Ultrasonic renal length as an indicator of renal fibrosis severity in non-diabetic patients with chronic kidney disease.

Background: Debate continues regarding the potential of the ultrasonic renal length to serve as an indicator for evaluating the advancement of renal fibrosis in chronic kidney disease (CKD). This study investigates the independent association between renal length and renal fibrosis in non-diabetic CKD patients and assesses its diagnostic performance.

Methods: From April 2019 to December 2021, 144 non-diabetic patients diagnosed with CKD who underwent a renal ultrasound examination and kidney biopsy were prospectively enrolled.

The Diagnostic Accuracy of Transcranial Color-Coded Doppler Ultrasound Technique in Stratifying Intracranial Cerebral Artery Stenoses in Cerebrovascular Disease Patients: A Systematic Review and Meta-Analysis.

The early and accurate stratification of intracranial cerebral artery stenosis (ICAS) is critical to inform treatment management and enhance the prognostic outcomes in patients with cerebrovascular disease (CVD). Digital subtraction angiography (DSA) is an invasive and expensive procedure but is the gold standard for the diagnosis of ICAS. Over recent years, transcranial color-coded Doppler ultrasound (TCCD) has been suggested to be a useful imaging method for accurately diagnosing ICAS.

Integrating shear wave elastography and estimated glomerular filtration rate to enhance diagnostic strategy for renal fibrosis assessment in chronic kidney disease.

Background: Assessing renal fibrosis non-invasively in patients with chronic kidney disease (CKD) remains a considerable clinical challenge. This study aimed to investigate the diagnostic efficacy of different approaches that combine shear wave elastography (SWE) and estimated glomerular filtration rate (eGFR) in distinguishing between mild fibrosis and moderate-to-severe fibrosis in CKD patients.

Methods: In this prospective study, 162 patients underwent renal SWE examinations and renal biopsies.

A Comparative Study of Transcranial Color-Coded Doppler (TCCD) and Transcranial Doppler (TCD) Ultrasonography Techniques in Assessing the Intracranial Cerebral Arteries Haemodynamics.

Cerebrovascular disease (CVD) poses a major public health and socio-economic burden worldwide due to its high morbidity and mortality rates. Accurate assessment of cerebral arteries' haemodynamic plays a crucial role in the diagnosis and treatment management of CVD. The study compared a non-imaging transcranial Doppler ultrasound (TCD) and transcranial color-coded Doppler ultrasound (with (cTCCD) and without (ncTCCD)) angle correction in quantifying middle cerebral arteries (MCAs) haemodynamic parameters.

MuNeRF: Robust Makeup Transfer in Neural Radiance Fields.

There has been a high demand for facial makeup transfer tools in fashion e-commerce and virtual avatar generation. Most of the existing makeup transfer methods are based on the generative adversarial networks. Despite their success in makeup transfer for a single image, they struggle to maintain the consistency of makeup under different poses and expressions of the same person.

Association of renal elasticity evaluated by real-time shear wave elastography with renal fibrosis in patients with chronic kidney disease.

Objective: Renal fibrosis is a final common pathological hallmark in the progression of chronic kidney disease (CKD). Non-invasive evaluation of renal fibrosis by mapping renal stiffness obtained by shear wave elastography (SWE) may facilitate the clinical therapeutic regimen for CKD patients.

Methods: A cohort of 162 patients diagnosed with CKD, who underwent renal biopsy, was prospectively and consecutively recruited between April 2019 and December 2021.

Similarity and difference in large-scale functional network alternations between behavioral addictions and substance use disorder: a comparative meta-analysis.

Behavioral addiction (BA) and substance use disorder (SUD) share similarities and differences in clinical symptoms, cognitive functions, and behavioral attributes. However, little is known about whether and how functional networks in the human brain manifest commonalities and differences between BA and SUD. Voxel-wise meta-analyses of resting-state functional connectivity (rs-FC) were conducted in BA and SUD separately, followed by quantitative conjunction analyses to identify the common and distinct alterations across both the BA and SUD groups.

Ultra-fast multi-parametric 4D-MRI image reconstruction for real-time applications using a downsampling-invariant deformable registration (D2R) model.

Background And Purpose: Motion estimation from severely downsampled 4D-MRI is essential for real-time imaging and tumor tracking. This simulation study developed a novel deep learning model for simultaneous MR image reconstruction and motion estimation, named the Downsampling-Invariant Deformable Registration (D2R) model.

Materials And Methods: Forty-three patients undergoing radiotherapy for liver tumors were recruited for model training and internal validation.

Abnormal structural alterations and disrupted functional connectivity in behavioral addiction: A meta-analysis of VBM and fMRI studies.

Background: Altered large-scale brain systems, including structural alterations and resting-state functional connectivity (rs-FC) changes, have been demonstrated as effective system-level biomarkers for revealing potential neural mechanism of multiple brain disorders. However, identifying consistent abnormalities of large-scale brain systems in behavioral addictions (BA) is challenging due to varying methods and inconsistent results. Therefore, the aim of this study was to identify the significantly abnormal large-scale brain systems in BA.

Explainable machine learning via intra-tumoral radiomics feature mapping for patient stratification in adjuvant chemotherapy for locoregionally advanced nasopharyngeal carcinoma.

Purpose: This study aimed to discover intra-tumor heterogeneity signature and validate its predictive value for adjuvant chemotherapy (ACT) following concurrent chemoradiotherapy (CCRT) in locoregionally advanced nasopharyngeal carcinoma (LA-NPC).

Materials And Methods: 397 LA-NPC patients were retrospectively enrolled. Pre-treatment contrast-enhanced T1-weighted (CET1-w) MR images, clinical variables, and follow-up were retrospectively collected.

Ultrasound-based radiomics analysis in the assessment of renal fibrosis in patients with chronic kidney disease.

Purpose: Assessment of renal fibrosis non-invasively in chronic kidney disease (CKD) patients is still a clinical challenge. In this study, we aimed to establish a radiomics model integrating radiomics features derived from ultrasound (US) images with clinical characteristics for the assessment of renal fibrosis severity in CKD patients.

Methods: A total of 160 patients with CKD who underwent kidney biopsy and renal US examination were prospectively enrolled.

Multimodal Data Integration to Predict Severe Acute Oral Mucositis of Nasopharyngeal Carcinoma Patients Following Radiation Therapy.

(1) Background: Acute oral mucositis is the most common side effect for nasopharyngeal carcinoma patients receiving radiotherapy. Improper or delayed intervention to severe AOM could degrade the quality of life or survival for NPC patients. An effective prediction method for severe AOM is needed for the individualized management of NPC patients in the era of personalized medicine.

Radiomic feature repeatability and its impact on prognostic model generalizability: A multi-institutional study on nasopharyngeal carcinoma patients.

Background And Purpose: To investigate the radiomic feature (RF) repeatability via perturbation and its impact on cross-institutional prognostic model generalizability in Nasopharyngeal Carcinoma (NPC) patients.

Materials And Methods: 286 and 183 NPC patients from two institutions were included for model training and validation. Perturbations with random translations and rotations were applied to contrast-enhanced T1-weighted (CET1-w) MR images.

Building reliable radiomic models using image perturbation.

Radiomic model reliability is a central premise for its clinical translation. Presently, it is assessed using test-retest or external data, which, unfortunately, is often scarce in reality. Therefore, we aimed to develop a novel image perturbation-based method (IPBM) for the first of its kind toward building a reliable radiomic model.