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. However, the diagnostic accuracy of TCCD in stratifying ICASs among patients with CVD remains unclear. Therefore, this systematic review and meta-analysis aimed at evaluating the diagnostic accuracy of TCCD in the stratification of intracranial steno-occlusions among CVD patients. A total of six databases-Embase, CINAHL, Medline, PubMed, Google Scholar, and Web of Science (core collection)-were searched for studies that assessed the diagnostic accuracy of TCCD in stratifying ICASs. The meta-analysis was performed using Meta-DiSc 1.4. The Quality Assessment of Diagnostic Accuracy Studies tool version 2 (QUADAS-2) assessed the risk of bias. Eighteen studies met all of the eligibility criteria. TCCD exhibited a high pooled diagnostic accuracy in stratifying intracranial steno-occlusions in patients presenting with CVD when compared to DSA as a reference standard (sensitivity = 90%; specificity = 87%; AUC = 97%). Additionally, the ultrasound parameters peak systolic velocity (PSV) and mean flow velocity (MFV) yielded a comparable diagnostic accuracy of "AUC = 0.96". In conclusion, TCCD could be a noble, safe, and accurate alternative imaging technique to DSA that can provide useful diagnostic information in stratifying intracranial steno-occlusions in patients presenting with CVD. TCCD should be considered in clinical cases where access to DSA is limited.

The Effects of Heat Treatment on the Microstructure and Mechanical Properties of a Selective Laser Melted AlCoFeNi Medium-Entropy Alloy.

A single body-centered cubic (BCC)-structured AlCoFeNi medium-entropy alloy (MEA) was prepared by the selective laser melting (SLM) technique. The hardness of the as-built sample was around 32.5 HRC. The ultimate tensile strength (UTS) was around 1211 MPa, the yield strength (YS) was around 1023 MPa, and the elongation (El) was around 10.8%. A novel BCC + B2 + face-centered cubic (FCC) structure was formed after aging. With an increase in aging temperature and duration, the number of fine grains increased, and more precipitates were observed. After aging at 450 °C for 4 h, the formed complex polyphase structure significantly improved the mechanical properties. Its hardness, UTS, YS, and El were around 45.7 HRC, 1535 MPa, 1489 MPa, and 8.5%, respectively. The improvement in mechanical properties was mainly due to Hall-Petch strengthening, which was caused by fine grains, and precipitation strengthening, which was caused by an increase in precipitates after aging. Meanwhile, the FCC precipitates made the alloy have good toughness. The complex interaction of multiple strengthening mechanisms leads to a good combination of strength, hardness, and toughness.

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. The assessment of renal cortex stiffness was performed using SWE imaging. The patients were classified into different groups based on pathological renal fibrosis (mild group: n = 74; moderate-to-severe group: n = 88). Binary logistic regression model and generalized additive model were conducted to investigate the association of renal elasticity with renal fibrosis. RESULTS: Compared with the mildly impaired group, the moderate-to-severe group showed a significant decline in renal elasticity (P < .001). In the fully adjusted model, each 10 kPa drop in renal elasticity was associated with a 3.5-fold increment in the risk of moderate-to-severe renal fibrosis (fully adjusted odds ratio, 4.54; 95% CI, 2.41-8.57). Particularly, participants in the lowest elasticity group (≤29.92 kPa) had a 20-fold increased chance of moderate-to-severe renal fibrosis than those in the group with highest elasticity (≥37.93 kPa). An inverse linear association was observed between renal elasticity increment and moderate-to-severe renal fibrosis risk. CONCLUSION: There is a negative linear association between increased renal elasticity and moderate-to-severe renal fibrosis risk among CKD patients. Patients with diminished renal stiffness have a higher risk of moderate-to-severe renal fibrosis. ADVANCES IN KNOWLEDGE: CKD patients with reduced renal stiffness have a higher likelihood of moderate-to-severe renal fibrosis.

