A5087788685- Radiomics and Machine Learning in Medical Imaging
- Lung Cancer Diagnosis and Treatment
- MRI in cancer diagnosis
- Medical Imaging Techniques and Applications
- Hepatocellular Carcinoma Treatment and Prognosis
- COVID-19 diagnosis using AI
- Advanced MRI Techniques and Applications
- Advanced X-ray and CT Imaging
- AI in cancer detection
- Lung Cancer Treatments and Mutations
- Supramolecular Chemistry and Complexes
- Global Cancer Incidence and Screening
- Molecular Sensors and Ion Detection
- Cerebrovascular and Carotid Artery Diseases
- Medical Imaging and Pathology Studies
- Gastric Cancer Management and Outcomes
- Lanthanide and Transition Metal Complexes
- Liver Disease Diagnosis and Treatment
- Plant-derived Lignans Synthesis and Bioactivity
- Esophageal Cancer Research and Treatment
- Cardiac Imaging and Diagnostics
- Brain Metastases and Treatment
- Lung Cancer Research Studies
- Plasmonic and Surface Plasmon Research
- Metamaterials and Metasurfaces Applications
Shanghai Institute of Hematology
2022-2025
Fudan University
2021-2025
Sun Yat-sen University
2011-2025
The First Affiliated Hospital, Sun Yat-sen University
2021-2025
Zhongshan Hospital
2021-2025
Institute of Forest Ecology, Environment and Protection
2022-2024
Chinese Academy of Forestry
2022-2024
Shanghai Medical College of Fudan University
2024
Anhui University of Technology
2023-2024
Guangdong Ocean University
2024
Lung cancer is the most frequently life-threatening disease and prominent cause of cancer-related mortality among human beings worldwide, where poor early diagnosis expensive detection costs are considered as significant reasons. Here, we try to tackle this issue by proposing a novel label-free low-cost strategy for rapid distinction lung cells relying on plasmonic toroidal metasurfaces at terahertz frequencies. Three disjoint regions displayed in identifiable intensity-frequency diagram,...
Abstract Background and objective Esophageal cancer (EC) is the seventh most prevalent globally sixth leading cause of cancer-related mortality. This study aimed to provide an updated stratified assessment rates in EC incidence, mortality, disability-adjusted life-years (DALYs) from 1990 2021 by sex, age, Socio-demographic Index (SDI) at global, regional, national levels, as well project future trends both regionally. Methods Data about age-standardized (ASRs) incidence (ASIR), mortality...
Identification of novel non-invasive biomarkers is critical for the early diagnosis lung adenocarcinoma (LUAD), especially accurate classification pulmonary nodule. Here, a multiplexed assay developed on an optimized nanoparticle-based laser desorption/ionization mass spectrometry platform sensitive and selective detection serum metabolic fingerprints (SMFs). Integrative SMFs based multi-modal platforms are constructed LUAD The dual modal model, with protein tumor marker neural network...
Purpose Pulmonary nodules are a potential manifestation of lung cancer. In computer‐aided diagnosis (CAD) cancer, it is great significance to extract the complete boundary pulmonary in computed tomography (CT) scans accurately. It can provide doctors with important information such as tumor size and density, which assist subsequent treatment. addition this, molecular subtype radiomics segmentation also plays pivotal role. Existing methods difficult use only one model simultaneously treat...
Background Three-dimensional (3D) time-of-flight (TOF) MR angiography (MRA) at 7 T has been reported to have high image quality for visualizing small perforating vessels. However, B1 inhomogeneity and more physiologic considerations limit its applications. Angiography 5 may provide another choice intracranial vascular imaging. Purpose To evaluate the cerebrovascular visualization of 5-T 3D TOF MRA branch arteries. Materials Methods Participants (healthy volunteers or participants with a...
