Area of research
Radiology, Nuclear Medicine and Imaging · Pulmonary and Respiratory Medicine
Research interest
Research focused on Receiver operating characteristic and Nuclear medicine, with related work in Nomogram, Lung cancer, Positron emission tomography. Notable publications include 'Development and validation of CT-based radiomics deep learning signatures to predict lymph node metastasis in non-functional pancreatic neuroendocrine tumors: a multicohort study', 'The Machine Learning Model for Distinguishing Pathological Subtypes of Non-Small Cell Lung Cancer', and 'Using tumor habitat-derived radiomic analysis during pretreatment 18F-FDG PET for predicting KRAS/NRAS/BRAF mutations in colorectal cancer'.
Comparison of 18F-FDG PET image quality and quantitative parameters between DPR and OSEM reconstruction algorithm in patients with lung cancer
18F-FAPI-04 PET/CT in the evaluation of ground-glass nodules less than 3 cm in diameter comparing to 18F-FDG PET/CT
The value of habitat analysis based on 18F-PSMA-1007 PET/CT images for prostate cancer risk grading
Using tumor habitat-derived radiomic analysis during pretreatment 18F-FDG PET for predicting KRAS/NRAS/BRAF mutations in colorectal cancer
Development and validation of CT-based radiomics deep learning signatures to predict lymph node metastasis in non-functional pancreatic neuroendocrine tumors: a multicohort study
Non-invasively Discriminating the Pathological Subtypes of Non-small Cell Lung Cancer with Pretreatment 18F-FDG PET/CT Using Deep Learning
Clinical application of Al18F-NOTA-FAPI PET/CT in diagnosis and TNM staging of pancreatic adenocarcinoma, compared to 18F-FDG
The Machine Learning Model for Distinguishing Pathological Subtypes of Non-Small Cell Lung Cancer
A nomogram model based on MRI and radiomic features developed and validated for the evaluation of lymph node metastasis in patients with rectal cancer
A Nomogram Model Developed and Validated for The Evaluation of Lymph Node Metastasis in Patients with Rectal Cancer
Prediction model based on 18F-FDG PET/CT radiomic features and clinical factors of EGFR mutations in lung adenocarcinoma
18F-FDG texture analysis predicts the pathological Fuhrman nuclear grade of clear cell renal cell carcinoma