Publications by authors named "Kaiji Deng"
Machine learning models for diagnosing Alzheimer's disease using brain cortical complexity.
Front Aging Neurosci
October 2024
Objective: This study aimed to develop and validate machine learning models (MLMs) to diagnose Alzheimer's disease (AD) using cortical complexity indicated by fractal dimension (FD).
Methods: A total of 296 participants with normal cognitive (NC) function and 182 with AD from the AD Neuroimaging Initiative database were randomly divided into training and internal validation cohorts. Then, FDs, demographic characteristics, baseline global cognitive function scales [Montreal Cognitive Assessment (MoCA), Functional Activities Questionnaire (FAQ), Global Deterioration Scale (GDS), Neuropsychiatric Inventory (NPI)], phospho-tau (p-tau 181), amyloidβ-42/40, apolipoprotein E (APOE) and polygenic hazard score (PHS) were collected to establish multiple MLMs.
Background: Accurate preoperative identification of isocitrate dehydrogenase (IDH) genotypes and tumor subtypes is highly important for proper treatment planning and prognosis evaluation in patients with glioma. This study aimed to differentiate IDH genotypes and tumor subtypes of adult-type diffuse gliomas using histogram features of quantitative susceptibility mapping (QSM) and apparent diffusion coefficient (ADC).
Methods: This prospective study enrolled patients with suspected gliomas between March 2019 and January 2022 in a random series.
Background: To compare the microstructural integrity of the corticospinal tract (CST) between glioma patients with motor epilepsy and without epilepsy using mean apparent propagator magnetic resonance imaging (MAP-MRI).
Methods: A total of 26 patients with glioma adjacent to the CST pathway (10 with motor epilepsy and 16 without epilepsy) and 13 matched healthy controls underwent brain structural and diffusion MRI. The morphological characteristics of the CST (tract volume, tract number, and average length) were extracted, and diffusion parameter values including mean squared displacement (MSD), q-space inverse variance (QIV), return-to-origin probability (RTOP), return-to-axis probabilities (RTAP), return-to-plane probabilities (RTPP), fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) along the CST were evaluated.
Objectives: To evaluate the glioma grade, Ki-67 expression, and IDH-1 mutation status using mean apparent propagator (MAP) MRI.
Methods: Forty enrolled glioma patients underwent structural and diffusion MRI. The diffusion metric values including fractional anisotropy (FA), mean diffusivity (MD), mean squared displacement (MSD), q-space inverse variance (QIV), return-to-origin probability (RTOP), return-to-axis probability (RTAP), and return-to-plane probability (RTPP) in tumor parenchyma (TP) and contralateral normal-appearing white matter (NAWM) were calculated.
Purpose: To evaluate the application of neurite orientation dispersion and density imaging (NODDI) to brain glioma-induced corticospinal tract (CST) injury.
Material And Methods: Twenty-four patients with high-grade glioma (HGG) in or adjacent to the CST pathway and 12 matched healthy subjects underwent structural and diffusion MRI. The CSTs were reconstructed on the both sides.
Objective: To evaluate the application of laplacian-regularized mean apparent propagator (MAPL)-MRI to brain glioma-induced corticospinal tract (CST) injury.
Materials And Methods: This study included 20 patients with glioma adjacent to the CST pathway who had undergone structural and diffusion MRI. The entire CSTs of the affected and healthy sides were reconstructed, and the peritumoral CSTs were manually segmented.