Comparison of Vendor-Independent Software Tools for Liver Proton Density Fat Fraction Estimation at 1.5 T.

(1) Background: Open-source software tools are available to estimate proton density fat fraction (PDFF). (2) Methods: We compared four algorithms: complex-based with graph cut (GC), magnitude-based (MAG), magnitude-only estimation with Rician noise modeling (MAG-R), and multi-scale quadratic pseudo-Boolean optimization with graph cut (QPBO). The accuracy and reliability of the methods were evaluated in phantoms with known fat/water ratios and a patient cohort with various grades (S0-S3) of steatosis. Image acquisitions were performed at 1.5 Tesla (T). (3) Results: The PDFF estimates showed a nearly perfect correlation (Pearson r = 0.999, p < 0.001) and inter-rater agreement (ICC = from 0.995 to 0.999, p < 0.001) with true fat fractions. The absolute bias was low with all methods (0.001-1%), and an ANCOVA detected no significant difference between the algorithms in vitro. The agreement across the methods was very good in the patient cohort (ICC = 0.891, p < 0.001). However, MAG estimates (-2.30% ± 6.11%, p = 0.005) were lower than MAG-R. The field inhomogeneity artifacts were most frequent in MAG-R (70%) and GC (39%) and absent in QPBO images. (4) Conclusions: The tested algorithms all accurately estimate PDFF in vitro. Meanwhile, QPBO is the least affected by field inhomogeneity artifacts in vivo.

The Calculation and Evaluation of an Ultrasound-Estimated Fat Fraction in Non-Alcoholic Fatty Liver Disease and Metabolic-Associated Fatty Liver Disease.

We aimed to develop a non-linear regression model that could predict the fat fraction of the liver (UEFF), similar to magnetic resonance imaging proton density fat fraction (MRI-PDFF), based on quantitative ultrasound (QUS) parameters. We measured and retrospectively collected the ultrasound attenuation coefficient (AC), backscatter-distribution coefficient (BSC-D), and liver stiffness (LS) using shear wave elastography (SWE) in 90 patients with clinically suspected non-alcoholic fatty liver disease (NAFLD), and 51 patients with clinically suspected metabolic-associated fatty liver disease (MAFLD). The MRI-PDFF was also measured in all patients within a month of the ultrasound scan. In the linear regression analysis, only AC and BSC-D showed a significant association with MRI-PDFF. Therefore, we developed prediction models using non-linear least squares analysis to estimate MRI-PDFF based on the AC and BSC-D parameters. We fitted the models on the NAFLD dataset and evaluated their performance in three-fold cross-validation repeated five times. We decided to use the model based on both parameters to calculate UEFF. The correlation between UEFF and MRI-PDFF was strong in NAFLD and very strong in MAFLD. According to a receiver operating characteristics (ROC) analysis, UEFF could differentiate between <5% vs. ≥5% and <10% vs. ≥10% MRI-PDFF steatosis with excellent, 0.97 and 0.91 area under the curve (AUC), accuracy in the NAFLD and with AUCs of 0.99 and 0.96 in the MAFLD groups. In conclusion, UEFF calculated from QUS parameters is an accurate method to quantify liver fat fraction and to diagnose ≥5% and ≥10% steatosis in both NAFLD and MAFLD. Therefore, UEFF can be an ideal non-invasive screening tool for patients with NAFLD and MAFLD risk factors.

Evaluation of Artificial Intelligence-Calculated Hepatorenal Index for Diagnosing Mild and Moderate Hepatic Steatosis in Non-Alcoholic Fatty Liver Disease.

