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.

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.

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.

Interpretable machine learning model integrating clinical and elastosonographic features to detect renal fibrosis in Asian patients with chronic kidney disease.

BACKGROUND: Non-invasive renal fibrosis assessment is critical for tailoring personalized decision-making and managing follow-up in patients with chronic kidney disease (CKD). We aimed to exploit machine learning algorithms using clinical and elastosonographic features to distinguish moderate-severe fibrosis from mild fibrosis among CKD patients. METHODS: A total of 162 patients with CKD who underwent shear wave elastography examinations and renal biopsies at our institution were prospectively enrolled. Four classifiers using machine learning algorithms, including eXtreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Light Gradient Boosting Machine (LightGBM), and K-Nearest Neighbor (KNN), which integrated elastosonographic features and clinical characteristics, were established to differentiate moderate-severe renal fibrosis from mild forms. The area under the receiver operating characteristic curve (AUC) and average precision were employed to compare the performance of constructed models, and the SHapley Additive exPlanations (SHAP) strategy was used to visualize and interpret the model output. RESULTS: The XGBoost model outperformed the other developed machine learning models, demonstrating optimal diagnostic performance in both the primary (AUC = 0.97, 95% confidence level (CI) 0.94-0.99; average precision = 0.97, 95% CI 0.97-0.98) and five-fold cross-validation (AUC = 0.85, 95% CI 0.73-0.98; average precision = 0.90, 95% CI 0.86-0.93) datasets. The SHAP approach provided visual interpretation for XGBoost, highlighting the features' impact on the diagnostic process, wherein the estimated glomerular filtration rate provided the largest contribution to the model output, followed by the elastic modulus, then renal length, renal resistive index, and hypertension. CONCLUSION: This study proposed an XGBoost model for distinguishing moderate-severe renal fibrosis from mild forms in CKD patients, which could be used to assist clinicians in decision-making and follow-up strategies. Moreover, the SHAP algorithm makes it feasible to visualize and interpret the feature processing and diagnostic processes of the model output.

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.

Using ultrasound elastography to assess non-invasive, non-pharmacological interventions for musculoskeletal stiffness: a systematic review and meta-analysis.

PURPOSE: To evaluate the current evidence regarding the use of ultrasound elastography for assessing non-invasive, non-pharmacological interventions for eliciting changes in musculoskeletal stiffness. METHODS: A systematic search of MEDLINE, CINAHL, EMBASE, and Web of Science databases was performed in accordance with Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines. Information on measurement and intervention procedures was extracted. Bias was assessed using Cochrane Risk of Bias or Risk of Bias In Non-randomised Studies of Interventions (ROBINS-I) tools for studies with true or quasi-experimental designs, respectively. Analyses were conducted for adequately powered subgroups based on intervention type, measurement site, and population assessed. RESULTS: Twenty-one studies were included in the review. Overall risk of bias was low for true experimental studies and moderate for quasi-experimental studies. Subgroup analyses indicated a large overall effect for interventions involving manual physiotherapy and taping/splinting for reducing masseter muscle stiffness in patients with masticatory muscle disorders (g = 1.488, 95% CI = 0.320-2.655, p = 0.013). Analyses for other intervention types and patient groups were underpowered. CONCLUSION: Ultrasound elastography demonstrates clinical applicability for assessing non-invasive, non-pharmacological interventions for musculoskeletal stiffness. However, the comparative efficacy of these interventions for modulating tissue stiffness remains inconclusive.

Elastography demonstrates clinical applicability for assessing non-invasive, non-pharmacological interventions for musculoskeletal stiffnessInterventions involving manual physiotherapy and taping/bracing showed a large overall effect for reducing masseter muscle stiffnessThe comparative efficacy of other interventions remains inconclusive.

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.

Respiratory-Correlated 4-Dimensional Magnetic Resonance Fingerprinting for Liver Cancer Radiation Therapy Motion Management.

