A5073869620- Advanced Neuroimaging Techniques and Applications
- Glioma Diagnosis and Treatment
- Advanced MRI Techniques and Applications
- Functional Brain Connectivity Studies
- Traumatic Brain Injury Research
- Radiomics and Machine Learning in Medical Imaging
- MRI in cancer diagnosis
- Ferroptosis and cancer prognosis
- Nanoparticle-Based Drug Delivery
- Cardiac Arrest and Resuscitation
- Acute Ischemic Stroke Management
- Advanced X-ray and CT Imaging
- Traumatic Brain Injury and Neurovascular Disturbances
- Cancer Cells and Metastasis
- Radioactivity and Radon Measurements
- Body Composition Measurement Techniques
- Intracerebral and Subarachnoid Hemorrhage Research
- Medical and Biological Ozone Research
- Cancer Immunotherapy and Biomarkers
- Immune cells in cancer
- Advanced Electron Microscopy Techniques and Applications
- Global Cancer Incidence and Screening
- Bone and Joint Diseases
- Colorectal Cancer Screening and Detection
- Optical Imaging and Spectroscopy Techniques
Taipei Medical University
2020-2025
Taipei Medical University Hospital
2020-2025
National Taiwan University
2017-2020
Taipei Medical University-Shuang Ho Hospital
2020
Fu Jen Catholic University
2019
Characterization of immunophenotypes in glioblastoma (GBM) is important for therapeutic stratification and helps predict treatment response prognosis. Radiomics can be used to molecular subtypes gene expression levels. However, whether radiomics aids immunophenotyping prediction still unknown. In this study, classify patients with GBM, we developed machine learning-based magnetic resonance (MR) radiomic models evaluate the enrichment levels four immune subsets: Cytotoxic T lymphocytes...
Abstract Background Glioblastoma multiforme (GBM) is an aggressive brain tumor with chemoresistant, immunosuppressive, and invasive properties. Despite standard therapies, including surgery, radiotherapy, temozolomide (TMZ) chemotherapy, tumors inevitably recur in the peritumoral region. Targeting GBM-mediated immunosuppressive properties a promising strategy to improve clinical outcomes. Methods We utilized genomic data from Taiwan GBM cohort The Cancer Genome Atlas (TCGA) analyze RNA...
Research has failed to resolve the dilemma experienced by localized prostate cancer patients who must choose between radical prostatectomy (RP) and external beam radiotherapy (RT). Because Charlson Comorbidity Index (CCI) is a measurable factor that affects survival events, this research seeks validate potential of CCI improve accuracy various prediction models. Thus, we employed Cox proportional hazard model machine learning methods, including random forest (RF) support vector (SVM), data...
Purpose: Targeted superparamagnetic iron oxide (SPIO) nanoparticles are a promising tool for molecular magnetic resonance imaging (MRI) diagnosis. Lipid-coated SPIO have nonfouling property that can reduce nonspecific binding to off-target cells and prevent agglomeration, making them suitable contrast agents MRI PD-L1 is poor prognostic factor patients with glioblastoma. Most recurrent glioblastomas temozolomide resistant. Diagnostic probes targeting could facilitate early diagnosis be used...
The molecular heterogeneity of gene expression profiles glioblastoma multiforme (GBM) are the most important prognostic factors for tumor recurrence and drug resistance. Thus, aim this study was to identify potential target genes related temozolomide (TMZ) resistance GBM recurrence. genomic data patients with from Cancer Genome Atlas (TCGA; 154 primary 13 recurrent tumors) a local cohort (29 4 tumors), samples different regions 25 peritumoral regions), Gene Expression Omnibus (GSE84465,...
Concussion, also known as mild traumatic brain injury (mTBI), commonly causes transient neurocognitive symptoms, but in some cases, it cognitive impairment, including working memory (WM) deficit, which can be long-lasting and impede a patient's return to work. The predictors of long-term outcomes following mTBI remain unclear, because abnormality is often absent structural imaging findings. Previous studies have demonstrated that WM functional activity estimated from magnetic resonance...
The functional connectivity of the default-mode network (DMN) monitored by magnetic resonance imaging (fMRI) in Alzheimer's disease (AD) patients has been found weaker than that healthy participants. Since breathing and heart beating can cause fluctuations fMRI signal, these physiological activities may affect data differently between AD We collected resting-state from age-matched With concurrent cardiac respiratory recordings, we estimated both responses phase-locked non-phase-locked to...
