Brain Magnetic Resonance Imaging Images Research Articles

3D‐SRDM: 3D super‐resolution of MRI volumes based on diffusion model

AbstractBrain magnetic resonance imaging (MRI) is crucial for diagnosing and understanding neurological disorders, but inherent limitations hinder the visualization of fine details in brain structures. The emergence of super‐resolution techniques, especially deep‐learning methods, has improved imaging quality of MRI, by increasing MRI spatial resolution. At present, deep‐learning algorithms mostly performed super‐resolution on 2D MRI images. However, considering 3D nature of MRI, 3D models are more suitable for brain MRI super‐resolution. To achieve finer brain structural details, this study proposes a 3D brain MRI super‐resolution method based on diffusion model (3D‐SRDM), which is a fast and easily trainable neural network for the generation of high‐resolution brain MRI images. In our 3D‐SRDM model, the self‐attention module in U‐Net is replaced with 3D spatial attention mechanism. The network structure of 3D‐SRDM is optimized to reduce training parameters. Moreover, accelerated sampling from denoising diffusion implicit model is also incorporated to reduce time consumption. By these optimizations, compared with original diffusion model, the proposed model can achieve about 10‐ and 5‐fold speed increase at 4× and 8× super‐resolution of 3D brain MRI volumes, respectively, almost without affecting image quality. Thus, 3D‐SRDM has potential application value in efficiently generating high‐resolution 3D brain MRI images, thus facilitating the doctors' diagnosis.

Design of Novel Brain Tumor Segmentation System Using Hybrid Heuristic-Aided Multiscale Self-Guided Attention Mechanism-Based Adaptive Unet+++ with 3D Brain MRI Images

Segmentation of brain tumors attains great importance in the medical industry. As the brain tumor causes an earlier death, detection and diagnosis are required. Generally, brain tumor diagnosis is done by considering Magnetic Resonance Imaging (MRI) because it has superficial feature information. In the existing works, the different segmentation process is analyzed, yet it is more time-consuming, and has complexities. Thus, building of an efficient segmentation model is a quite challenging task. As the former implemented models are not in place to segment the abnormal region properly, it suggests developing an effective automated model using recently emerged techniques of deep learning. To surmount such challenging factors, a novel 3D brain tumor segmentation model is proposed with hybrid heuristic development. Initially, the brain images are collected from the standard benchmark datasets. The collected brain images are pre-processed using the adaptive technique of Contrast Limited Adaptive Histogram Equalization (CLAHE). The pre-processed images are segmented with the developed Multiscale Self-Guided Attention Mechanism-based Adaptive UNet3+ (MSGAM-AUNet3+), where the parameters are optimized with the hybrid optimization strategy of Modified Path Finder Coyote Optimization (MPFCO) to elevate the segmentation performance. The experimental analysis is carried out to estimate the efficiency of the developed framework with the comparison using diverse segmentation techniques.

Automatic brain-tumor diagnosis using cascaded deep convolutional neural networks with symmetric U-Net and asymmetric residual-blocks

The use of various kinds of magnetic resonance imaging (MRI) techniques for examining brain tissue has increased significantly in recent years, and manual investigation of each of the resulting images can be a time-consuming task. This paper presents an automatic brain-tumor diagnosis system that uses a CNN for detection, classification, and segmentation of glioblastomas; the latter stage seeks to segment tumors inside glioma MRI images. The structure of the developed multi-unit system consists of two stages. The first stage is responsible for tumor detection and classification by categorizing brain MRI images into normal, high-grade glioma (glioblastoma), and low-grade glioma. The uniqueness of the proposed network lies in its use of different levels of features, including local and global paths. The second stage is responsible for tumor segmentation, and skip connections and residual units are used during this step. Using 1800 images extracted from the BraTS 2017 dataset, the detection and classification stage was found to achieve a maximum accuracy of 99%. The segmentation stage was then evaluated using the Dice score, specificity, and sensitivity. The results showed that the suggested deep-learning-based system ranks highest among a variety of different strategies reported in the literature.

