Different radiomics annotation methods comparison in rectal cancer characterisation and prognosis prediction: a two-centre study.

Objectives: To explore the performance differences of multiple annotations in radiomics analysis and provide a reference for tumour annotation in large-scale medical image analysis.

Methods: A total of 342 patients from two centres who underwent radical resection for rectal cancer were retrospectively studied and divided into training, internal validation, and external validation cohorts. Three predictive tasks of tumour T-stage (pT), lymph node metastasis (pLNM), and disease-free survival (pDFS) were performed.

Characterization of local white matter microstructural alterations in Alzheimer's disease: A reproducible study.

Alzheimer's disease (AD) is a neurodegenerative disease with a close association with microstructural alterations in white matter (WM). Current studies lack the characterization and further validation of specific regions in WM fiber tracts in AD. This study subdivided fiber tracts into multiple fiber clusters on the basis of automated fiber clustering and performed quantitative analysis along the fiber clusters to identify local WM microstructural alterations in AD.

A multi-cohort study of the hippocampal radiomics model and its associated biological changes in Alzheimer's Disease.

There have been no previous reports of hippocampal radiomics features associated with biological functions in Alzheimer's Disease (AD). This study aims to develop and validate a hippocampal radiomics model from structural magnetic resonance imaging (MRI) data for identifying patients with AD, and to explore the mechanism underlying the developed radiomics model using peripheral blood gene expression. In this retrospective multi-study, a radiomics model was developed based on the radiomics discovery group (n = 420) and validated in other cohorts.

Altered functional connectivity in patients with post-stroke fatigue: A resting-state fMRI study.

Background: Post-stroke fatigue (PSF) was a common complication after stroke. This study aimed to explore the neuroimaging mechanism of PSF, which was rarely studied.

Methods: Patients with the first episode of ischemic stroke were recruited from the First Affiliated Hospital of Wenzhou Medical University between March 2021 and December 2022.

Multi-scale context UNet-like network with redesigned skip connections for medical image segmentation.

Background And Objective: Medical image segmentation has garnered significant research attention in the neural network community as a fundamental requirement for developing intelligent medical assistant systems. A series of UNet-like networks with an encoder-decoder architecture have achieved remarkable success in medical image segmentation. Among these networks, UNet2+ (UNet++) and UNet3+ (UNet+++) have introduced redesigned skip connections, dense skip connections, and full-scale skip connections, respectively, surpassing the performance of the original UNet.

Uncertainty-guided transformer for brain tumor segmentation.

Multi-model data can enhance brain tumor segmentation for the rich information it provides. However, it also introduces some redundant information that interferes with the segmentation estimation, as some modalities may catch features irrelevant to the tissue of interest. Besides, the ambiguous boundaries and irregulate shapes of different grade tumors lead to a non-confidence estimate of segmentation quality.

The interaction of global small vessel disease burden and Alzheimer's disease pathologies do not change the independent association of amyloid-beta with hippocampal volume: A longitudinal study on mild cognitive impairment subjects.

The purpose of this study was to investigate whether the co-existence of global small vessel disease (SVD) burdens and Alzheimer's disease (AD) pathologies change hippocampal volume (HV) and cognitive function of mild cognitive impairment (MCI) subjects. We obtained MRI images, cerebrospinal fluid biomarkers (Aβ and p-tau), and neuropsychological tests of 310 MCI subjects from ADNI. The global SVD score was assessed.

Deep Learning-Based Multiparametric MRI Model for Preoperative T-Stage in Rectal Cancer.

Background: Conventional MRI staging can be challenging in the preoperative assessment of rectal cancer. Deep learning methods based on MRI have shown promise in cancer diagnosis and prognostication. However, the value of deep learning in rectal cancer T-staging is unclear.

Lactate Dehydrogenase Predicts Hemorrhagic Transformation in Patients with Acute Ischemic Stroke.

Introduction: Hemorrhagic transformation (HT) is a severe but frequent complication of acute ischemic stroke (AIS). This study aimed to evaluate the relationship between serum lactate dehydrogenase (LDH) levels and HT.