PURPOSE: The objective of this study was to develop a respiratory-correlated (RC) 4-dimensional (4D) imaging technique based on magnetic resonance fingerprinting (MRF) (RC-4DMRF) for liver tumor motion management in radiation therapy. METHODS AND MATERIALS: Thirteen patients with liver cancer were prospectively enrolled in this study. k-space MRF signals of the liver were acquired during free-breathing using the fast acquisition with steady-state precession sequence on a 3T scanner. The signals were binned into 8 respiratory phases based on respiratory surrogates, and interphase displacement vector fields were estimated using a phase-specific low-rank optimization method. Hereafter, the tissue property maps, including T1 and T2 relaxation times, and proton density, were reconstructed using a pyramid motion-compensated method that alternatively optimized interphase displacement vector fields and subspace images. To evaluate the efficacy of RC-4DMRF, amplitude motion differences and Pearson correlation coefficients were determined to assess measurement agreement in tumor motion between RC-4DMRF and cine magnetic resonance imaging (MRI); mean absolute percentage errors of the RC-4DMRF-derived tissue maps were calculated to reveal tissue quantification accuracy using digital human phantom; and tumor-to-liver contrast-to-noise ratio of RC-4DMRF images was compared with that of planning CT and contrast-enhanced MRI (CE-MRI) images. A paired Student t test was used for statistical significance analysis with a P value threshold of .05. RESULTS: RC-4DMRF achieved excellent agreement in motion measurement with cine MRI, yielding the mean (± standard deviation) Pearson correlation coefficients of 0.95 ± 0.05 and 0.93 ± 0.09 and amplitude motion differences of 1.48 ± 1.06 mm and 0.81 ± 0.64 mm in the superior-inferior and anterior-posterior directions, respectively. Moreover, RC-4DMRF achieved high accuracy in tissue property quantification, with mean absolute percentage errors of 8.8%, 9.6%, and 5.0% for T1, T2, and proton density, respectively. Notably, the tumor contrast-to-noise ratio in RC-4DMRI-derived T1 maps (6.41 ± 3.37) was found to be the highest among all tissue property maps, approximately equal to that of CE-MRI (6.96 ± 1.01, P = .862), and substantially higher than that of planning CT (2.91 ± 1.97, P = .048). CONCLUSIONS: RC-4DMRF demonstrated high accuracy in respiratory motion measurement and tissue properties quantification, potentially facilitating tumor motion management in liver radiation therapy.

Inter-observer and intra-observer reliability between manual segmentation and semi-automated segmentation for carotid vessel wall volume measurements on three-dimensional ultrasonography.

PURPOSE: Carotid vessel wall volume (VWV) measurement on three-dimensional ultrasonography (3DUS) outperforms conventional two-dimensional ultrasonography for carotid atherosclerosis evaluation. Although time-saving semi-automated algorithms have been introduced, their clinical availability remains limited due to a lack of validation, particularly an extensive reliability analysis. This study compared inter-observer and intra-observer reliability between manual segmentation and semi-automated segmentation for carotid VWV measurements on 3DUS. METHODS: Thirty-one 3DUS volume datasets were prospectively acquired from 20 healthy subjects, aged >18 years, without previous stroke, transient ischemic attack, or cardiovascular disease. Five observers segmented all volume datasets both manually and semi-automatically. The process was repeated five times. Reliability was expressed by the intraclass correlation coefficient, supplemented by the coefficient of variation. RESULTS: Carotid VWV measurements using the common carotid artery (CCA) were more reliable than those using the internal carotid artery (ICA) or external carotid artery (ECA) for both manual and semiautomated segmentation (manual segmentation, CCA: inter-observer, 0.935; intra-observer, 0.934 to 0.966; ICA: inter-observer, 0.784; intra-observer, 0.756 to 0.878; ECA: inter-observer, 0.732; intraobserver, 0.919 to 0.962; semi-automated segmentation, CCA: inter-observer, 0.986; intra-observer, 0.954 to 0.993; ICA: inter-observer, 0.977; intra-observer, 0.958 to 0.978; ECA: inter-observer, 0.966; intra-observer, 0.884 to 0.937). Total carotid VWV measurements by manual (inter-observer, 0.922; intra-observer, 0.927 to 0.961) and semi-automated segmentation (inter-observer, 0.987; intra-observer, 0.968 to 0.989) were highly reliable. Semi-automated segmentation showed higher reliability than manual segmentation for both individual and total carotid VWV measurements. CONCLUSION: 3DUS carotid VWV measurements of the CCA are more reliable than measurements of the ICA and ECA. Total carotid VWV measurements are highly reliable. Semi-automated segmentation has higher reliability than manual segmentation.

