution of cell density, tumour vasculature, and tumour geometry obtained from clinical MRI imaging data. Tumour mechanics is simulated through the Finite Element method, coupling the dynamics of tumour growth and remodelling and the mechano-transport of oxygen and chemotherapy. These models enable a new application of tumour mechanics, namely predicting changes in tumour size and shape resulting from chemotherapeutic interventions for individual patients. Although the specific context of application in this work is neuroblastoma, the proposed methodologies can be extended to other solid tumours. Given the difficulty for treating paediatric solid tumours like neuroblastoma, this work includes two patients with different prognosis, who received chemotherapy treatment. The results obtained from the simulation are compared with the actual tumour size and shape from patients. Overall, the simulations provided clinically useful information to evaluate the effectiveness of the chemotherapy treatment in each case. These results suggest that the biomechanical model could be a valuable tool for personalised medicine in solid tumours.

Multi-modal Neuroimaging Phenotyping of Mnemonic Anosognosia in the Aging Brain

BackgroundUnawareness is a behavioral condition characterized by a lack of self-awareness of objective memory decline. In the context of Alzheimer’s Disease (AD), unawareness may develop in predementia stages and contributes to disease severity and progression. Here, we use in-vivo multi-modal neuroimaging to profile the brain phenotype of individuals presenting altered self-awareness of memory during aging.MethodsAmyloid- and tau-PET (N = 335) and resting-state functional MRI (N = 713) imaging data of individuals from the Anti-Amyloid Treatment in Asymptomatic Alzheimer’s Disease (A4)/Longitudinal Evaluation of Amyloid Risk and Neurodegeneration (LEARN) Study were used in this research. We applied whole-brain voxel-wise and region-of-interest analyses to characterize the cortical intersections of tau, amyloid, and functional connectivity networks underlying unawareness in the aging brain compared to aware, complainer and control groups.ResultsIndividuals with unawareness present elevated amyloid and tau burden in midline core regions of the default mode network compared to aware, complainer or control individuals. Unawareness is characterized by an altered network connectivity pattern featuring hyperconnectivity in the medial anterior prefrontal cortex and posterior occipito-parietal regions co-locating with amyloid and tau deposition.ConclusionsUnawareness is an early behavioral biomarker of AD pathology. Failure of the self-referential system in unawareness of memory decline can be linked to amyloid and tau burden, along with functional network connectivity disruptions, in several medial frontal and parieto-occipital areas of the human brain.

A High-Performance Method Based on Features Fusion of EEG Brain Signal and MRI-Imaging Data for Epilepsy Classification

Abstract A 1-dimensional (1D) and 2-dimensional (2D) biomedical signal analysis based on the Discrete Cosine Transform (DCT) feature extraction method was performed to diagnose epilepsy disorders with high accuracy. For this purpose, Electroencephalogram (EEG) data were used for 1D signal analysis and Magnetic Resonance Imaging (MRI) data were used for 2D signal analysis. The feature vectors were obtained by applying 1D DCT together with statistical methods such as mean, variance, standard deviation, kurtosis, and skewness for EEG data and by applying 2D DCT together with the statistical method of mean for MRI data. The most useful features were selected by applying Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Forward Selection and Backward Selection methods to the obtained feature vectors. Using EEG stand-alone features, MRI stand-alone features and EEG-MRI fused features, the classification of healthy and epileptic subjects was performed in the form of two clusters. The result of epilepsy classification in this work is 96% success of 1D EEG data by using the features selected by the PCA method, 94% success of 2D MRI data using the selected features by applying the Forward Method, 100% classification accuracy of 1D EEG and 2D MRI datasets by LDA method using the obtained fused features . The article shows that the fused features of EEG-MRI can be used very effectively for the diagnosis of epilepsy.

