A Preliminary Study on an Intelligent Segmentation and Classification Model for Amygdala-Hippocampus MRI Images in Alzheimer's Disease.

Background: This study developed a deep learning model for segmenting and classifying the amygdala-hippocampus in Alzheimer's disease (AD), using a large-scale neuroimaging dataset to improve early AD detection and intervention.

Methods: We collected 1000 healthy controls (HC) and 1000 AD patients as internal training data from 15 Chinese medical centers. The independent external validation dataset was sourced from another three centers.

Machine learning of whole-brain resting-state fMRI signatures for individualized grading of frontal gliomas.

Purpose: Accurate preoperative grading of gliomas is critical for therapeutic planning and prognostic evaluation. We developed a noninvasive machine learning model leveraging whole-brain resting-state functional magnetic resonance imaging (rs-fMRI) biomarkers to discriminate high-grade (HGGs) and low-grade gliomas (LGGs) in the frontal lobe.

Methods: This retrospective study included 138 patients (78 LGGs, 60 HGGs) with left frontal gliomas.

CAP-Net: Carotid Artery Plaque Segmentation System Based on Computed Tomography Angiography.

Rationale And Objectives: Diagnosis of carotid plaques from head and neck CT angiography (CTA) scans is typically time-consuming and labor-intensive, leading to limited studies and unpleasant results in this area. The objective of this study is to develop a deep-learning-based model for detection and segmentation of carotid plaques using CTA images.

Materials And Methods: CTA images from 1061 patients (765 male; 296 female) with 4048 carotid plaques were included and split into a 75% training-validation set and a 25% independent test set.

Radiomics based on BPX images to detect abnormal bone mineral density.

Background: Abnormal bone mineral density (BMD) is a major contributor to bone fragility and fractures. While dual-energy X-ray absorptiometry (DXA) and quantitative computed tomography (QCT) are the primary diagnostic modalities, both methods are associated with additional radiation exposure and costs. This study investigates the feasibility of using radiomics to establish an automated tool for identifying patients at high risk for BMD abnormality based on biplanar X-ray radiography (BPX) images.

Structural plasticity of the contralesional subfields of hippocampus and amygdala in patients with IDH-mutant astrocytoma and oligodendroglioma.

Purpose: To detect the structural plasticity of the contralesional hippocampus and amygdala in patients with unilateral IDH-mutant astrocytoma and oligodendroglioma, and to compare the differences between these two types of tumors.

Methods: 3D T1-weighted MRI images were collected from 46 patients with left-hemispheric tumors (IDH-mutant astrocytoma, n = 22; oligodendroglioma, n = 24) and 23 healthy controls (HCs). Volumetric differences in the subregional volumes of the hippocampus and amygdala were assessed using FreeSurfer software.

Alterations of gray matter volume and structural covariance network in unilateral frontal lobe low-grade gliomas.

Purpose: To explore the alterations of gray matter volume (GMV) and structural covariant network (SCN) in unilateral frontal lobe low-grade gliomas (FLGGs).

Materials And Methods: The three dimensional (3D) T1 structural images of 117 patients with unilateral FLGGs and 68 age- and sex-matched healthy controls (HCs) were enrolled. The voxel-based morphometry (VBM) analysis and graph theoretical analysis of SCN were conducted to investigate the impact of unilateral FLGGs on the brain structure.

A Medical Multimodal Large Language Model for Pediatric Pneumonia.

Pediatric pneumonia is the leading cause of death among children under five years worldwide, imposing a substantial burden on affected families. Currently, there are three significant hurdles in diagnosing and treating pediatric pneumonia. Firstly, pediatric pneumonia shares similar symptoms with other respiratory diseases, making rapid and accurate differential diagnosis challenging.

A systematic analysis for disease burden, risk factors, and trend projection of Alzheimer's disease and other dementias in China and globally.

Background: This study aims to provide improvement directions for aging societies by analyzing the disease burden, risk factors and trend forecasts of AD and other dementias (ADD) in China and globally from 1990 to 2021.

Methods: Data sourced from Global Burden of Disease 2021. We extracted indicators of disease burden and risk factors for ADD in people aged 40 years and older, including incidence, prevalence, deaths, disability-adjusted life years, years lived with disability and years of life lost.

Harnessing routine MRI for the early screening of Parkinson's disease: a multicenter machine learning study using T2-weighted FLAIR imaging.

Objective: To explore the potential of radiomics features derived from T2-weighted fluid-attenuated inversion recovery (T2W FLAIR) images to distinguish idiopathic Parkinson's disease (PD) patients from healthy controls (HCs).

Methods: T2W FLAIR images from 1727 subjects were retrospectively obtained from five cohorts and divided into a training set (395 PD/574 HC), an internal test set (99 PD/144 HC) and an external test set (295 PD/220 HC). Regions of interest (ROIs), including bilateral globus pallidus (GP), putamen (PU), substantia nigra (SN), and red nucleus (RN), were manually delineated.

Risk of acute cerebral infarction in patients with hypertension based on high-resolution MRI.

Background: Hypertension is associated with both carotid plaque formation and acute cerebral infarction (ACI). We aim to investigate the correlation between high-resolution magnetic resonance imaging (HR-MRI) characteristics of vulnerable carotid plaques and the severity of ipsilateral ACI in patients with hypertension.

Methods: A retrospective collection of HR-MRI carotid plaque of patients with hypertension was conducted.

AI-based automatic estimation of single-kidney glomerular filtration rate and split renal function using non-contrast CT.