Computed tomography pulmonary angiography (CTPA) is the gold standard for diagnosis of embolism (PE). The semi-quantitative clot burden scoring based on imaging related to risk stratification and prognosis acute PE, but it cannot be widely applied in clinic due difficulty calculation. This study developed a high-quality VB-Net deep learning (DL) model combined with Transformer, which can detect PE from images automatically calculate score (CBS). aim this was help patients via earlier...
To investigate associations between breast cancer molecular subtype and intravoxel incoherent motion imaging (IVIM) amide proton transfer-weighted (APTw). This prospective study involved 264 patients with suspected tumors who underwent both APTw IVIM MRI. The maximum diameter of the tumor (Dmax), APT value, apparent diffusion coefficient (ADC), (D), pseudo (D*), perfusion fraction (f) values along histological subtype, grade, prognostic factors (Ki-67, estrogen receptor (ER), progesterone...
Abstract Background Invasive lung adenocarcinoma (LUAD) with the high‐grade patterns (HGPs) has potential for rapid metastasis and frequent recurrence. Therefore, accurately predicting presence of components is crucial doctors to develop personalized treatment plans improve patient prognosis. Purpose To a CNN–transformer fusion network based on radiomics clinical information HGPs LUAD. Methods A total 288 lesions in patients pathologically confirmed invasive LUAD were enrolled. Firstly,...
Tumor-targeted immunotherapy is a remarkable breakthrough, offering the inimitable advantage of specific tumoricidal effects with reduced immune-associated cytotoxicity. However, existing platforms suffer from low efficacy, inability to induce strong immunogenic cell death (ICD), and restrained capacity transforming immune-deserted tumors into immune-cultivated ones. Here, an innovative platform, perfluorooctyl bromide (PFOB) nanoemulsions holding MnO2 nanoparticles (MBP), was developed...
The purpose of this study was to develop a predictive model that could accurately predict the malignancy pulmonary ground-glass nodules (GGNs) and invasiveness malignant GGNs.The authors built two binary classification models GGNs GGNs.Results our developed showed random forest achieve 95.1% accuracy 83.0% be predicted by forest.
Background: Risk prediction models of lung nodules have been built to alleviate the heavy interpretative burden on clinicians. However, malignancy scores output by those can be difficult interpret in a clinically meaningful manner. In contrast, modeling nodule growth may more readily useful. This study developed CT-based visual forecasting system that visualize and quantify three dimensions (3D) any future time point using follow-up CT scans. Methods: We retrospectively included 246 patients...
Radiogenomics investigates radiographic imaging phenotypes associated with gene expression patterns. This study aims to explore relationships between CT radiomics features and data in non-small cell lung cancer (NSCLC).Eighty-nine NSCLC patients are included the study. Radiomics extracted selected quantify phenotype of tumors on CT-scans. Co-expressed genes also clustered first principal component cluster is represented, which defined as a metagene. Then, statistical analysis was performed...
Objective: To investigate the utility of pre-immunotherapy contrast-enhanced CT-based texture classification in predicting response to non-small cell lung cancer (NSCLC) immunotherapy treatment. Methods: Sixty-three patients with 72 lesions who received were enrolled this study. We extracted textures including histogram, absolute gradient, run-length matrix, gray-level co-occurrence autoregressive model, and wavelet transform from CT by using Mazda software. Three different methods, namely,...
In order to assist doctors in arranging the postoperative treatments and re-examinations for non-small cell lung cancer (NSCLC) patients, this study was initiated explore a prognostic analysis method NSCLC based on computed tomography (CT) radiomics. The data of 173 patients were collected retrospectively clinically meaningful 3-year survival used as predictive limit predict patient's prognosis time range. Firstly, tumors segmented radiomics features extracted. Secondly, feature weighting...
Radiomics models based on computed tomography (CT) can be used to differentiate invasive ground-glass nodules (GGNs) in lung adenocarcinoma help determine the optimal timing of GGN resection, improve accuracy prognostic prediction, and reduce unnecessary surgeries. However, general radiomics does not fully utilize follow-up data often lacks model interpretation. Therefore, this study aimed build an interpretable delta predict invasiveness.