Methods: We retrospectively included 542 AIS patients with HT and 1,091 age- and gender-matched patients without HT.

Observation of Curative Effect of Lung Recruitment in Patients with Acute Respiratory Distress Syndrome after Cardiopulmonary Bypass Surgery.

Recruitment maneuver (RM) has become a routine supplementary maneuver for clinical rescue of severe ARDS with low tidal volume/pressure-limited mechanical ventilation. Recruitment of patients with ARDS mechanical ventilation can improve the lung compliance, promote the opening of collapsed alveoli, improve the ratio of ventilation to blood flow, reduce dead space, reduce shunt flow, and improve oxygenation function. In this paper, the patients were divided into lung recruitment group and conventional treatment group by the random number permutation table method.

Semi-supervised region-connectivity-based cerebrovascular segmentation for time-of-flight magnetic resonance angiography image.

Deep-learning-based methods have achieved state-of-the-art results in cerebrovascular segmentation. However, it is costly and time-consuming to acquire labeled data because of the complex structure of cerebral vessels. In this paper, we propose a novel semi-supervised cerebrovascular segmentation with a region-connectivity-based mean teacher model (RC-MT) from time-of-flight magnetic resonance angiography (TOF-MRA), where unlabeled data is introduced into the training.

Automated quantification of brain connectivity in Alzheimer's disease using ClusterMetric.

Diffusion magnetic resonance imaging tractography allows investigating brain structural connections in a noninvasive way and has been widely used for understanding neurological disease. Quantification of brain connectivity along with its length by dividing a fiber bundle into multiple segments (node) is a powerful approach to assess biological properties, which is termed as tractometry. However, current tractometry methods face challenges in node identification along with the length of complex bundles whose morphology is difficult to summarize.

Brain network connectivity feature extraction using deep learning for Alzheimer's disease classification.

Early diagnosis and therapeutic intervention for Alzheimer's disease (AD) is currently the only viable option for improving clinical outcomes. Combining structural magnetic resonance imaging (sMRI) and resting-state functional magnetic resonance imaging (rs-fMRI) to diagnose AD has yielded promising results. Most studies assume fixed time lags when constructing functional networks.

Integrative Analysis of 5-Hydroxymethylcytosine and Transcriptional Profiling Identified 5hmC-Modified lncRNA Panel as Non-Invasive Biomarkers for Diagnosis and Prognosis of Pancreatic Cancer.

Pancreatic adenocarcinoma (PAAD) is the fourth leading cause of cancer-related deaths worldwide. 5-Hydroxymethylcytosine (5hmC)-mediated epigenetic regulation has been reported to be involved in cancer pathobiology and has emerged to be promising biomarkers for cancer diagnosis and prognosis. However, 5hmC alterations at long non-coding RNA (lncRNA) genes and their clinical significance remained unknown.

White matter alterations in heart-kidney imbalance insomnia and Jiao-Tai-Wan treatment: A diffusion-tensor imaging study.

Previous studies have reported changes in white matter microstructures in patients with insomnia. However, few neuroimaging studies have focused specifically on white matter tracts in insomnia patients after having received treatment. In this prospective study, diffusion-tensor imaging was used in two samples of heart-kidney imbalance insomnia patients who were treated with placebo or Jiao-Tai-Wan, a traditional Chinese medicine commonly used to treat heart-kidney imbalance insomnia, to assess the changes in white matter tracts.

Intravoxel incoherent motion diffusion-weighted imaging in the characterization of Alzheimer's disease.

Objectives: Alzheimer's disease (AD) is the most common type of dementia, and characterizing brain changes in AD is important for clinical diagnosis and prognosis. This study was designed to evaluate the classification performance of intravoxel incoherent motion (IVIM) diffusion-weighted imaging in differentiating between AD patients and normal control (NC) subjects and to explore its potential effectiveness as a neuroimaging biomarker.

Methods: Thirty-one patients with probable AD and twenty NC subjects were included in the prospective study.