Exploring Deep Relationship Learning for Regression: A Case Study on Brain Age Estimation

To understand how the brain develops and matures, an accurate age estimate utilizing MRI pictures is essential. There has been encouraging progress in this area using deep learning-based methods, especially CNNs, which can extract important features from MRI scans. By combining the VGG16 and EDCNN networks, this study presents a new method for determining brain age. For feature extraction, we use VGG16, and for brain age prediction, we use EDCNN. The proposed method was evaluated using the Kaggle repository, a freely available benchmark dataset that includes magnetic resonance imaging (MRI) images of 562 individuals ranging in age from 20 to 86. Following the preprocessing of the dataset, the images were adjusted to 224 × 224 x 3 dimensions and normalized. Using a broad range of evaluation criteria, the proposed methodology's performance was contrasted with that of several state-of-the-art techniques. Additionally, ablation experiments were conducted as part of this study to determine the significance of various components of the suggested technique. The results showed that the method's performance was significantly improved when VGG16 was used as a feature extractor. In addition, this study found that the proposed method's performance was significantly improved after VGG16 network tuning. The research demonstrates that technologies based on deep learning might help us comprehend how the brain develops and ages. The approach uses EDCNN to forecast brain ageing and VGG16 to extract features. In order to decipher the structural subtleties seen in brain MRI images, the acclaimed feature extraction tool VGG16 is used as a base.

Brain Damage Detection Using Ensemble Classifier for Children Health Progress

Magnetic Resonance Imaging (MRI) is a medical imaging technique used to detect brain damage, particularly in children. Previous classification techniques for brain damage had low accuracy, complex edge detection, and high noise levels. In this proposed work, use an ensemble classification technique to analyze MRI brain images of 6-10 year old children. The input data is preprocessed using a Median filter to remove noise and improve image quality before being sent to a segmentation technique. Each part of the segmented image, such as tissue, inner layer, overall shape, and texture, is classified based on pixel size. The classification is done using an ensemble of Support Vector Machines (SVM) and K-Nearest Neighbors (KNN) techniques. The ensemble prediction is multiplied with several input values to classify the MRI image, aggregating different layer values. The output classified image is validated and developed using Mat Lab software simulation, with an accuracy of 97.86%. This method can accurately classify various types of brain damage and aid in early detection in children.

A Survey on AI-based Parkinson Disease Detection: Taxonomy, Case Study, and Research Challenges

Parkinson Disease (PD) is most common diseases from majority of disease encountered all over the world, with more than 7 million individuals being affected. PD is a type of progressive nervous system disease, causing deterioration in health or function. The timely identification of PD is a significant challenge because it rarely shows symptoms in the early stages. Moreover, it is typically encountered in older people where the symptoms sometimes coincide with age-related issues. Deep Learning (DL) can be integrated into many methodologies in diagnosing PD, such as Magnetic Resonance Imaging (MRI) and Single-Photon Emission Computed Tomography (SPECT). DL algorithms can detect PD based on observing some common symptoms. Moreover, it can also be detected using brain MRI images. So, in this study, we reviewed existing DL algorithms for timely identification of PD. We also have developed a CNN model for the timely identification of PD. We used 3D brain MRI images of PPMI datasets and achieved the 88% accuracy.

A visual attention-based algorithm for brain tumor detection using an on-center saliency map and a superpixel-based framework

Brain tumors are life-threatening and are typically identified by experts using imaging modalities like Magnetic Resonance Imaging (MRI), Computed Tomography (CT), and Positron Emission Tomography (PET). However, any error due to human intervention in brain anomaly detection can have devastating consequences. This study proposes a tumor detection algorithm for brain MRI images. Previous research into tumor detection has drawbacks, paving the way for further investigations. A visual attention-based technique for tumor detection is proposed to overcome these drawbacks. Brain tumors have a wide range of intensity, varying from inner matter-alike intensity to skull-alike intensity, making them difficult to threshold. Thus, a unique approach to threshold using entropy has been utilized. An on-center saliency map accurately captures the biological visual attention-focused tumorous region from the original image. Later, a superpixel-based framework has been proposed and used to capture the true structure of the tumor. Finally, it was experimentally shown that the proposed algorithm outperforms the existing algorithms for brain tumor detection.

Brain Tumor Detection Using Neural Classification in Machine Learning

Brain cancer classification is a difficult task due to the variety and complexity of tumors shown in magnetic resonance imaging (MRI) pictures. This research presents two neural network approaches for categorizing MRI brain images. The proposed neural network method consists of three steps: feature extraction, dimensionality reduction, and classification. First, we extracted features from MRI images using discrete wavelet transformation (DWT). In this second stage, we reduce the salient features of MRIs using principal component analysis (PCA). For the classification step, two supervised machine learning classifiers have been developed. Artificial neural networks are used by both classifiers; however, the second one employs back propagation (BPN) while the first one uses feed-forward (FF-ANN). Using the classifiers, MRI brain images of the subjects were classified as normal or abnormal. Artificial neural networks have numerous applications, including function approximation, feature extraction, optimization, and classification (ANNs). They are specifically intended to enhance photos, distinguish and categorize items, separate and register objects, and extract features. Among these, object and picture recognition is the most important for complex processing tasks such as classifying brain tumors. Radial basis function (RBF), cellular, multi-layer perceptron (MLP), hop field, and pulse-coupled neural networks have all been used in image segmentation. These networks can be categorized as feed-forward (associative) or feedback (auto-associative).