Deep learning attention-guided radiomics for COVID-19 chest radiograph classification.

Background: Accurate assessment of coronavirus disease 2019 (COVID-19) lung involvement through chest radiograph plays an important role in effective management of the infection. This study aims to develop a two-step feature merging method to integrate image features from deep learning and radiomics to differentiate COVID-19, non-COVID-19 pneumonia and normal chest radiographs (CXR). Methods: In this study, a deformable convolutional neural network (deformable CNN) was developed and used as a feature extractor to obtain 1,024-dimensional deep learning latent representation (DLR) features. Then 1,069-dimensional radiomics features were extracted from the region of interest (ROI) guided by deformable CNN's attention. The two feature sets were concatenated to generate a merged feature set for classification. For comparative experiments, the same process has been applied to the DLR-only feature set for verifying the effectiveness of feature concatenation. Results: Using the merged feature set resulted in an overall average accuracy of 91.0% for three-class classification, representing a statistically significant improvement of 0.6% compared to the DLR-only classification. The recall and precision of classification into the COVID-19 class were 0.926 and 0.976, respectively. The feature merging method was shown to significantly improve the classification performance as compared to using only deep learning features, regardless of choice of classifier (P value <0.0001). Three classes' F1-score were 0.892, 0.890, and 0.950 correspondingly (i.e., normal, non-COVID-19 pneumonia, COVID-19). Conclusions: A two-step COVID-19 classification framework integrating information from both DLR and radiomics features (guided by deep learning attention mechanism) has been developed. The proposed feature merging method has been shown to improve the performance of chest radiograph classification as compared to the case of using only deep learning features.

Artificial intelligence-assisted multistrategy image enhancement of chest X-rays for COVID-19 classification.

Background: The coronavirus disease 2019 (COVID-19) led to a dramatic increase in the number of cases of patients with pneumonia worldwide. In this study, we aimed to develop an AI-assisted multistrategy image enhancement technique for chest X-ray (CXR) images to improve the accuracy of COVID-19 classification. Methods: Our new classification strategy consisted of 3 parts. First, the improved U-Net model with a variational encoder segmented the lung region in the CXR images processed by histogram equalization. Second, the residual net (ResNet) model with multidilated-rate convolution layers was used to suppress the bone signals in the 217 lung-only CXR images. A total of 80% of the available data were allocated for training and validation. The other 20% of the remaining data were used for testing. The enhanced CXR images containing only soft tissue information were obtained. Third, the neural network model with a residual cascade was used for the super-resolution reconstruction of low-resolution bone-suppressed CXR images. The training and testing data consisted of 1,200 and 100 CXR images, respectively. To evaluate the new strategy, improved visual geometry group (VGG)-16 and ResNet-18 models were used for the COVID-19 classification task of 2,767 CXR images. The accuracy of the multistrategy enhanced CXR images was verified through comparative experiments with various enhancement images. In terms of quantitative verification, 8-fold cross-validation was performed on the bone suppression model. In terms of evaluating the COVID-19 classification, the CXR images obtained by the improved method were used to train 2 classification models. Results: Compared with other methods, the CXR images obtained based on the proposed model had better performance in the metrics of peak signal-to-noise ratio and root mean square error. The super-resolution CXR images of bone suppression obtained based on the neural network model were also anatomically close to the real CXR images. Compared with the initial CXR images, the classification accuracy rates of the internal and external testing data on the VGG-16 model increased by 5.09% and 12.81%, respectively, while the values increased by 3.51% and 18.20%, respectively, for the ResNet-18 model. The numerical results were better than those of the single-enhancement, double-enhancement, and no-enhancement CXR images. Conclusions: The multistrategy enhanced CXR images can help to classify COVID-19 more accurately than the other existing methods.

Combined Shear Wave Elastography and EU TIRADS in Differentiating Malignant and Benign Thyroid Nodules.

