A5101654042- Radiomics and Machine Learning in Medical Imaging
- MRI in cancer diagnosis
- Advanced X-ray and CT Imaging
- Medical Imaging Techniques and Applications
- Human-Automation Interaction and Safety
- Medical Imaging and Analysis
- Gaze Tracking and Assistive Technology
- Advanced MRI Techniques and Applications
- Lung Cancer Diagnosis and Treatment
- Probabilistic and Robust Engineering Design
- Cardiovascular Disease and Adiposity
- Evacuation and Crowd Dynamics
- Colorectal and Anal Carcinomas
- Gastric Cancer Management and Outcomes
- Advanced Mathematical Modeling in Engineering
- Model Reduction and Neural Networks
- Head and Neck Cancer Studies
- Spatial Cognition and Navigation
- Lanthanide and Transition Metal Complexes
- Electron Spin Resonance Studies
Chinese Academy of Medical Sciences & Peking Union Medical College
2021-2024
Peking Union Medical College Hospital
2024
Minzu University of China
2024
University of Louisville Hospital
2024
Shenzhen Institutes of Advanced Technology
2021
Chinese Academy of Sciences
2021
Chemical exchange saturation transfer (CEST) MRI, versatile for detecting endogenous mobile proteins and tissue pH, has proved valuable in tumor imaging. However, CEST MRI scans are often performed under non-equilibrium conditions, which confound characterization. This study proposed a quasi-steady-state (QUASS) algorithm to standardize fast accurate imaging at 3 T. The signal evolution was modeled by longitudinal relaxation rate during delay (Td) spinlock RF time (Ts), from the QUASS effect...
Accurate pretreatment prediction for disease progression of nasopharyngeal carcinoma is key to intensify therapeutic strategies high-risk individuals. Our aim was evaluate the value baseline MRI-based radiomics machine-learning models in predicting patients who achieved complete response after treatment.In this retrospective study, 171 with pathologically confirmed were included. Using hold-out cross validation scheme (7:3), relevant radiomic features selected least absolute shrinkage and...
Abstract Purpose To explore the value of MRI-based radiomics features in predicting risk disease progression for nasopharyngeal carcinoma (NPC). Methods 199 patients confirmed with NPC were retrospectively included and then divided into training validation set using a hold-out (159: 40). Discriminative radiomic selected Wilcoxon signed-rank test from tumors normal masticatory muscles 37 patients. LASSO Cox regression Pearson correlation analysis applied to further confirm differential...
Abstract Background To investigate the usefulness of radiomics analysis based on voxel-wise mapping DCE-MRI time-intensity-curve (TIC) profiles in quantifying temporal and spatial hemodynamic heterogeneity. Methods From December 2018 to August 2022, 428 patients with 639 breast lesions were retrospectively enrolled. The TIC profile each voxel within manually segmented 3D lesion was categorized into 19 subtypes wash-in rate (nonenhanced, slow, medium, fast), wash-out enhancement (persistent,...
We propose a novel model-free and data-driven approach, i.e., voxel-wise composition ratio on 19 dynamic contrast-enhanced MRI (DCE-MRI) time-intensity curve (TIC) profiles (Type-19) to visualize quantify spatial hemodynamic heterogeneity. The proposed quantitative method for breast tumor was evaluated compared with the two existing methods (qualitative semi-quantitative methods) in 4 different clinical applications. In distinguishing malignancy cancer lesions predicting proliferation...
Motivation: An effective approach that enables simultaneous quantification of spatial and temporal heterogeneity based on DCE-MRI is lacking. Goal(s): To develop a data-driven model-free to quantify hemodynamic heterogeneity. Approach: We introduced radiomics analysis voxelwise mapping time-intensity-curve (TIC) profiles investigated its value in differentiating malignant benign breast lesions. Results: Radiomics features composition ratio TIC showed good performance Impact: provide novel...