Alzheimer's MRI Recognition Based on Convolutional Neural Networks

Alzheimer's disease (AD) is a progressively worsening neurodegenerative disorder, where early diagnosis is crucial for implementing effective disease management strategies. In multimodal diagnostic pathways, Magnetic Resonance Imaging (MRI) plays a pivotal role as a non-invasive imaging technique in disease identification and progression monitoring. Deep learning, particularly Convolutional Neural Networks (CNN), can extract key biomarkers from complex imaging data. By training CNN models to automatically interpret MRI scans, radiology experts can utilize these advanced analytical tools for more efficient and consistent pathological assessments, thereby enhancing clinical decision support systems. This study develops a deep learning-based method for the automatic classification of Alzheimer's brain MRI images, using convolutional neural networks to categorize MRI images into four stages: no dementia, very mild dementia, mild dementia, and moderate dementia. A CNN model is constructed, learning distinctive features of the images through multi-level feature extraction and performing feature map visualization. Early stopping is employed to prevent overfitting. The model is trained on a training set and evaluated on a test set, with performance metrics including confusion matrix, accuracy, precision, recall, F1 score, Kappa coefficient, Matthew’s coefficient, ROC curve with AUC value, and PQ curve with AP value. The results show that the proposed model effectively differentiates between various categories of MRI images, providing valuable tools for early diagnosis and condition monitoring.

Probable Novel APP Met671Leu Mutation in a Chinese Han Family with Early-Onset Alzheimer's Disease.

Familial Alzheimer's disease (AD) is a rare disease caused by autosomal-dominant mutations. APP (encoding amyloid precursor protein), PSEN1 (encoding presenilin 1), and PSEN2 (encoding presenilin 2) are the most common genes cause dominant inherited AD. This study aimed to demonstrate a Chinese early-onset AD pedigree presenting as progressive memory impairment, apraxia, visual-spatial disorders, psychobehavioral disorders, and personality changes with a novel APP gene mutation. The family contains four patients, three carries and three normal family members. The proband underwent brain magnetic resonance imaging (MRI), 18F-fludeoxyglucose positron emission tomography (18F-FDG-PET), cerebrospinal fluid amyloid detection, 18F-florbetapir (AV-45) Positron Emission Computed Tomography (PET) imaging, whole-exome sequencing and Sanger sequencing. Brain MRI images showed brain atrophy, especially in the entorhinal cortex, temporal hippocampus, and lateral ventricle dilation. The FDG-PET showed hypometabolism in the frontotemporal, parietal, and hippocampal regions. 18F-florbetapir (AV-45) PET imaging showed cerebral cortex Aβ protein deposition. The cerebrospinal fluid amyloid protein test showed Aβ42/Aβ40 ratio decreases, pathological phosphor-tau level increases. Whole-exome sequencing detected a new missense mutation of codon 671 (M671L), which was a heterozygous A to T point mutation at position 2011 (c.2011A > T) in exon 16 of the amyloid precursor protein, resulting in the replacement of methionine to Leucine. The co-separation analysis was validated in this family. The mutation was found in 3 patients, 3 clinical normal members in the family, but not in the other 3 unaffected family members, 100 unrelated normal subjects, or 100 sporadic patients with AD. This mutation was probably pathogenic and novel in a Chinese Han family with early-onset AD.

Detection of Early Stage Alzheimer's Disease in Gradient-Based MR Images Using Deep Learning Methods

Alzheimer's Disease (AD), a form of dementia prevalent in older age, is a neurodegenerative condition impacting brain nerve cells. Early-stage AD symptoms are often subtle, complicating timely diagnosis. Early detection allows for intervention, slowing disease progression and facilitating appropriate treatments. Deep learning methods, particularly gradient-based images, prove promising for early Alzheimer's detection in Magnetic Resonance (MR) imaging. Gradient-based images, highlighting details in low-intensity images and enhancing contrast, play a vital role in determining structures' location, shape, and size, notably in techniques like Magnetic Resonance Imaging (MRI). This study aims to boost model performance in early AD detection by applying the Gradient filter before training deep learning models on diverse-angle and constant-density brain MRI images. The dataset comprises three categories representing early-stage AD, including images of mild cognitive impairment and healthy individuals. Original and gradient-filtered image subsets were inputted into deep learning models. Results indicate superior performance of gradient-based images, with the Densenet201 deep learning model achieving the highest accuracy at 98.63%.