Although multimodal ultrasound approaches have been suggested to potentially improve the diagnosis of thyroid cancer; the diagnostic utility of the combination of SWE and malignancy-risk stratification systems remains vague due to the lack of standardized criteria. The purpose of the study was to assess the diagnostic value of the combination of grey scale ultrasound assessment using EU TIRADS and shear wave elastography. 121 patients (126 nodules−81 benign; 45 malignant) underwent grey scale ultrasound and SWE imaging of nodules between 0.5 cm and 5 cm prior to biopsy and/or surgery. Nodules were analyzed based on size stratifications: <1 cm (n = 43); 1−2 cm (n = 52) and >2 cm (n = 31) and equivocal cytology status (n = 52), and diagnostic performance assessments were conducted. The combination of EU TIRADS with SWE using the SD parameter; maintained a high sensitivity and significantly improved the specificity of sole EU TIRADS for nodules 1−2 cm (SEN: 72.2% vs. 88.9%, p > 0.05; SPEC: 76.5% vs. 55.9%, p < 0.01) and >2 cm (SEN: 71.4% vs. 85.7%, p > 0.05; SPEC: 95.8% vs. 62.5%, p < 0.01). For cytologically-equivocal nodules; the combination with the SWE minimum parameter resulted in a significant reduction in sensitivity with increased specificity (SEN: 60% vs. 80%; SPEC: 83.4% vs. 37.8%; all p < 0.05). SWE in combination with EU TIRADS is diagnostically efficient in discriminating nodules > 1 cm but is not ideal for discriminating cytologically-equivocal nodules.

Diagnostic Efficacy of Advanced Ultrasonography Imaging Techniques in Infants with Biliary Atresia (BA): A Systematic Review and Meta-Analysis.

The early diagnosis of biliary atresia (BA) in cholestatic infants is critical to the success of the treatment. Intraoperative cholangiography (IOC), an invasive imaging technique, is the current strategy for the diagnosis of BA. Ultrasonography has advanced over recent years and emerging techniques such as shear wave elastography (SWE) have the potential to improve BA diagnosis. This review sought to evaluate the diagnostic efficacy of advanced ultrasonography techniques in the diagnosis of BA. Six databases (CINAHL, Medline, PubMed, Google Scholar, Web of Science (core collection), and Embase) were searched for studies assessing the diagnostic performance of advanced ultrasonography techniques in differentiating BA from non-BA causes of infantile cholestasis. The meta-analysis was performed using Meta-DiSc 1.4 and Comprehensive Meta-analysis v3 software. Quality Assessment of Diagnostic Accuracy Studies tool version 2 (QUADAS-2) assessed the risk of bias. Fifteen studies consisting of 2185 patients (BA = 1105; non-BA = 1080) met the inclusion criteria. SWE was the only advanced ultrasonography technique reported and had a good pooled diagnostic performance (sensitivity = 83%; specificity = 77%; AUC = 0.896). Liver stiffness indicators were significantly higher in BA compared to non-BA patients (p < 0.000). SWE could be a useful tool in differentiating BA from non-BA causes of infantile cholestasis. Future studies to assess the utility of other advanced ultrasonography techniques are recommended.

Development and validation of bone-suppressed deep learning classification of COVID-19 presentation in chest radiographs.