Objectives: To address SPECT's radioactivity, complexity, and costliness in measuring renal function, this study employs artificial intelligence (AI) with non-contrast CT to estimate single-kidney glomerular filtration rate (GFR) and split renal function (SRF).

Methods: 245 patients with atrophic kidney or hydronephrosis were included from two centers (Training set: 128 patients from Center I; Test set: 117 patients from Center II). The renal parenchyma and hydronephrosis regions in non-contrast CT were automatically segmented by deep learning.

Automated Fast Prediction of Bone Mineral Density From Low-dose Computed Tomography.

Background: Low-dose chest CT (LDCT) is commonly employed for the early screening of lung cancer. However, it has rarely been utilized in the assessment of volumetric bone mineral density (vBMD) and the diagnosis of osteoporosis (OP).

Purpose: This study investigated the feasibility of using deep learning to establish a system for vBMD prediction and OP classification based on LDCT scans.

Accurate Prostate Segmentation in Large-Scale Magnetic Resonance Imaging Datasets via First-in-First-Out Feature Memory and Multi-Scale Context Modeling.

Prostate cancer, a prevalent malignancy affecting males globally, underscores the critical need for precise prostate segmentation in diagnostic imaging. However, accurate delineation via MRI still faces several challenges: (1) The distinction of the prostate from surrounding soft tissues is impeded by subtle boundaries in MRI images. (2) Regions such as the apex and base of the prostate exhibit inherent blurriness, which complicates edge extraction and precise segmentation.

White-Matter Structural Connectivity and Alzheimer's Disease: A Mendelian Randomization Study.

Background: Alzheimer's disease (AD) and white-matter structural connectivity have been linked in some observational studies, although it is unknown if this is a causal relationship. The purpose of this study was to examine the impact of various white-matter structural connectivity on AD via a two-sample multivariate Mendelian randomization (MR) approach.

Methods: The genome-wide association study (GWAS) of Wainberg et al.

A novel hybrid deep learning framework based on biplanar X-ray radiography images for bone density prediction and classification.

Unlabelled: This study utilized deep learning for bone mineral density (BMD) prediction and classification using biplanar X-ray radiography (BPX) images from Huashan Hospital Medical Checkup Center. Results showed high accuracy and strong correlation with quantitative computed tomography (QCT) results. The proposed models offer potential for screening patients at a high risk of osteoporosis and reducing unnecessary radiation and costs.

An automated hybrid approach via deep learning and radiomics focused on the midbrain and substantia nigra to detect early-stage Parkinson's disease.

Objectives: The altered neuromelanin in substantia nigra pars compacta (SNpc) is a valuable biomarker in the detection of early-stage Parkinson's disease (EPD). Diagnosis via visual inspection or single radiomics based method is challenging. Thus, we proposed a novel hybrid model that integrates radiomics and deep learning methodologies to automatically detect EPD based on neuromelanin-sensitive MRI, namely short-echo-time Magnitude (setMag) reconstructed from quantitative susceptibility mapping (QSM).

Key developments and hotspots of exosomes in Alzheimer's disease: a bibliometric study spanning 2003 to 2023.

Background: Alzheimer's disease (AD) is a degenerative illness of the central nervous system that is irreversible and is characterized by gradual behavioral impairment and cognitive dysfunction. Researches on exosomes in AD have gradually gained the attention of scholars in recent years. However, the literatures in this research area do not yet have a comprehensive visualization analysis.

SRTRP-Net: A multi-task learning network for segmentation and prediction of stereotactic radiosurgery treatment response in brain metastases.

Before the Stereotactic Radiosurgery (SRS) treatment, it is of great clinical significance to avoid secondary genetic damage and guide the personalized treatment plans for patients with brain metastases (BM) by predicting the response to SRS treatment of brain metastatic lesions. Thus, we developed a multi-task learning model termed SRTRP-Net to provide prior knowledge of BM ROI and predict the SRS treatment response of the lesion. In dual-encoder tumor segmentation Network (DTS-Net), two parallel encoders encode the original and mirrored multi-modal MRI images.

The Absence of Intra-Tumoral Tertiary Lymphoid Structures is Associated with a Worse Prognosis and mTOR Signaling Activation in Hepatocellular Carcinoma with Liver Transplantation: A Multicenter Retrospective Study.

Tertiary lymphoid structure (TLS) can predict the prognosis and sensitivity of tumors to immune checkpoint inhibitors (ICIs) therapy, whether it can be noninvasively predicted by radiomics in hepatocellular carcinoma with liver transplantation (HCC-LT) has not been explored. In this study, it is found that intra-tumoral TLS abundance is significantly correlated with recurrence-free survival (RFS) and overall survival (OS). Tumor tissues with TLS are characterized by inflammatory signatures and high infiltration of antitumor immune cells, while those without TLS exhibit uncontrolled cell cycle progression and activated mTOR signaling by bulk and single-cell RNA-seq analyses.

Automatic segmentation of hepatocellular carcinoma on dynamic contrast-enhanced MRI based on deep learning.

. Precise hepatocellular carcinoma (HCC) detection is crucial for clinical management. While studies focus on computed tomography-based automatic algorithms, there is a rareness of research on automatic detection based on dynamic contrast enhanced (DCE) magnetic resonance imaging.

HSA-net with a novel CAD pipeline boosts both clinical brain tumor MR image classification and segmentation.

Brain tumors are among the most prevalent neoplasms in current medical studies. Accurately distinguishing and classifying brain tumor types accurately is crucial for patient treatment and survival in clinical practice. However, existing computer-aided diagnostic pipelines are inadequate for practical medical use due to tumor complexity.