A5100714002- Radiomics and Machine Learning in Medical Imaging
- Medical Image Segmentation Techniques
- AI in cancer detection
- Advanced Neural Network Applications
- Retinal Imaging and Analysis
- MicroRNA in disease regulation
- Cancer-related molecular mechanisms research
- Dental Radiography and Imaging
- Circular RNAs in diseases
- Medical Imaging Techniques and Applications
- 3D Shape Modeling and Analysis
- COVID-19 diagnosis using AI
- Medical Imaging and Analysis
- Brain Tumor Detection and Classification
- Computer Graphics and Visualization Techniques
- Advanced X-ray and CT Imaging
- Advanced Vision and Imaging
- Domain Adaptation and Few-Shot Learning
- Fetal and Pediatric Neurological Disorders
- RNA modifications and cancer
- Retinal and Optic Conditions
- Lung Cancer Diagnosis and Treatment
- Distributed and Parallel Computing Systems
- Advanced Numerical Analysis Techniques
- Cancer, Hypoxia, and Metabolism
Shanghai Jiao Tong University
2016-2025
Guangxi University
2016-2024
Jingning County People's Hospital
2024
Jinan University
2015-2024
ShenZhen People’s Hospital
2015-2024
Institute of Infection and Immunity
2024
University of Ottawa
2024
Ottawa Hospital
2024
Ottawa Hospital Research Institute
2024
Southern University of Science and Technology
2022-2024
Acute myeloid leukemia (AML) carrying NPM1 mutations and cytoplasmic nucleophosmin (NPMc+ AML) accounts for about one-third of adult AML shows distinct features, including a unique gene expression profile. MicroRNAs (miRNAs) are small noncoding RNAs 19-25 nucleotides in length that have been linked to the development cancer. Here, we investigated role miRNAs biology NPMc+ AML. The miRNA was evaluated 85 de novo patients characterized subcellular localization/mutation status FLT3 using custom...
This paper relates the post-analysis of first edition HEad and neCK TumOR (HECKTOR) challenge. challenge was held as a satellite event 23rd International Conference on Medical Image Computing Computer-Assisted Intervention (MICCAI) 2020, its kind focusing lesion segmentation in combined FDG-PET CT image modalities. The challenge's task is automatic Gross Tumor Volume (GTV) Head Neck (H&N) oropharyngeal primary tumors FDG-PET/CT images. To this end, participants were given training set 201...
Abstract Background To develop and validate a survival model with clinico-biological features 18 F- FDG PET/CT radiomic via machine learning, for predicting the prognosis from primary tumor of colorectal cancer. Methods A total 196 pathologically confirmed patients cancer (stage I to stage IV) were included. Preoperative clinical factors, serum markers, included recurrence-free analysis. For modeling validation, randomly divided into training (n = 137) validation 59) set, while 78 III...
In-utero fetal MRI is emerging as an important tool in the diagnosis and analysis of developing human brain. Automatic segmentation brain a vital step quantitative prenatal neurodevelopment both research clinical context. However, manual cerebral structures time-consuming prone to error inter-observer variability. Therefore, we organized Fetal Tissue Annotation (FeTA) Challenge 2021 order encourage development automatic algorithms on international level. The challenge utilized FeTA Dataset,...
The decrease in the copy number of mitochondrial DNA (mtDNA) cancer tissues might be associated with a oxidative mtDNA damage to achieve immortalization and progression. Lung specimens were collected from 29 patients stage III non-small cell lung (NSCLC) after neoadjuvant chemotherapy followed by surgical resection. relative (formation 8-OHdG mtDNA) each tissue measured quantitative real-time PCR. Seven female 22 male patients, mean age 63.5 years evaluated. Tumors five became progressive,...
Segmentation of colorectal cancerous regions from 3-D magnetic resonance (MR) images is a crucial procedure for radiotherapy. Automatic delineation whole volumes in urgent demand yet very challenging. Drawbacks existing deep-learning-based methods this task are two-fold: 1) extensive graphics processing unit (GPU) memory footprint tensor limits the trainable volume size, shrinks effective receptive field, and therefore, degrades speed segmentation performance 2) in-region supported by...
Identification of epidermal growth factor receptor (EGFR) mutation types is crucial before tyrosine kinase inhibitors (TKIs) treatment. Radiomics a new strategy to noninvasively predict the genetic status cancer. In this study, we aimed develop predictive model based on 18F-fluorodeoxyglucose positron emission tomography-computed tomography (18F-FDG PET/CT) radiomic features identify specific EGFR subtypes.We retrospectively studied 18F-FDG PET/CT images 148 patients with isolated lung...
Detecting various types of cells in and around the tumor matrix holds a special significance characterizing micro-environment for cancer prognostication research. Automating tasks detecting, segmenting, classifying nuclei can free up pathologists' time higher value reduce errors due to fatigue subjectivity. To encourage computer vision research community develop test algorithms these tasks, we prepared large diverse dataset nucleus boundary annotations class labels. The has over 46,000 from...
Accurate tumour response prediction to targeted therapy allows for personalised conversion patients with unresectable colorectal cancer liver metastases (CRLM). In this study, we aimed develop and validate a multi-modal deep learning model predict the efficacy of bevacizumab in initially CRLM using baseline PET/CT, clinical data, colonoscopy biopsy specimens.
Temporal knowledge graph (TKG) reasoning that infers future missing facts is an essential and challenging task. Predicting events typically relies on closely related historical facts, yielding more accurate results for repetitive or periodic events. However, with sparse interactions, the effectiveness of this method, which focuses leveraging high-frequency information, diminishes. Recently, capabilities diffusion models in image generation have opened new opportunities TKG reasoning....
miRNA-486 (miR-486) was first identified from the human fetal liver cDNA library and considered to be associated with hepatocellular carcinoma (HCC) development. Its roles in regulation of HCC metastasis chemosensitivity have not been explored yet.miR-486 expression tissues, cell lines serum evaluated by real-time polymerase chain reaction. miR-486 overexpression or downregulation SMMC-7721/LM3 conducted lentivirus transfection. Cell proliferation, migration apoptosis were quantitated using...
MicroRNA-34a (miR-34a) is a direct transcriptional target of p53, and downregulated in several different types cancer. However, the underlying mechanism miR-34a effects colorectal cancer not well understood. In this study, we explored role cell invasion, migration, apoptosis. Transient overexpression SW480 cells caused severe decrease migration invasion (both, p<0.05) compared to control groups. Combining transfection with 5-fluorouracil (5-FU) treatment further enhanced inhibition 5-FU...
Lung cancer and colorectal account for over one-third of all deaths in the United States. MicroRNA-301a (miR-301a) is an activator both nuclear factor-κB (NF-κB) Stat3, overexpressed deadly malignancies. In this work, we show that genetic ablation miR-301a reduces Kras-driven lung tumorigenesis mice. And deficiency protects animals from dextran sodium sulfate-induced colon inflammation colitis-associated carcinogenesis. We also demonstrate deletion bone marrow-derived cells attenuates tumor...