Based on neural network cascade abnormal texture information dissemination of classification of patients with schizophrenia and depression

This study used MRI brain image segmentation to identify novel magnetic resonance imaging (MRI) biomarkers to distinguish patients with schizophrenia (SCZ), major depressive disorder (MD), and healthy control (HC). Brain texture measurements, including entropy and contrast, were calculated to capture variability in adjacent MRI voxel intensity. These measures are then applied to group classification in deep learning techniques and combined with hierarchical correlations to locate results. Texture feature maps were extracted from segmented brain MRI scans of 141 patients with schizophrenia (SCZ), 103 patients with major depressive disorder (MD) and 238 healthy controls (HC). Gray scale coassociation matrix (GLCM) is a monomer matrix calculated in a voxel cube. Deep learning methods were evaluated to determine the application capability of texture feature mapping in binary classification (SCZ vs. HC, MD vs. HC, SCZ vs. MD). The method is implemented by repeated nesting and cross-validation for feature selection. Regions that show the highest correlation (positive or negative). In this study, the authors successfully classified SCZ, MD and HC. This suggests that texture analysis can be used as an effective feature extraction method to distinguish different disease states. Compared with other methods, texture analysis can capture richer image information and improve classification accuracy in some cases. The classification accuracy of SCZ and HC, MD and HC, SCZ and MD reached 84.6%, 86.4% and 76.21%, respectively. Among them, SCZ and HC are the most significant features with high sensitivity and specificity.

Development of Brain MRI Image Segmentation program using UNET++ network for radiosurgery planning

Image processing is one of the most important and widely used techniques in the medical field. Magnetic Resonance Imaging (MRI) can provide diagnostic images with high contrast and high resolution, especially for low-density tissue. Therefore, applications to support tumor prediction are researched and developed. In this paper, we use applied artificial intelligence to identify and detect tumors using the UNET ++ deep learning model, which achieved results with a recognition rate of about 80%. The results for a great deal of built-in functionality in the built-in physician support software system in practice.

Hydranencephaly Associated with Bilateral Megalocornea and Bilateral Cleft Lip in an Infant of Mother with Gestational Diabetes Mellitus

Abstract Background: Hydranencephaly is a rare congenital disorder of the central nervous system, in which the cerebrum is replaced by a membranous sac of cerebrospinal fluid. Clinical Description: A 2-month-old baby girl presented with abnormal body movements. She was born to a mother with gestational diabetes and had been diagnosed with hydrocephalus antenatally. The baby had features of facial dysmorphism such as bilateral megalocornea, bilateral cleft lip, and cleft palate. A magnetic resonance imaging (MRI) brain revealed the absence of cerebral hemispheres with remnants of parenchyma in the orbitofrontal area that had been replaced by cerebrospinal fluid, diagnostic of hydranencephaly. Karyotyping and echocardiography findings were normal. Management and Outcome: The prognosis was explained to parents, who opted for no therapeutic intervention. The thus patient kept on supportive care and she succumbed at the age of 6 months. Conclusion: Our case creates awareness regarding a possible association between gestational diabetes and hydranencephaly. Furthermore, the hydranencephaly in this case was unique due to its association with bilateral megalocornea and cleft lip. The case reiterates the fact that a hydrocephalus diagnosed antenatally may actually be a hydranencephaly, which is confirmed in MRI brain imaging postnatally.

