Area of research
Radiology, Nuclear Medicine and Imaging · Hepatology
Research interest
Research focused on Hepatocellular carcinoma and Radiomics, with related work in Radiology, Receiver operating characteristic, Nomogram. Notable publications include 'Preoperative Diagnosis of Dual‐Phenotype Hepatocellular Carcinoma Using Enhanced MRI Radiomics Models', 'MRI-based deep learning radiomics to differentiate dual-phenotype hepatocellular carcinoma from HCC and intrahepatic cholangiocarcinoma: a multicenter study', and 'Deep Learning Radiopathomics Models Based on Contrast-enhanced MRI and Pathologic Imaging for Predicting Vessels Encapsulating Tumor Clusters and Prognosis in Hepatocellular...'.
MRI-based deep learning radiomics to differentiate dual-phenotype hepatocellular carcinoma from HCC and intrahepatic cholangiocarcinoma: a multicenter study
Deep Learning Radiopathomics Models Based on Contrast-enhanced MRI and Pathologic Imaging for Predicting Vessels Encapsulating Tumor Clusters and Prognosis in Hepatocellular Carcinoma
<scp>MRI</scp> ‐Based Score to Predict Retreatment Response for Viable Hepatocellular Carcinomas After Transarterial Chemoembolization
MRI-Based Models Using Habitat Imaging for Predicting Distinct Vascular Patterns in Hepatocellular Carcinoma
Gd-EOB-DTPA-enhanced MRI radiomics and deep learning models for predicting the pathological differentiation degree in hepatocellular carcinoma
Habitat radiomics and deep learning on gadoxetic acid-enhanced MRI for noninvasive assessment of CK19 expression and recurrence-free survival in hepatocellular carcinoma
Clinical‑imaging‑radiomic nomogram based on unenhanced CT effectively predicts adrenal metastases in patients with lung cancer with small hyperattenuating adrenal incidentalomas
Adrenal indeterminate nodules: CT-based radiomics analysis of different machine learning models for predicting adrenal metastases in lung cancer patients
Preoperative Diagnosis of Dual‐Phenotype Hepatocellular Carcinoma Using Enhanced <scp>MRI</scp> Radiomics Models