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. In this paper, we propose a robust makeup transfer method which consistently transfers the makeup style of a reference image to facial images in any poses and expressions. Our method introduces the implicit 3D representation, neural radiance fields (NeRFs), to ensure the geometric and appearance consistency. It has two separate stages, including one basic NeRF module to reconstruct the geometry from the input facial image sequence, and a makeup module to learn how to transfer the reference makeup style consistently. We propose a novel hybrid makeup loss which is specially designed based on the makeup characteristics to supervise the training of the makeup module. The proposed loss significantly improves the visual quality and faithfulness of the makeup transfer effects. To better align the distribution between the transferred makeup and the reference makeup, a patch-based discriminator that works in the pose-independent UV texture space is proposed to provide more accurate control of the synthesized makeup. Extensive experiments and a user study demonstrate the superiority of our network for a variety of different makeup styles.

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. A cross-sectional study involving 50 healthy adults aged ≥ 18 years was conducted. The bilateral MCAs were insonated via three trans-temporal windows (TTWs-anterior, middle, and posterior) using TCD, cTCCD, and ncTCCD techniques. The MCA peak systolic velocity (PSV) and mean flow velocity (MFV) were recorded at proximal and distal imaging depths that could be visualised on TCCD with a detectable spectral waveform. A total of 152 measurements were recorded in 41 (82%) subjects with at least one-sided open TTW across the three techniques. The mean PSVs measured using TCD, ncTCCD, and cTCCD were 83 ± 18 cm/s, 81 ± 19 cm/s, and 93 ± 21 cm/s, respectively. There was no significant difference in PSV between TCD and ncTCCD (bias = 2 cm/s, p = 1.000), whereas cTCCD yielded a significantly higher PSV than TCD and ncTCCD (bias = -10 cm/s, p < 0.001; bias = -12 cm/s, p ≤ 0.001, respectively). The bias in MFV between TCD and ncTCCD techniques was (bias = -0.5 cm/s; p = 1.000), whereas cTCCD demonstrated a higher MFV compared to TCD and ncTCCD (bias = -8 cm/s, p < 0.001; bias = -8 cm/s, p ≤ 0.001, respectively). TCCD is a practically applicable imaging technique in assessing MCA blood flow velocities. cTCCD is more accurate and tends to give higher MCA blood flow velocities than non-imaging TCD and ncTCCD techniques. ncTCCD is comparable to non-imaging TCD and should be considered in clinical cases where using both TCD and TCCD measurements is needed.

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. Using SWE, the right renal cortex stiffness was measured, and the corresponding SWE value was recorded. Four diagnostic patterns were used to combine eGFR and SWE value: in isolation, in series, in parallel, and in integration. The receiver operating characteristic (ROC) curve was established, and the area under the ROC curve (AUC) was calculated to quantify diagnostic performance. Sensitivity, specificity, and accuracy were computed. Results: The eGFR demonstrated sensitivity of 68.2% and specificity of 83.8%, whereas the SWE value displayed sensitivity of 84.1% and specificity of 62.2%, yielding a similar AUC (78.2% and 77.8%, respectively). Combining in series improved specificity to 97.3%, superior to other diagnostic patterns (all P values <0.01), but compromised sensitivity to 58.0%. When combined in parallel, the sensitivity increased to 94.3%, exceeding any other strategies (all P values <0.05), but the specificity dropped to 48.7%. The integrated strategy, incorporating eGFR with SWE value via the logistic regression algorithm, exhibited an AUC of 85.8%, outperforming all existing approaches (all P values <0.01), with balanced sensitivity, specificity, and accuracy of 86.4%, 74.3%, and 80.9%, respectively. Conclusions: Using an integrated strategy to combine eGFR and SWE value could improve diagnostic performance in distinguishing between mild renal fibrosis and moderate-to-severe renal fibrosis in patients with CKD, thereby helping clinicians perform a more accurate clinical diagnosis.

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. A total of 92 datasets with 2444 addicted patients and 2712 healthy controls (HCs) were eligible for the meta-analysis. Our findings demonstrated that BA and SUD exhibited common alterations in rs-FC between frontoparietal network (FPN) and other high-level neurocognitive networks (i.e. default mode network (DMN), affective network (AN), and salience network (SN)) as well as hyperconnectivity between SN seeds and the Rolandic operculum in SSN. In addition, compared with BA, SUD exhibited several distinct within- and between-network rs-FC alterations mainly involved in the DMN and FPN. Further, altered within- and between-network rs-FC showed significant association with clinical characteristics such as the severity of addiction in BA and duration of substance usage in SUD. The common rs-FC alterations in BA and SUD exhibited the relationship with consistent aberrant behaviors in both addiction groups, such as impaired inhibition control and salience attribution. By contrast, the distinct rs-FC alterations might suggest specific substance effects on the brain neural transmitter systems in SUD.