Mean apparent diffusion coefficient in a single slice may predict tumor response to whole-brain radiation therapy in non-small-cell lung cancer patients with brain metastases.

Objectives: This study aimed to access the performance of apparent diffusion coefficient (ADC) as a predictor for treatment response to whole-brain radiotherapy (WBRT) in patients with brain metastases (BMs) from non-small-cell lung cancer (NSCLC).

Methods: A retrospective analysis was conducted of 102 NSCLC patients with BMs who underwent WBRT between 2012 and 2016. Diffusion-weighted MRI were performed pre-WBRT and within 12 weeks after WBRT started.

MRI compared with low-dose CT scanning in the diagnosis of axial spondyloarthritis.

Objectives: To compare the performance of conventional radiography, ldCT, and MRI in the diagnosis of sacroiliitis in suspected axial spondyloarthritis (axSpA).

Methods: Patients presenting with > 3 months chronic back pain were assessed by axSpA-experienced rheumatologists and diagnosed as axSpA or not; axSpA patients were then considered nr-axSpA or AS using plain radiography. Non-axSpA patients were recruited as controls, and divided into non-inflammatory and inflammatory groups on the basis of inflammatory back pain and/or CRP/ESR elevation.

Characterization of Alzheimer's Disease Using Ultra-high b-values Apparent Diffusion Coefficient and Diffusion Kurtosis Imaging.

The aim of the study is to investigate the diffusion characteristics of Alzheimer's disease (AD) patients using an ultra-high b-values apparent diffusion coefficient (ADC_uh) and diffusion kurtosis imaging (DKI). A total of 31 AD patients and 20 healthy controls (HC) who underwent both MRI examination and clinical assessment were included in this study. Diffusion weighted imaging (DWI) was acquired with 14 b-values in the range of 0 and 5000 s/mm.

Molecular Mechanism Exploration of Pancreatitis Based on miRNA Expression Profile.

Background: Pancreatitis is a popular disease around the world, and can also lead to pancreatic cancer. Pancreatitis can be distinguished into two types, acute pancreatitis (AP) and chronic pancreatitis (CP). Every year, AP leads to approximately 275,000 new cases and is the most frequent gastrointestinal disease in American.

An Intelligent Parkinson's Disease Diagnostic System Based on a Chaotic Bacterial Foraging Optimization Enhanced Fuzzy KNN Approach.

Parkinson's disease (PD) is a common neurodegenerative disease, which has attracted more and more attention. Many artificial intelligence methods have been used for the diagnosis of PD. In this study, an enhanced fuzzy -nearest neighbor (FKNN) method for the early detection of PD based upon vocal measurements was developed.

Visualization of Anatomic Variation of the Anterior Septal Vein on Susceptibility-Weighted Imaging.

Background And Purpose: Understanding the anatomy of the anterior septal vein (ASV) is critical for minimally invasive procedures to the third ventricle and for assessing lesion size and venous drainage in the anterior cranial fossa. Accordingly, this study evaluated topographic anatomy and anatomic variation of the ASV using susceptibility-weighted imaging (SWI).

Methods: Sixty volunteers were examined using a 3.

Lipid storage changes in human skeletal muscle during detraining.

Unlabelled: Exercise training is known to increase intramuscular triglyceride content in both trained and untrained legs. The purpose of the study was to determine the changes of intramyocellular lipids (IMCL) and extramyocellular lipids (EMCL) of both trained and untrained legs during detraining. We measured both IMCL and EMCL levels in previously trained vs.

[Functional MRI and cognition assessment in subcortical ischemic vascular disease].

Objective: To evaluate the imaging features of patients with vascular cognitive impairment (VCI) induced by Subcortical Ischemic Vascular Disease (SIVD), through magnetic resonance diffusion tensor imaging (DTI) and proton spectroscopy (MRS) technology.

Methods: A total of 52 patients with SIVD were enrolled. After analysis of scale score, 32 patients with cognitive impairment were assigned to VCI group and 20 patients with no cognitive impairment were assigned to control group.