Preoperative evaluation of microvascular invasion in hepatocellular carcinoma with a radiological feature-based nomogram: a bi-centre study

PurposeTo develop a nomogram for preoperative assessment of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) based on the radiological features of enhanced CT and to verify two imaging techniques (CT and MRI) in an external centre.MethodA total of 346 patients were retrospectively included (training, n = 185, CT images; external testing 1, n = 90, CT images; external testing 2, n = 71, MRI images), including 229 MVI-negative patients and 117 MVI-positive patients. The radiological features and clinical information of enhanced CT images were analysed, and the independent variables associated with MVI in HCC were determined by logistic regression analysis. Then, a nomogram prediction model was constructed. External validation was performed on CT (n = 90) and MRI (n = 71) images from another centre.ResultsAmong the 23 radiological and clinical features, size, arterial peritumoral enhancement (APE), tumour margin and alpha-fetoprotein (AFP) were independent influencing factors for MVI in HCC. The nomogram integrating these risk factors had a good predictive effect, with AUC, specificity and sensitivity values of 0.834 (95% CI: 0.774–0.895), 75.0% and 83.5%, respectively. The AUC values of external verification based on CT and MRI image data were 0.794 (95% CI: 0.700–0.888) and 0.883 (95% CI: 0.807–0.959), respectively. No statistical difference in AUC values among training set and testing sets was found.ConclusionThe proposed nomogram prediction model for MVI in HCC has high accuracy, can be used with different imaging techniques, and has good clinical applicability.

Effect of residual volume after surgery of the discoid lateral meniscus on tibiofemoral joint biomechanics: a finite element analysis

BackgroundThe purpose of this study was to investigate the influence of different residual meniscus volume on the biomechanics of tibiofemoral joint after discoid lateral meniscus (DLM) surgery by finite element analysis.MethodsA knee joint model was established based on CT and MRI imaging data. The DLM model was divided into five regions according to conventional meniscectomy, with volumes of 15%, 15%, 15%, 15%, 15%, and 40% for each region. Additionally, the DLM model was divided into anterior and posterior parts to obtain ten regions. The DLM was resected according to the design scheme, and together with the intact discoid meniscus, a total of 15 models were obtained. Finite element analysis was conducted to assess shear and pressure trends on the knee joint.ResultsThe study observed significant changes in peak shear stress and compressive stress in the lateral meniscus and lateral femur cartilage. As the meniscus volume decreased, there was an increase in these stresses. Specifically, when the meniscus volume reduced to 40%, there was a sharp increase in shear stress (302%) and compressive stress (152%) on the meniscus, as well as shear stress (195%) and compressive stress (157%) on the lateral femur cartilage. Furthermore, the model grouping results showed that preserving a higher frontal volume in the meniscus model provided better biomechanical advantages.ConclusionThe use of finite element analysis has demonstrated that preserving more than 55% of the meniscus volume is necessary to prevent a significant increase in joint stress, which can potentially lead to joint degeneration. Additionally, it is crucial to preserve the front volume of the DLM in order to achieve improved knee biomechanical outcomes.

A Review of Machine Learning Techniques for Early Detection of Alzheimer's disease

This review aims to discuss the use of AI and ML in diagnosing and managing neurodegenerative diseases, with particular emphasis on AD and MCI. Emerging innovations present in depth the effectiveness of using ML models such as SVM, random forests, CNNs, and new frameworks such as quantum-classical neural networks on data obtained from MRI imaging, EEG signals, genetic makers and sociodemographic data. Widely used research findings demonstrate that these tools offer seemingly higher detection rates, sensitivity, and specificity than traditional diagnostic techniques for identifying and diagnosing early-stage illnesses. Some of them are techniques based on analyzing EEG time-frequency bands, combining MRI and PET data integration approaches, and creating telemedicine services to overcome geographical barriers. Furthermore, interpretable AI models improve clinical relevance by providing decision and trust support among practitioners. While these achievements are notable, the following limitations need to be noted, thus making it easier to establish the generalizability of the results and ways of using datasets that are free from bias and difficulties associated with applying AI in clinical settings. There are pressing questions regarding patients' rights and privacy, the issue of homogenization and standardization of data, and the distribution and accessibility of AI tools across industries as well as within the same region. More studies should be conducted to expand AI applications, use a more diverse dataset, and promote cooperation between representatives of various fields of science to ensure that technological advancement meets clinical demands. It also includes new methods like Vision Transformers and Quantum Computing Enhanced Deep Learning to overcome diagnostic issues in time-consuming and multi-parametric data analysis. These gaps can be closed with the help of AI and ML to enhance diagnostic accuracy, select the right treatment strategy, and risk assessment for the long-term management of NDs. In conclusion, this review similarly reaffirms how stunning AI's role is in improving future neurodegenerative disease care. For this reason, the deployment process must be done sensibly to enhance the patient's value most appropriately.