Area of research
Radiology, Nuclear Medicine and Imaging · Hepatology
Research interest
Research focused on Receiver operating characteristic and Magnetic resonance imaging, with related work in Colorectal cancer, Hepatocellular carcinoma, Stage (stratigraphy). Notable publications include 'An MRI-based multi-objective radiomics model predicts lymph node status in patients with rectal cancer', 'Deep transfer learning based on magnetic resonance imaging can improve the diagnosis of lymph node metastasis in patients with rectal cancer', and 'Histogram Analysis of Diffusion-Weighted Magnetic Resonance Imaging as a Biomarker to Predict Lymph Node Metastasis in T3 Stage Rectal Carcinoma'.
METnet: A novel deep learning model predicting MET dysregulation in non-small-cell lung cancer on computed tomography images
Clinical prediction of microvascular invasion in hepatocellular carcinoma using an MRI-based graph convolutional network model integrated with nomogram
Cancer Immunotherapy and Medical Imaging Research Trends from 2003 to 2023: A Bibliometric Analysis
Rare Metastatic Embryonal Carcinoma Resembling Lymphoma: A Case Report
Magnetic resonance imaging radiomics modeling predicts tumor deposits and prognosis in stage T3 lymph node positive rectal cancer
LI-RADS Morphological Type Predicts Prognosis of Patients with Hepatocellular Carcinoma After Radical Resection
Clinical-Radiological Characteristic for Predicting Ultra-Early Recurrence After Liver Resection in Solitary Hepatocellular Carcinoma Patients
The influence of neoadjuvant chemoradiotherapy combined with lateral lymph nodes dissection or not on the local recurrence of low to intermediate‐stage II/III rectal cancer
Correction to: Histogram analysis based on multi-parameter MR imaging as a biomarker to predict lymph node metastasis in T3 stage rectal cancer
Deep transfer learning based on magnetic resonance imaging can improve the diagnosis of lymph node metastasis in patients with rectal cancer
Histogram Analysis of Diffusion-Weighted Magnetic Resonance Imaging as a Biomarker to Predict Lymph Node Metastasis in T3 Stage Rectal Carcinoma
Histogram analysis based on multi-parameter MR imaging as a biomarker to predict lymph node metastasis in T3 stage rectal cancer
Rim enhancement on hepatobiliary phase of pre-treatment 3.0 T MRI: A potential marker for early chemotherapy response in colorectal liver metastases treated with XELOX
An MRI-based multi-objective radiomics model predicts lymph node status in patients with rectal cancer
Computational quantitative measures of Gd-EOB-DTPA enhanced MRI hepatobiliary phase images can predict microvascular invasion of small HCC