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. Five prospective patients from another center were recruited for external validation. Patients received 4D-MRI scans and 3D MRI scans. The 4D-MRI was retrospectively down-sampled to simulate real-time acquisition. Motion estimation was performed using the proposed D2R model. The accuracy and robustness of the proposed D2R model and baseline methods, including Demons, Elastix, the parametric total variation (pTV) algorithm, and VoxelMorph, were compared. High-quality (HQ) 4D-MR images were also constructed using the D2R model for real-time imaging feasibility verification. The image quality and motion accuracy of the constructed HQ 4D-MRI were evaluated. RESULTS: The D2R model showed significantly superior and robust registration performance than all the baseline methods at downsampling factors up to 500. HQ T1-weighted and T2-weighted 4D-MR images were also successfully constructed with significantly improved image quality, sub-voxel level motion error, and real-time efficiency. External validation demonstrated the robustness and generalizability of the technique. CONCLUSION: In this study, we developed a novel D2R model for deformation estimation of downsampled 4D-MR images. HQ 4D-MR images were successfully constructed using the D2R model. This model may expand the clinical implementation of 4D-MRI for real-time motion management during liver cancer treatment.

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. Method: PubMed, OVID Embase, OVID Medline, and Web of Science were searched with relevant keywords to identify potential studies. A total of 52 studies including 35 rs-FC studies and 17 structural studies were examined by extracting the coordinates of seeds and target brain regions. The seeds were then categorized into predefined seven networks by their locations based on previous parcellations in rs-FC studies, followed by pooling the results in those networks. Results: The rs-FC findings illustrated that BA were characterized as abnormal networks in response to inhibition, salience attribution, self-referential mental process, and reward-driven behaviors. Meanwhile, meta-analysis of structural studies showed decreased gray matter volume in the anterior cingulate cortex, extending to the middle cingulate cortex and the superior frontal gyrus. Importantly, overlapping regions in the cingulate cortex and anterior thalamus projections extending to caudate regions exhibited both dysfunctions in structure and rs-FC. Conclusions: This study highlighted substantial dysconnectivity in BA, which might result in impaired response to inhibition and salience attribution. Therefore, this study might provide novel insights of neural biomarkers for clinical diagnoses and treatment targets for 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. We identified single predictive radiomic feature from primary gross tumor volume (GTVnp) and defined predicted subvolume by calculating voxel-wised feature mapping and within GTVnp. We independently validate predictive value of identified feature and associated predicted subvolume. RESULTS: Only one radiomic feature, gldm_DependenceVariance in 3 mm-sigma LoG-filtered image, was discovered as a signature. In the high-risk group determined by the signature, patients received CCRT + ACT achieved 3-year disease free survival (DFS) rate of 90% versus 57% (HR, 0.20; 95%CI, 0.05-0.94; P = 0.007) for CCRT alone. The multivariate analysis showed patients receiving CCRT + ACT had a HR of 0.21 (95%CI: 0.06-0.68, P = 0.009) for DFS compared to those receiving CCRT alone. The predictive value can also be generalized to the subvolume with multivariate HR of 0.27 (P = 0.017) for DFS. CONCLUSION: The signature with its heterogeneity mapping could be a reliable and explainable ACT decision-making tool in clinical practice.

