Assessment of Intravoxel Incoherent Motion Diffusion-Weighted Imaging in Insomnia.

Purpose: Few studies have investigated insomnia using intravoxel incoherent motion imaging. The aim ofthis study was to evaluate the classification performance of intravoxel incoherent motion diffusion-weighted imaging in differentiating between insomnia disorder patients and normal control subjects. The potential effects of microstructure and microcirculation between sleep quality and glymphatic system function were subsequently explored.

Synthetic MRI study of brain volume and subcortical myelin in various Parkinson's disease motor subtypes.

Alterations in brain volume and subcortical myelin content (MyC) between tremor-dominant (TD) and postural instability and gait difficulty (PIGD) subtypes of Parkinson's (PD) disease remain unclear. This study investigated whole-brain volume and subcortical myelin content differences among PD motor subtypes using synthetic MRI. Ninety-two PD patients (39 TD and 53 PIGD) and 39 healthy controls were included.

Multimodal imaging deep learning model for predicting extraprostatic extension in prostate cancer using MpMRI and 18 F-PSMA-PET/CT.

Objective: This study aimed to construct a multimodal imaging deep learning (DL) model integrating mpMRI and F-PSMA-PET/CT for the prediction of extraprostatic extension (EPE) in prostate cancer, and to assess its effectiveness in enhancing the diagnostic accuracy of radiologists.

Methods: Clinical and imaging data were retrospectively collected from patients with pathologically confirmed prostate cancer (PCa) who underwent radical prostatectomy (RP). Data were collected from a primary institution (Center 1, n = 197) between January 2019 and June 2022 and an external institution (Center 2, n = 36) between July 2021 and November 2022.

Distinguishing atypical parotid carcinomas and pleomorphic adenomas based on multiphasic computed tomography radiomics nomogram: a multicenter study.

Objective: This study aimed to develop, validate, and test a comprehensive radiomics prediction model using clinical data and contrast-enhanced multiphasic computed tomography (CT) scans for differentiating between atypical parotid carcinomas (PCAs) and pleomorphic adenomas (PAs) within a multicenter cohort.

Materials And Methods: The study involved 218 patients diagnosed with either PAs (n=162) or atypical PCAs (n=56) (no invasion of adjacent tissues or lymph node metastases) across three anonymized hospitals, divided into a training set (n=175) and a validation set (n=43). Clinical features and radiological findings were used to develop a clinical model.

Population-specific brain charts reveal Chinese-Western differences in neurodevelopmental trajectories.

Human brain charts provide unprecedented opportunities for decoding neurodevelopmental milestones and establishing clinical benchmarks for precision brain medicine . However, current lifespan brain charts are primarily derived from European and North American cohorts, with Asian populations severely underrepresented. Here, we present the first population-specific brain charts for China, developed through the Chinese Lifespan Brain Mapping Consortium (Phase I) using neuroimaging data from 43,037 participants (aged 0-100 years) across 384 sites nationwide.

Explainable classification of Parkinson's disease with different motor subtypes by analyzing the synthetic MRI quantitative parameters of subcortical nuclei.

Objectives: To explore differences in quantitative parameters of subcortical nuclei using synthetic MRI across different motor subtypes of Parkinson's Disease (PD), and to develop an interpretable model for distinguishing PD subtypes.

Methods: A total of 102 PD patients, including 43 Tremor-Dominant (TD) subtype and 59 Postural Instability and Gait Difficulty (PIGD) subtype and 42 age- and gender-matched healthy controls (HCs) were included. T1, T2, Proton Density (PrD), and Myelin Content (MYC) were extracted from 16 subcortical nuclei.

The predictive value of multiparametric MRI combined with [F]PSMA-1007 PET/CT for the pathological upgrade in prostate cancer: a multicenter study.

Purpose: This study aimed to develop a predictive model that integrates parameters derived from preoperative multiparametric magnetic resonance imaging (mpMRI) and [F]PSMA-1007 PET/CT for reliably predicting pathological upgrading from systematic biopsy (SB) to radical prostatectomy (RP) specimens.

Methods: We ultimately retrospectively analyzed 163 patients with biopsy-confirmed localized prostate cancer (PCa) who underwent preoperative mpMRI and [F]PSMA-1007 PET/CT scans between January 2019 and June 2022. Clinical and imaging characteristics were compared between patients with and without pathological upgrading.

Noninvasive prognostic classification of ITH in HCC with multi-omics insights and therapeutic implications.