We propose a flexible form-fittingMRI receiver coil array assembledby individualcoilmodules. This design targetsMRI applications requiring conforming to the anatomy of various shapes or sizes. Coil modules in our proposed were arranged with gaps between them. Each module had circumferential shielding structure stacked on top coil. Together they achieve robust decoupling when was bent differently. Two types investigatedby using full-wave electromagnetic simulations and imaging experiments....
<h3>BACKGROUND AND PURPOSE:</h3> Phase imaging helps determine a lesion's susceptibility. However, various inhomogenous phase patterns could be observed in the serial images of lesion and render image interpretation challenging. We evaluated diagnostic accuracy differentiating cerebral microbleeds calcifications from axial locations. <h3>MATERIALS METHODS:</h3> This study retrospectively enrolled 31 consecutive patients undergoing both CT MR for acute infarction exhibiting dark spots...
Neuronal activation sequence information is essential for understanding brain functions. Extracting such timing from blood-oxygenation-level-dependent functional magnetic resonance imaging (fMRI) signals confounded by local cerebral vascular reactivity (CVR), which varies across locations. Thus, detecting neuronal synchrony as well inferring inter-regional causal modulation using fMRI can be biased. Here we used fast measurements sampled at 10 Hz to measure the latency difference between...
Abstract Background This study investigates the potential of diffusion tensor imaging (DTI) in identifying penumbral volume (PV) compared to standard gadolinium-required perfusion–diffusion mismatch (PDM), utilizing a stack-based ensemble machine learning (ML) approach with enhanced explainability. Methods Sixteen male rats were subjected middle cerebral artery occlusion. The penumbra was identified using PDM at 30 and 90 min after We used 11 DTI-derived metrics 14 distance-based features...
This paper introduces CompressedMediQ, a novel hybrid quantum-classical machine learning pipeline specifically developed to address the computational challenges associated with high-dimensional multi-class neuroimaging data analysis. Standard datasets, such as large-scale MRI from Alzheimer's Disease Neuroimaging Initiative (ADNI) and in Frontotemporal Dementia (NIFD), present significant hurdles due their vast size complexity. CompressedMediQ integrates classical high-performance computing...
Motivation: Addressing persistent working-memory decline (PWMD) in concussion patients is crucial, but prognostic methods are limited. This study explores the potential of glymphatic system as a novel biomarker. Goal(s): Determine if early measurement dysfunction within 1 month post-concussion can predict PWMD. Approach: A 1-year prospective observational was conducted, assessing function, microhemorrhage, sleep quality, and neurocognitive tests 1-month injury. Results: Significant...
Abstract Background : Targeted superparamagnetic iron oxide (SPIO) nanoparticles are a promising tool for molecular magnetic resonance imaging (MRI) diagnosis. Lipid-coated SPIO have nonfouling property that can reduce nonspecific binding to off-target cells and prevent agglomeration, making them suitable contrast agents MRI PD-L1 is poor prognostic factor patients with glioblastoma. Most recurrent glioblastomas temozolomide-resistant. Diagnostic probes targeting could help early diagnosis...
We propose a multi-output segmentation approach, which incorporates other non-lesion brain tissue maps into the additional output layers to force model learn more about lesion and characteristics. construct cross-vendor study by training white matter hyperintensities on cases collected from one vendor testing performance eight different data sets. The can be significantly improved, especially in sets shows low image contrast similarity with data, suggesting feasibility of incorporating...
The objective of this study is to construct a framework for precise individualized prediction post-concussive cognitive outcomes based on the early fMRI and neuropsychological biomarkers assessed at baseline facilitate therapeutic intervention rehabilitation strategies. Satisfactory predictions can be achieved patients whose WM function did not recover after 3 months (accuracy = 87.5%), 6 83.3%), 1 year 83.3%). results prove feasibility using machine learning&ndash;based approaches...
Concussion, also known as mild traumatic brain injury (mTBI), commonly causes transient neurocognitive symptoms, but in some cases, it cognitive impairment, including working memory (WM) deficit, which can be long-lasting and impede a patient&rsquo;s return to work. The predictors of long-term outcomes following mTBI remain unclear because abnormality is often absent structural imaging findings. purpose the study was determine whether machine learning-based models using functional...