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. Patients were classified into the mild or moderate-severe fibrosis group based on pathology results. Radiomics features were extracted from the US images, and a radiomics signature was constructed using the maximum relevance minimum redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) regression algorithms. Multivariable logistic regression was employed to construct the radiomics model, which incorporated the radiomics signature and the selected clinical variables. The established model was evaluated for discrimination, calibration, and clinical utility in the derivation cohort and internal cross-validation (CV) analysis, respectively. RESULTS: The radiomics signature, consisting of nine identified fibrosis-related features, achieved moderate discriminatory ability with an area under the receiver operating characteristic curve (AUC) of 0.72 (95% confidence interval (CI) 0.64-0.79). By combining the radiomics signature with significant clinical risk factors, the radiomics model showed satisfactory discrimination performance, yielding an AUC of 0.85 (95% CI 0.79-0.91) in the derivation cohort and a mean AUC of 0.84 (95% CI 0.77-0.92) in the internal CV analysis. It also demonstrated fine accuracy via the calibration curve. Furthermore, the decision curve analysis indicated that the model was clinically useful. CONCLUSION: The proposed radiomics model showed favorable performance in determining the individualized risk of moderate-severe renal fibrosis in patients with CKD, which may facilitate more effective clinical decision-making.

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. (2) Methods: A total of 242 biopsy-proven NPC patients were retrospectively recruited in this study. Radiomics features were extracted from contrast-enhanced CT (CECT), contrast-enhanced T1-weighted (cT1WI), and T2-weighted (T2WI) images in the primary tumor and tumor-related area. Dosiomics features were extracted from 2D or 3D dose-volume histograms (DVH). Multiple models were established with single and integrated data. The dataset was randomized into training and test sets at a ratio of 7:3 with 10-fold cross-validation. (3) Results: The best-performing model using Gaussian Naive Bayes (GNB) (mean validation AUC = 0.81 ± 0.10) was established with integrated radiomics and dosiomics data. The GNB radiomics and dosiomics models yielded mean validation AUC of 0.6 ± 0.20 and 0.69 ± 0.14, respectively. (4) Conclusions: Integrating radiomics and dosiomics data from the primary tumor area could generate the best-performing model for severe AOM prediction.

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. RFs were extracted from primary tumor volume under a wide range of image filtering and discretization settings. RF repeatability was assessed by intraclass correlation coefficient (ICC), which was used to equally separate the RFs into low- and high-repeatable groups by the median value. After feature selection, multivariate Cox regression and Kaplan-Meier analysis were independently employed to develop and analyze prognostic models. Concordance index (C-index) and P-value from log-rank test were used to assess model performance. RESULTS: Most textural RFs from high-pass wavelet-filtered images were susceptible to image perturbations. It was more prominent when a smaller discretization bin number was used (e.g., 8, mean ICC = 0.69). Using high-repeatable RFs for model development yielded a significantly higher C-index (0.63) in the validation cohort than when only low-repeatable RFs were used (0.57, P = 0.024), suggesting higher model generalizability. Besides, significant risk stratification in the validation cohort was observed only when high-repeatable RFs were used (P < 0.001). CONCLUSION: Repeatability of RFs from high-pass wavelet-filtered CET1-w MR images of primary NPC tumor was poor, particularly when a smaller bin number was used. Exclusive use of high-repeatable RFs is suggested to safeguard model generalizability for wide-spreading clinical utilization.

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. We first developed a radiomic prognostic model for head-and-neck cancer patients on a training (70%) and evaluated on a testing (30%) cohort using C-index. Subsequently, we applied the IPBM to CT images of both cohorts (Perturbed-Train and Perturbed-Test cohort) to generate 60 additional samples for both cohorts. Model reliability was assessed using intra-class correlation coefficient (ICC) to quantify consistency of the C-index among the 60 samples in the Perturbed-Train and Perturbed-Test cohorts. Besides, we re-trained the radiomic model using reliable RFs exclusively (ICC > 0.75) to validate the IPBM. Results showed moderate model reliability in Perturbed-Train (ICC: 0.565, 95%CI 0.518-0.615) and Perturbed-Test (ICC: 0.596, 95%CI 0.527-0.670) cohorts. An enhanced reliability of the re-trained model was observed in Perturbed-Train (ICC: 0.782, 95%CI 0.759-0.815) and Perturbed-Test (ICC: 0.825, 95%CI 0.782-0.867) cohorts, indicating validity of the IPBM. To conclude, we demonstrated capability of the IPBM toward building reliable radiomic models, providing community with a novel model reliability assessment strategy prior to prospective evaluation.