Background: Coronavirus disease 2019 (COVID-19) is a pandemic disease. Fast and accurate diagnosis of COVID-19 from chest radiography may enable more efficient allocation of scarce medical resources and hence improved patient outcomes. Deep learning classification of chest radiographs may be a plausible step towards this. We hypothesize that bone suppression of chest radiographs may improve the performance of deep learning classification of COVID-19 phenomena in chest radiographs. Methods: Two bone suppression methods (Gusarev et al. and Rajaraman et al.) were implemented. The Gusarev and Rajaraman methods were trained on 217 pairs of normal and bone-suppressed chest radiographs from the X-ray Bone Shadow Suppression dataset (https://www.kaggle.com/hmchuong/xray-bone-shadow-supression). Two classifier methods with different network architectures were implemented. Binary classifier models were trained on the public RICORD-1c and RSNA Pneumonia Challenge datasets. An external test dataset was created retrospectively from a set of 320 COVID-19 positive patients from Queen Elizabeth Hospital (Hong Kong, China) and a set of 518 non-COVID-19 patients from Pamela Youde Nethersole Eastern Hospital (Hong Kong, China), and used to evaluate the effect of bone suppression on classifier performance. Classification performance, quantified by sensitivity, specificity, negative predictive value (NPV), accuracy and area under the receiver operating curve (AUC), for non-suppressed radiographs was compared to that for bone suppressed radiographs. Some of the pre-trained models used in this study are published at (https://github.com/danielnflam). Results: Bone suppression of external test data was found to significantly (P<0.05) improve AUC for one classifier architecture [from 0.698 (non-suppressed) to 0.732 (Rajaraman-suppressed)]. For the other classifier architecture, suppression did not significantly (P>0.05) improve or worsen classifier performance. Conclusions: Rajaraman suppression significantly improved classification performance in one classification architecture, and did not significantly worsen classifier performance in the other classifier architecture. This research could be extended to explore the impact of bone suppression on classification of different lung pathologies, and the effect of other image enhancement techniques on classifier performance.

Diagnostic Value of AngioPLUS Microvascular Imaging in Thyroid Nodule Diagnosis Using Quantitative and Qualitative Vascularity Grading.

This study investigated the diagnostic value of the Angio Planewave Ultrasensitive (AngioPLUS) Doppler ultrasound in improving the efficacy of grey scale ultrasound in thyroid nodule diagnosis. The EU TIRADS was used for the grey scale ultrasound assessment of 94 thyroid nodules. conventional Doppler and AngioPLUS Doppler ultrasound images were evaluated using qualitative vascularity grading, where predominant central vascularity indicated malignancy-suspicion, and quantitative regional vascularity assessment, where predominant peripheral vascularity using a ratio vascularity index (RVI) of > 1 indicated benign disease. Diagnostic performance outcomes of sole and combination approaches were calculated based on final pathologic results. Using sole EU TIRADS and AngioPLUS + power Doppler imaging (APDI) based on qualitative vascularity and RVI, the results were a sensitivity of 83.3% vs. 83.3 vs. 66.7% and a specificity of 50% vs. 81.3% vs. 73.4, respectively. EU TIRADS combined with APDI significantly improved the specificity using both qualitative vascularity and RVI assessment approaches (84.4% and 81%, respectively, p < 0.05); and slightly reduced the sensitivity (76.7% and 58.1%). For cytologically-equivocal thyroid nodules, the combination approach using qualitative vascularity assessment outperformed the EU TIRADS (sensitivity: both were 88.9%; specificity: 77.4% vs. 38.7%, p < 0.05; and AUROC: 0.83 vs. 0.62, p < 0.05). APDI combined with EU TIRADS is diagnostically efficient in stratifying thyroid nodules, particularly cytologically-equivocal nodules.

Prognostic value of cervical nodal necrosis on staging imaging of nasopharyngeal carcinoma in era of intensity-modulated radiotherapy: a systematic review and meta-analysis.

PURPOSES: To systematically review and perform meta-analysis to evaluate the prognostic value of cervical nodal necrosis (CNN) on the staging computed tomography/magnetic resonance imaging (MRI) of nasopharyngeal carcinoma (NPC) in era of intensity-modulated radiotherapy. METHODS: Literature search through PubMed, EMBASE, and Cochrane Library was conducted. The hazard ratios (HRs) with 95% confidence intervals (CIs) of CNN for distant metastasis-free survival (DMFS), disease free survival (DFS) and overall survival (OS) were extracted from the eligible studies and meta-analysis was performed to evaluate the pooled HRs with 95%CI. RESULTS: Nine studies, which investigated the prognostic values of 6 CNN patterns on MRI were included. Six/9 studies were eligible for meta-analysis, which investigated the CNN presence/absence in any nodal group among 4359 patients. The pooled unadjusted HRs showed that the CNN presence predicted poor DMFS (HR =1.89, 95%CI =1.72-2.08), DFS (HR =1.57, 95%CI =1.08-2.26), and OS (HR =1.87, 95%CI =1.69-2.06). The pooled adjusted HRs also showed the consistent results for DMFS (HR =1.34, 95%CI =1.17-1.54), DFS (HR =1.30, 95%CI =1.08-1.56), and OS (HR =1.61, 95%CI =1.27-2.04). Results shown in the other studies analysing different CNN patterns indicated the high grade of CNN predicted poor outcome, but meta-analysis was unable to perform because of the heterogeneity of the analysed CNN patterns. CONCLUSION: The CNN observed on the staging MRI is a negative factor for NPC outcome, suggesting that the inclusion of CNN is important in the future survival analysis. However, whether and how should CNN be included in the staging system warrant further evaluation.