Intratumoral heterogeneity (ITH) is a critical factor associated with treatment failure and disease relapse in hepatocellular carcinoma (HCC). However, decoding ITH in a noninvasive and comprehensive manner remains a notable challenge. In this study involving 851 patients from five centers, we developed a noninvasive prognostic classification for ITH using radiomics based on multisequence MRI, termed radiomics ITH (RITH) phenotypes.

Naringenin Attenuates Ox-LDL-induced Injury in HUVECs by Regulating Lipophagy Partly through the SOCE Pathway.

Objective: Naringenin (NAR) is a naturally occurring tiny molecule that has a significant role in lipid metabolism. However, the molecular mechanism by which NAR is involved in lipid metabolism to protect ECs is not clear. This study aims to investigate the effect of NAR on autophagy in oxidized low-density lipoprotein (ox-LDL)-treated human umbilical vein endothelial cells (HUVECs) and its potential molecular mechanisms.

Multimodal radiomics based on fluorine-18 prostate-specific membrane antigen positron emission tomography and multiparametric magnetic resonance imaging in predicting persistent prostate-specific antigen after radical prostatectomy.

Background: Persistent prostate-specific antigen (PSA) after radical prostatectomy (RP) is associated with increased metastasis and mortality. However, the value of the radiomics for predicting persistent PSA is unclear. Our study aimed to evaluate the diagnostic performance of F-PSMA-1007 positron emission tomography (PET) and multiparametric magnetic resonance imaging (mpMRI) radiomics for the prediction of persistent PSA after RP.

How growing up without siblings affects the adult brain and behaviour in the CHIMGEN cohort.

With the worldwide increase in only-child families, it is crucial to understand the effects of growing up without siblings (GWS) on the adult brain, behaviour and the underlying pathways. Using the CHIMGEN cohort, we investigated the associations of GWS with adult brain structure, function, connectivity, cognition, personality and mental health, as well as the pathway from GWS to GWS-related growth environments to brain and to behaviour development, in 2,397 pairs of individuals with and without siblings well matched in covariates. We found associations linking GWS to higher language fibre integrity, lower motor fibre integrity, larger cerebellar volume, smaller cerebral volume and lower frontotemporal spontaneous brain activity.

Cross-ancestry and sex-stratified genome-wide association analyses of amygdala and subnucleus volumes.

The amygdala is a small but critical multi-nucleus structure for emotion, cognition and neuropsychiatric disorders. Although genetic associations with amygdala volumetric traits have been investigated in sex-combined European populations, cross-ancestry and sex-stratified analyses are lacking. Here we conducted cross-ancestry and sex-stratified genome-wide association analyses for 21 amygdala volumetric traits in 6,923 Chinese and 48,634 European individuals.

Alteration of cortical structure and functional connectivity in childhood trauma.

Childhood trauma acts as an independent risk factor for mental disorders. This study utilized multi-modal MRI techniques to integrate cortical structural measurements and functional connectivity (FC) to identify neurobiological markers of trauma. This study recruited 215 participants, divided into a trauma group (n = 57) and a well-matched non-trauma group (n = 158) based on the childhood trauma questionnaire.

Association Between Single Nucleotide Polymorphisms in the Aquaporin-4 Gene and Longitudinal Changes in White Matter Free Water and Cognitive Function in Non-Demented Older Adults.

We investigated whether aquaporin-4 (AQP4) single-nucleotide polymorphisms (SNPs) influence Alzheimer's disease (AD) progression through changes in the glymphatic system. We included 242 non-dementia participants and chose six SNPs previously shown to be related to AD. We analyzed the associations between AQP4 SNPs and glymphatic markers, including enlarged perivascular spaces (PVS), white matter free water (FW), and diffusion tensor image analysis along the perivascular space (DTI-ALPS), in both cross-sectional and longitudinal data.

An automatic deep-learning approach for the prediction of post-stroke epilepsy after an initial intracerebral hemorrhage based on non-contrast computed tomography imaging.

Background: Post-stroke epilepsy (PSE) is a common and significant complication that often occurs after stroke, and affects patients' prognosis and overall quality of life. In recent years, non-contrast computed tomography (NCCT) has become the preferred method for the clinical diagnosis of intracerebral hemorrhage (ICH). This study aimed to develop and validate a triple deep-learning model, simply named, the post-stroke epilepsy network (PSENet), to predict PSE in ICH patients based on NCCT.

Prediction of adverse pathology in prostate cancer using a multimodal deep learning approach based on [F]PSMA-1007 PET/CT and multiparametric MRI.

