5 results match your criteria: "The Affiliated Jiangmen Hospital of Sun Yat-sen University[Affiliation]"
Contrast-enhanced CT in the differential diagnosis of bladder cancer and paraganglioma.
Abdom Radiol (NY)
May 2024
Department of Radiology, Rehabilitation Hospital of China National Nuclear Corporation, Number 120 Jinjiang Road, Yuelu District, Changsha, 410017, Hunan Province, China.
Article Synopsis
- This study aimed to highlight the effectiveness of contrast-enhanced computed tomography (CECT) in distinguishing between bladder paraganglioma (BPG) and bladder cancer.
- Researchers analyzed medical records from 19 patients with BPG and 56 patients with bladder cancer, focusing on imaging results from both unenhanced and contrast-enhanced CT scans.
- Key findings revealed significant differences in patient age, tumor characteristics, and specific CT values, indicating that certain CECT measurements can effectively help differentiate between BPG and bladder cancer, particularly in the corticomedullary phase.
[Corrigendum] Lentivirus‑mediated RNA interference of clusterin enhances the chemosensitivity of EJ bladder cancer cells to epirubicin .
Mol Med Rep
September 2021
State Key Laboratory of Oncology in South China, Cancer Center, Sun Yat-sen University, Guangzhou 510080, P.R. China.
Subsequently to the publication of the above paper, the authors have realized that Fig. 2A in this paper contained an error. The image selected to represent the experiment showing the invasion ability of EJ cells in the epirubicine/LV‑NC group of Fig.
View Article and Find Full Text PDFSegmentation of small ground glass opacity pulmonary nodules based on Markov random field energy and Bayesian probability difference.
Biomed Eng Online
June 2020
The Department of Radiology, The Affiliated Jiangmen Hospital of Sun Yat-sen University, Jiangmen, 529000, China.
Background: Image segmentation is an important part of computer-aided diagnosis (CAD), the segmentation of small ground glass opacity (GGO) pulmonary nodules is beneficial for the early detection of lung cancer. For the segmentation of small GGO pulmonary nodules, an integrated active contour model based on Markov random field energy and Bayesian probability difference (IACM_MRFEBPD) is proposed in this paper.
Methods: First, the Markov random field (MRF) is constructed on the computed tomography (CT) images, then the MRF energy is calculated.
Radiomics nomogram for preoperative differentiation of lung tuberculoma from adenocarcinoma in solitary pulmonary solid nodule.
Eur J Radiol
July 2020
The Department of Radiology, The Affiliated Jiangmen Hospital of Sun Yat-sen University, Jiangmen, Guangdong Province, China. Electronic address:
Purpose: To investigate the preoperative differential diagnostic performance of a radiomics nomogram in tuberculous granuloma (TBG) and lung adenocarcinoma (LAC) appearing as solitary pulmonary solid nodules (SPSNs).
Method: We retrospectively recruited 426 patients with SPSNs from two centers and assigned them to training (n = 123), internal validation (n = 121), and external validation cohorts (n = 182). A model of deep learning (DL) was built for tumor segmentation from routine computed tomography (CT) images and extraction of 3D radiomics features.
Differentiating minimally invasive and invasive adenocarcinomas in patients with solitary sub-solid pulmonary nodules with a radiomics nomogram.
Clin Radiol
July 2019
The Department of Radiology, The Affiliated Jiangmen Hospital of Sun Yat-sen University, Jiangmen City, Guangdong Province, China. Electronic address:
Aim: To evaluate the preoperative differentiation between the minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IAC) in patients with sub-solid pulmonary nodules using a radiomics nomogram.
Materials And Methods: A total of 100 patients with sub-solid pulmonary nodules who had pathologically confirmed MIA (43 patients, 13 male and 30 female) or IAC (57 patients, 26 male and 31 female) were recruited retrospectively. Radiomics features were extracted from computed tomography (CT) images.