Structural and passive mechanical properties of the medial gastrocnemius muscle in ambulatory individuals with chronic stroke.

BACKGROUND: This study aimed to investigate the structural, morphological and passive mechanical properties of the medial gastrocnemius muscle among ambulating chronic stroke survivors using a computational model previously established in healthy individuals without stroke. METHODS: Individuals with chronic stroke (n = 14, age = 63.4 ± 6.0 years) and healthy controls (n = 15, age = 59.6 ± 8.4 years) participated in the study. The mechanical properties of the medial gastrocnemius were measured during continuous passive ankle motion using ultrasound elastography and a corresponding muscle mechanical property-angle curve was estimated where slack angle and elasticity were determined. Muscle thickness, fascicle length, pennation angle, and echo intensity were also assessed using B-mode ultrasound. FINDINGS: No significant differences in slack angle (paretic: -16.2° ± 6.13°, non-paretic: -16.93° ± 6.80°, p = 0.82), or slack elasticity (paretic: 4.36 ± 1.94 kPa, non-paretic: 4.54 ± 1.24 kPa, p = 0.64) were found between sides or groups. Lower muscle pennation angle (paretic: 13.6 ± 2.9°, non-paretic: 15.9 ± 2.0°, p = 0.019) and higher echo intensity (paretic: 80.5 ± 13.6, non-paretic: 63.4 ± 17.1, p = 0.003) were observed for paretic muscles. No significant between-sides differences were found for muscle thickness (paretic: 1.5 ± 0.3 cm, non-paretic: 1.6 ± 0.2 cm, p = 0.255) or fascicle length (paretic: 6.6 ± 1.9 cm, non-paretic: 7.1 ± 2.2 cm, p = 0.216). Significant between-groups difference was also observed for fascicle length [non-dominant side (control): 6.2 ± 0.8 cm, paretic side (stroke): 6.6 ± 1.9 cm, p = 0.017]. INTERPRETATION: Although muscle mechanical properties increased exponentially over the slack ankle, measures between paretic and non-paretic sides were similar in ambulating participants with chronic stroke. Side-to-side differences in structural and morphological measures suggest the impact of stroke was relatively more pronounced for these muscle parameters than for passive mechanical properties.

Hemodynamic and morphological changes of the central retinal artery in myopic eyes.

Due to excessive elongation of the eyeball, myopia-related vascular abnormalities are frequently observed in the central retinal artery (CRA) and its intraretinal branches. In addition to inconsistency in previously reported findings, hemodynamic (reduced flow velocity, increased vascular resistance) and morphological changes (narrower vessel diameter) were usually studied separately. This cross-sectional study evaluated the hemodynamic and morphological characteristics concurrently in a large sample of healthy myopes, by using the color Doppler ultrasound and adaptive optics retinal camera. Results showed that the retrobulbar segment of CRA had a tendency of slightly reduced flow velocity in eyeballs with longer axial length, but the correlation was not significant after adjusting for the multiple correlations. Vascular resistance was not affected by the axial elongation. With respect to the intraretinal branches, no significant changes in longer eyes of total diameter or lumen diameter were observed, while both the wall thickness and the wall cross-sectional area were significantly increased, but only a marginally increase in the wall to lumen ratio was found with increasing axial length. This implies some potential small artery remodeling in the intraretinal CRA branches. Overall, blood supply of the inner retina in healthy young myopes is likely to be maintained. Additionally, morphological parameters of vascular microstructure could be potential biomarkers to monitor myopia progression and understand myopia-related vascular abnormalities in future studies.