Purpose: Accurate prediction of adverse pathology (AP) in prostate cancer (PCa) patients is crucial for formulating effective treatment strategies. This study aims to develop and evaluate a multimodal deep learning model based on [F]PSMA-1007 PET/CT and multiparametric MRI (mpMRI) to predict the presence of AP, and investigate whether the model that integrates [F]PSMA-1007 PET/CT and mpMRI outperforms the individual PET/CT or mpMRI models in predicting AP.

Methods: 341 PCa patients who underwent radical prostatectomy (RP) with mpMRI and PET/CT scans were retrospectively analyzed.

Association of small vessel disease progression with longitudinal cognitive decline across mild cognitive impairment.

Background: Cerebral small vessel disease (SVD) is the leading cause of vascular dementia. However, it is unclear whether the individual SVD or global SVD progression correlates with cognitive decline across mild cognitive impairment (MCI) subjects.

Objective: To investigate the association of small vessel disease progression with longitudinal cognitive decline across MCI.

Predicting Vulnerability Status of Carotid Plaques Using CTA-Based Quantitative Analysis.

The study aimed to develop a radiomics model to assess carotid artery plaque vulnerability using computed tomography angiography images. It retrospectively included 107 patients with carotid artery stenosis who underwent carotid artery stenting from 2017 to 2022. Patients were categorized into stable and vulnerable plaque groups based on pathology.

Utilizing combined quantitative multiparametric MRI as potential biomarkers for improved early-stage parkinson's disease diagnosis.

Background: Identifying Parkinson's disease (PD) during its initial phases presents considerable hurdles for clinicians.

Purpose: To examine the feasibility and efficacy of a machine learning model based on quantitative multiparametric magnetic resonance imaging (MRI) features in identifying early-stage PD.

Methods: We recruited 33 participants, including 19 with early-stage PD, 14 with advanced-stage PD and 20 healthy control subjects.

Clinical-Radiomics Nomogram Model Based on CT Angiography for Prediction of Intracranial Aneurysm Rupture: A Multicenter Study.

Objective: Risk estimation of intracranial aneurysm rupture is critical in determining treatment strategy. There is a scarcity of multicenter studies on the predictive power of clinical-radiomics models for aneurysm rupture. This study aims to develop a clinical-radiomics model and explore its additional value in the discrimination of aneurysm rupture.

Development and Validation of Multiparametric MRI-based Interpretable Deep Learning Radiomics Fusion Model for Predicting Lymph Node Metastasis and Prognosis in Rectal Cancer: A Two-center Study.

Rationale And Objectives: To develop interpretable machine learning models that utilize deep learning (DL) and radiomics based on multiparametric Magnetic resonance imaging (MRI) to predict preoperative lymph node (LN) metastasis in rectal cancer.

Materials And Methods: This retrospective study involved 286 cancer patients confirmed by histopathology from center 1 (Training set) and 66 patients from center 2 (External test set). Radiomics features were extracted from T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) sequences, whereas DL features were obtained using four models: MobileNet-V3-large, Inception-V3, ResNet50, and VGG16.

Stilbene glycosides alleviate atherosclerosis partly by promoting lipophagy of dendritic cells.

Atherosclerosis (AS) is a chronic inflammatory disease resulting from lipid metabolism disorders and immune imbalances. Dendritic cells (DCs) are key cells that regulate adaptive and adaptive immunity. When DCs engulf excessive amounts lipids, their function is altered, thereby, accelerating the inflammatory process of AS.

A dual-center study: can ultrasound radiomics differentiate type I and type II epithelial ovarian cancer patients with normal CA125 levels?

Objective: CA125 is recommended by many countries as the primary screening test for ovarian cancer. But there are patients with ovarian cancer having normal CA125. We hope to identify the types of EOC with normal CA125 levels better by building a refined model based on the ultrasound radiomics, thus providing precise medical treatment for patients.

Development and validation of a machine learning-based F-fluorodeoxyglucose PET/CT radiomics signature for predicting gastric cancer survival.

Background: Survival prognosis of patients with gastric cancer (GC) often influences physicians' choice of their follow-up treatment. This study aimed to develop a positron emission tomography (PET)-based radiomics model combined with clinical tumor-node-metastasis (TNM) staging to predict overall survival (OS) in patients with GC.

Methods: We reviewed the clinical information of a total of 327 patients with pathological confirmation of GC undergoing 18 F-fluorodeoxyglucose (18 F-FDG) PET scans.