Area of research
Radiation · Radiology, Nuclear Medicine and Imaging
Research interest
Research focused on Segmentation and Radiomics, with related work in Hepatocellular carcinoma, Nomogram, Radiation therapy. Notable publications include 'MMFNet: A multi-modality MRI fusion network for segmentation of nasopharyngeal carcinoma', 'Reproducibility and non-redundancy of radiomic features extracted from arterial phase CT scans in hepatocellular carcinoma patients: impact of tumor segmentation variability', and 'Patient-specific daily updated deep learning auto-segmentation for MRI-guided adaptive radiotherapy'.
Machine Learning Radiomics for Predicting Response to MR-Guided Radiotherapy in Unresectable Hepatocellular Carcinoma: A Multicenter Cohort Study
Development and validation of a 18F-FDG PET/CT radiomics nomogram for predicting progression free survival in locally advanced cervical cancer: a retrospective multicenter study
Prognostic nomogram combining 18F-FDG PET/CT radiomics and clinical data for stage III NSCLC survival prediction
Dose-Painting Proton Radiotherapy Guided by Functional MRI in Non-enhancing High-Grade Gliomas
Patient-specific daily updated deep learning auto-segmentation for MRI-guided adaptive radiotherapy
Technical Note: End‐to‐end verification of an MR‐Linac using a dynamic motion phantom
MMFNet: A multi-modality MRI fusion network for segmentation of nasopharyngeal carcinoma
Reproducibility and non-redundancy of radiomic features extracted from arterial phase CT scans in hepatocellular carcinoma patients: impact of tumor segmentation variability
Prognostic Value of Texture Analysis Based on Pretreatment DWI-Weighted MRI for Esophageal Squamous Cell Carcinoma Patients Treated With Concurrent Chemo-Radiotherapy
A survey of clinical application of image-guided radiotherapy in North China