Publications by authors named "Xinxiang Zhou"
Background: Poly (ADP-ribose) polymerase inhibitors (PARPi) serve as crucial therapeutic agents in solid tumor treatment. Preclinical investigations suggest a potential protective function of PARPi against endocrine and metabolic impairments. Nevertheless, the existing body of evidence remains inconclusive on this aspect.
View Article and Find Full Text PDFBackground: To explore whether the combination of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) and nonmono-exponential (NME) model-based diffusion-weighted imaging (DWI) via deep neural network (DNN) can improve the prediction of breast cancer molecular subtypes compared to either imaging technique used alone.
Patients And Methods: This prospective study examined 480 breast cancers in 475 patients undergoing DCE-MRI and NME-DWI at 3.0 T.
Background: Dynamic contrast-enhanced (DCE) MRI commonly outperforms diffusion-weighted (DW) MRI in breast cancer discrimination. However, the side effects of contrast agents limit the use of DCE-MRI, particularly in patients with chronic kidney disease.
Purpose: To develop a novel deep learning model to fully exploit the potential of overall b-value DW-MRI without the need for a contrast agent in predicting breast cancer molecular subtypes and to evaluate its performance in comparison with DCE-MRI.
Article Synopsis
- The study compares the effectiveness of two imaging techniques—Dynamic Contrast-Enhanced MRI (DCE-MRI) and Non-Mono-Exponential Model-Based Diffusion-Weighted Imaging (NME-DWI)—in predicting breast cancer biomarkers and molecular subtypes, using a group of 477 female patients with breast cancer.
- *The research involves extracting high-throughput features from tumor images and applying various machine learning models to classify the cancer types, assessing the performance through statistical tests like AUC.
- *Results indicate that there were no significant performance differences between DCE-MRI and NME-DWI for most classification tasks, suggesting both methods are similarly effective.
Background: Dynamic-exponential intravoxel incoherent motion (IVIM) imaging is a potential technique for prediction, monitoring, and differential diagnosis of hepatic diseases, especially liver tumors. However, the use of such technique at voxel level is still limited.
Purpose: To develop an unsupervised deep learning approach for voxel-wise dynamic-exponential IVIM modeling and parameter estimation in the liver.
Background: Intravoxel incoherent motion (IVIM) tensor imaging is a promising technique for diagnosis and monitoring of cardiovascular diseases. Knowledge about measurement repeatability, however, remains limited.
Purpose: To evaluate short-term repeatability of IVIM tensor imaging in normal in vivo human hearts.
Article Synopsis
- Reduced serum omentin-1 levels may indicate a higher risk of poor outcomes after acute intracerebral hemorrhage, suggesting its role as a prognostic biomarker.
- * In a study of 104 patients, lower omentin-1 concentrations were linked to worse functional outcomes over a 90-day follow-up, with a significant correlation identified (P < 0.001).
- * An optimal serum omentin-1 threshold of 179.7 ng/ml was found to effectively distinguish patients at risk for adverse outcomes, achieving 88% sensitivity and 70% specificity.
Objective: Comparing the characteristics of cognitive impairment of patients with single subcortical lesion stroke of four different areas, we are to explore the cognitive function of the thalamus and basal ganglia and this is help for early identification of vascular cognitive impairment (VCI).
Methods: 63 patients with single subcortical lesion stroke (including 14 left thalamic stoke group, 17 left basal ganglia stroke group, 15 right thalamic stroke group, 17 right basal ganglia stroke group) and 34 healthy subjects participated in the current study, whose age, sex and education were matched. A comprehensive neuropsychological battery was used for evaluation.