Fusion of Features: A Technique to Improve Autism Spectrum Disorder Detection Using Brain MRI Images

Autism Spectrum Disorder (ASD) is a major incident neurological disorder. Medical practitioners use different diagnostic techniques such as Electroencephalogram (EEG) Analysis, Magnetic Resonance Imaging (MRI) analysis, and traditional Behavioral Analysis for ASD detection. However, diagnosis success largely depends on specialists' knowledge and remains seldom accessible to remote patients. To address this issue, recently, various machine learning (ML) approaches have been developed for ASD detection using brain MRI images. The performance of these approaches is often limited because of poor feature discrimination, inferior quality of features, high feature length, and poor correlation of features. Thus, there is a need for robust feature extraction and selection techniques to improve the performance of ASD detection. The proposed work demonstrates a fusion of three features, namely Gray Level Co-occurrence Matrix (GLCM) based holistic texture features, Local Binary Pattern (LBP) based local texture features, and Geometrical Features of the Corpus Callosum (GFCC) from brain MRI images. Further, a correlation-based feature selection technique is employed for the salient feature selection from the GLCM, LBP, and GFCC set to improve the feature quality. The effectiveness of the selected feature is evaluated using three ML classifiers such as K-Nearest neighbor (KNN), Support Vector Machine (SVM), and Classification Tree (CT). The proposed ASD detection scheme provides an accuracy of 95.86% with 10-fold cross-validation with a CT classifier. It is observed that the accuracy of the proposed system is improved by 11.32% over the recent GLCM-based ASD system. The correlation-based feature selection techniques minimize the recognition time by 34.95% over the ASD system without feature selection.

Transfer Learning for Alzheimer's Disease Diagnosis using EfficientNet-B0 Convolutional Neural Network

Alzheimer's disease (AD) is an irreversible neurological disorder that causes the gradual decline of one's cognitive abilities, and thus, early detection is significant to slow down its deterioration. Magnetic resonance imaging (MRI) images have been commonly used to diagnose AD. Furthermore, deep learning techniques such as the Convolutional Neural Network (CNN) are utilized to assist the diagnosis due to the complexity of MRI's analysis. However, CNN models require large datasets for training and have a challenging nature for model optimization. Thus, Transfer Learning, an emerging method that can improve the performance of the deep learning model by eliminating the need for training from scratch, is introduced. This paper will propose a Transfer Learning-based EfficientNet-B0 model to classify MRI brain images for AD diagnosis. The MRI images are obtained from Alzheimer's Disease Neuroimaging Initiative (ADNI) database; only axial-plane images are used in this study. As a result, the multi-class classification of MRI images into AD, MCI, and NC classes using a Transfer Learning-based model resulted in a training accuracy of 98.93% and a validation accuracy of 87.17%. These results evidenced the significance of Transfer Learning in improving model performance.

PDFF-CNN: An attention-guided dynamic multi-orientation feature fusion method for gestational age prediction on imbalanced fetal brain MRI dataset.

Fetal brain magnetic resonance imaging (MRI)-based gestational age prediction has been widely used to characterize normal fetal brain development and diagnose congenital brainmalformations. The uncertainty of fetal position and external interference leads to variable localization and direction of the fetal brain. In addition, pregnant women typically concentrate on receiving MRI scans during the fetal anomaly scanning week, leading to an imbalanced distribution of fetal brain MRI data. The above-mentioned problems pose great challenges for deep learning-based fetal brain MRI gestational ageprediction. In this study, a pyramid squeeze attention (PSA)-guided dynamic feature fusion CNN (PDFF-CNN) is proposed to robustly predict gestational ages from fetal brain MRI images on an imbalanced dataset. PDFF-CNN contains four components: transformation module, feature extraction module, dynamic feature fusion module, and balanced mean square error (MSE) loss. The transformation and feature extraction modules are employed by using the PSA to learn multiscale and multi-orientation feature representations in a parallel weight-sharing Siamese network. The dynamic feature fusion module automatically learns the weights of feature vectors generated in the feature extraction module to dynamically fuse multiscale and multi-orientation brain sulci and gyri features. Considering the fact of the imbalanced dataset, the balanced MSE loss is used to mitigate the negative impact of imbalanced data distribution on gestational age predictionperformance. Evaluated on an imbalanced fetal brain MRI dataset of 1327 routine clinical T2-weighted MRI images from 157 subjects, PDFF-CNN achieved promising gestational age prediction performance with an overall mean absolute error of 0.848 weeks and an of 0.904. Furthermore, the attention activation maps of PDFF-CNN were derived, which revealed regional features that contributed to gestational age prediction at each gestationalstage. These results suggest that the proposed PDFF-CNN might have broad clinical applicability in guiding treatment interventions and delivery planning, which has the potential to be helpful with prenatal diagnosis.

