Author profile
Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences
To develop and validate a combined model, based on multi-sequence magnetic resonance imaging (MRI), integrating immune checkpoint molecular markers and multiple independent prognostic factors, to predict overall survival (OS) in nasopharyngeal carcinoma (NPC)...
To investigate the value of CT-based radiomics for preoperative prediction lymph node status of the esophagogastric junction adenocarcinoma.
Development of a deep learning model for accurate preoperative identification of glioblastoma and solitary brain metastases by combining multi-centre and multi-sequence magnetic resonance images and comparison of the performance of different deep learning mod...
This study designed SIB-IMRT plans based on pathologically confirmed positive LNs and compared them with conventional IMRT plans to evaluate the feasibility and dosimetric advantages of SIB-IMRT for rectal cancer patients with metastatic LNs.
To contour the PCMs utilizing MRI/CT image fusion technology and to evaluate the feasibility of PCM-sparing in proton therapy for NPC.
The purpose of this study was to explore the difference between the two plans of intensity modulated radiotherapy (IMRT) for head and neck tumors, which did not limit the dose of pharyngeal constrictor and protected the pharyngeal constrictor, the dose of pha...
The study aims to develop and validate a radiomics model using multiple sequence (MS) -MRI to predict the OS rate in patients diagnosed with NPC.
To investigate and improve the diagnostic performance of preoperative prediction of the Ki-67 expression index using CT-based radiomics.
To explore the value of radiological features extracted from T2-weighted imaging (T 2 WI) images of the parotid gland of patients with nasopharyngeal cancer (NPC) in predicting advanced radiation xerostomia after radiotherapy.
To establish and validate a combined model based on multi-sequence MRI radiomics and clinical features for predicting LN metastasis in rectal adenocarcinoma patients.
Development of a deep learning model for accurate preoperative identification of glioblastoma and solitary brain metastases by combining multi-centre and multi-sequence magnetic resonance images and comparison of the performance of different deep learning mod...
To determine the diagnostic performance of a machine learning model based on radiographic features of fluorine-18-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) / computed tomography (CT) in distinguishing cervical adenocarcinoma (AC) from sq...