Publications by authors named "Guang Wu Lin"
Article Synopsis
- The study investigates how well multiparametric MRI-based deep learning models can distinguish between triple-negative breast cancer (TNBC) and fibroadenomas in BI-RADS 4 lesions.
- The research involved 319 female patients and employed training, validation, and testing sets to assess the diagnostic performance of radiologists with and without AI support.
- Results showed that the AI combined diagnosis significantly improved the accuracy of junior radiologists and achieved high AUC scores, demonstrating its effectiveness in aiding cancer diagnosis.
Objectives: Moyamoya disease patients with hemorrhagic stroke usually have a poor prognosis. This study aimed to determine whether hemorrhagic moyamoya disease could be distinguished from MRA images using transfer deep learning and to screen potential regions that contain rich distinguishing information from MRA images in moyamoya disease.
Materials And Methods: A total of 116 adult patients with bilateral moyamoya diseases suffering from hemorrhagic or ischemia complications were retrospectively screened.
Objective: To construct a MRI radiomics model and help radiologists to improve the assessments of pelvic lymph node metastasis (PLNM) in endometrial cancer (EC) preoperatively.
Methods: During January 2014 and May 2019, 622 EC patients (age 56.6 ± 8.
Background: High- and low-risk endometrial cancer (EC) differ in whether lymphadenectomy is performed. Assessment of high-risk EC is essential for planning surgery appropriately.
Purpose: To develop a radiomics nomogram for high-risk EC prediction preoperatively.
Background: Primary hepatic neuroendocrine tumors (PHNET) are extremely rare and difficult to distinguish from primary and metastatic liver cancers since PHNETs blood supply comes from the liver artery. This study aims to investigate CT and MR imaging findings of primary hepatic neuroendocrine tumor (PHNET) and correlation with the 2010 WHO pathological classification.
Methods: We examined CT and MRI scans from 29 patients who were diagnosed with PHNET and correlated the data with the 2010 WHO classification of neuroendocrine tumors.
Objective: Residual tumor and fibrosis are commonly observed with magnetic resonance (MR) imaging following radiotherapy for nasopharyngeal carcinoma (NPC). Therefore, MR images of NPC following treatment with radiotherapy were retrospectively analyzed to evaluate whether post-radiation changes associated with residual tumors, recurrent tumors, and fibrosis could be distinguished 1 month and 3-6 months after treatment.
Methods: MR images were analyzed for 108 patients who completed radiotherapy for NPC and underwent 5-years of follow-up.
Background: A solitary necrotic nodule (SNN) of the liver is an uncommon lesion, which is different from primary and metastatic liver cancers.
Objectives: To analyze the classification, CT and MR manifestation, and the pathological basis of solitary necrotic nodule of the liver (SNN) in order to evaluate CT and MRI as a diagnosing tool.
Patients And Methods: This study included 29 patients with liver SNNs, out of which 14 had no clinical symptoms and were discovered by routine ultrasound examinations, six were found by computed tomography (CT) due to abdominal illness, four had ovarian tumors, and five had gastrointestinal cancer surgeries, previously.