Hierarchical neural architecture search with adaptive global–local feature learning for Magnetic Resonance Image reconstruction

Neural architecture search (NAS) has been introduced into the design of deep neural network architectures for Magnetic Resonance Imaging (MRI) reconstruction since NAS-based methods can acquire the complex network architecture automatically without professional designing experience and improve the model’s generalization ability. However, current NAS-based MRI reconstruction methods suffer from a lack of efficient operators in the search space, which leads to challenges in effectively recovering high-frequency details. This limitation is primarily due to the prevalent use of convolution operators in the current search space, which struggle to capture both global and local features of MR images simultaneously, resulting in insufficient information utilization. To address this issue, a generative adversarial network (GAN) based model is proposed to reconstruct the MR image from under-sampled K-space data. Firstly, parameterized global and local feature learning modules at multiple scales are added into the search space to improve the capability of recovering high-frequency details. Secondly, to mitigate the increased search time caused by the augmented search space, a hierarchical NAS is designed to learn the global–local feature learning modules that enable the reconstruction network to learn global and local information of MR images at different scales adaptively. Thirdly, to reduce the number of network parameters and computational complexity, the standard operations in global–local feature learning modules are replaced with lightweight operations. Finally, experiments on several publicly available brain MRI image datasets evaluate the performance of the proposed method. Compared to the state-of-the-art MRI reconstruction methods, the proposed method yields better reconstruction results in terms of peak signal-to-noise ratio and structural similarity at a lower computational cost. Additionally, our reconstruction results are validated through a brain tumor classification task, affirming the practicability of the proposed method. Our code is available at https://github.com/wwHwo/HNASMRI.

Radiomics of metastatic brain tumor as a predictive image biomarker of progression-free survival in patients with non-small-cell lung cancer with brain metastasis receiving tyrosine kinase inhibitors

Background and objectiveEpidermal growth factor receptor (EGFR)–targeted tyrosine kinase inhibitors (TKIs) are the first-line therapy for EGFR-mutant non-small-cell lung cancer (NSCLC). Early prediction of treatment failure in patients with brain metastases treated with EGFR–TKIs may help in making decisions for systemic drug therapy or local brain tumor control. This study examined the predictive power of the radiomics of both brain metastasis tumors and primary lung tumors. We propose a deep learning based CoxCC model based on quantitative brain magnetic resonance imaging (MRI), a prognostic index and clinical data; the model can be used to predict progression-free survival (PFS) after EGFR–TKI therapy in advanced EGFR-mutant NSCLC. MethodsThis retrospective single-center study included 271 patients receiving first-line EGFR–TKI targeted therapy in 2018–2019. Among them, 72 patients who had brain metastases before receiving first-line EGFR–TKI treatment. Three radiomic features were extracted from pretreatment brain MRI images. A CoxCC model for the progression risk stratification of EGFR–TKI treatment was proposed on the basis of MRI radiomics, clinical features, and a prognostic index. We performed time-dependent PFS predictions to evaluate the performance of the CoxCC model. ResultsThe CoxCC model based on a prognostic index, clinical features, and radiomic features of brain metastasis exhibited higher performance than clinical features combined with indexes previously proposed for determining the prognosis of brain metastasis, including recursive partitioning analysis, diagnostic-specific graded prognostic assessment, graded prognostic assessment for lung cancer using molecular markers (lung-molGPA), and modified lung-molGPA, with c-index values of 0.75, 0.67, 0.66, 0.65, and 0.65, respectively. The model achieved areas under the curve of 0.88, 0.73, 0.92, and 0.90 for predicting PFS at 3, 6, 9 and 12 months, respectively. PFS significantly differed between the high- and low-risk groups (p < 0.001). ConclusionsFor patients with advanced-stage NSCLC with brain metastasis, MRI radiomics of brain metastases may predict PFS. The CoxCC model integrating brain metastasis radiomics, clinical features, and a prognostic index provided reliable multi-time-point PFS predictions for patients with advanced NSCLC and brain metastases receiving EGFR–TKI treatment.

Manifestasi Klinis dan Gambaran Pencitraan Struktural dan Fungsional Berbagai Subtipe Demensia

Dementia causes impaired cognition, behavior, and daily functional activities with physical, mental, and psychosocial consequences for patients, families, and communities. However, dementia diagnosis is still challenging. Classification of dementia subtypes also requires structuraland functional imaging studies. The structural imaging needed to support Alzheimer’s dementia and vascular dementia diagnosis is brain magnetic resonance imaging (MRI), frontotemporal dementia diagnosis can be supported by brain MRI or functional imaging, while Lewy Body dementia diagnosis requires functional imaging and biomarkers findings.