Background and Objectives: This study aims to evaluate artificial intelligence-calculated hepatorenal index (AI-HRI) as a diagnostic method for hepatic steatosis. Materials and Methods: We prospectively enrolled 102 patients with clinically suspected non-alcoholic fatty liver disease (NAFLD). All patients had a quantitative ultrasound (QUS), including AI-HRI, ultrasound attenuation coefficient (AC,) and ultrasound backscatter-distribution coefficient (SC) measurements. The ultrasonographic fatty liver indicator (US-FLI) score was also calculated. The magnetic resonance imaging fat fraction (MRI-PDFF) was the reference to classify patients into four grades of steatosis: none < 5%, mild 5-10%, moderate 10-20%, and severe ≥ 20%. We compared AI-HRI between steatosis grades and calculated Spearman's correlation (rs) between the methods. We determined the agreement between AI-HRI by two examiners using the intraclass correlation coefficient (ICC) of 68 cases. We performed a receiver operating characteristics (ROC) analysis to estimate the area under the curve (AUC) for AI-HRI. Results: The mean AI-HRI was 2.27 (standard deviation, ±0.96) in the patient cohort. The AI-HRI was significantly different between groups without (1.480 ± 0.607, p < 0.003) and with mild steatosis (2.155 ± 0.776), as well as between mild and moderate steatosis (2.777 ± 0.923, p < 0.018). AI-HRI showed moderate correlation with AC (rs = 0.597), SC (rs = 0.473), US-FLI (rs = 0.5), and MRI-PDFF (rs = 0.528). The agreement in AI-HRI was good between the two examiners (ICC = 0.635, 95% confidence interval (CI) = 0.411-0.774, p < 0.001). The AI-HRI could detect mild steatosis (AUC = 0.758, 95% CI = 0.621-0.894) with fair and moderate/severe steatosis (AUC = 0.803, 95% CI = 0.721-0.885) with good accuracy. However, the performance of AI-HRI was not significantly different (p < 0.578) between the two diagnostic tasks. Conclusions: AI-HRI is an easy-to-use, reproducible, and accurate QUS method for diagnosing mild and moderate hepatic steatosis.

Microvascular flow imaging to differentiate focal hepatic lesions: the spoke-wheel pattern as a specific sign of focal nodular hyperplasia.

Microvascular flow imaging (MVFI) is an advanced Doppler ultrasound technique designed to detect slow-velocity blood flow in small-caliber microvessels. This technique is capable of realtime, highly detailed visualization of tumor vessels without using a contrast agent. MVFI has been recently applied for the characterization of focal liver lesions and has revealed typical vascularity distributions in multiple types thereof. Focal nodular hyperplasia (FNH) constitutes an important differential diagnosis of malignant liver tumors. In this essay, we provide iconographic documentation of the MVFI appearance of FNH and other common solid liver lesions. Identifying the typical patterns of vascularity, including the spoke-wheel pattern with MVFI, can expedite the diagnosis, spare patients from unnecessary procedures, and save costs.

Radiomics analysis of contrast-enhanced CT scans can distinguish between clear cell and non-clear cell renal cell carcinoma in different imaging protocols.

Introduction: This study aimed to construct a radiomics-based machine learning (ML) model for differentiation between non-clear cell and clear cell renal cell carcinomas (ccRCC) that is robust against institutional imaging protocols and scanners. Materials and methods: Preoperative unenhanced (UN), corticomedullary (CM), and excretory (EX) phase CT scans from 209 patients diagnosed with RCCs were retrospectively collected. After the three-dimensional segmentation, 107 radiomics features (RFs) were extracted from the tumor volumes in each contrast phase. For the ML analysis, the cases were randomly split into training and test sets with a 3:1 ratio. Highly correlated RFs were filtered out based on Pearson's correlation coefficient (r > 0.95). Intraclass correlation coefficient analysis was used to select RFs with excellent reproducibility (ICC ≥ 0.90). The most predictive RFs were selected by the least absolute shrinkage and selection operator (LASSO). A support vector machine algorithm-based binary classifier (SVC) was constructed to predict tumor types and its performance was evaluated based-on receiver operating characteristic curve (ROC) analysis. The "Kidney Tumor Segmentation 2019" (KiTS19) publicly available dataset was used during external validation of the model. The performance of the SVC was also compared with an expert radiologist's. Results: The training set consisted of 121 ccRCCs and 38 non-ccRCCs, while the independent internal test set contained 40 ccRCCs and 13 non-ccRCCs. For external validation, 50 ccRCCs and 23 non-ccRCCs were identified from the KiTS19 dataset with the available UN, CM, and EX phase CTs. After filtering out the highly correlated and poorly reproducible features, the LASSO algorithm selected 10 CM phase RFs that were then used for model construction. During external validation, the SVC achieved an area under the ROC curve (AUC) value, accuracy, sensitivity, and specificity of 0.83, 0.78, 0.80, and 0.74, respectively. UN and/or EX phase RFs did not further increase the model's performance. Meanwhile, in the same comparison, the expert radiologist achieved similar performance with an AUC of 0.77, an accuracy of 0.79, a sensitivity of 0.84, and a specificity of 0.69. Conclusion: Radiomics analysis of CM phase CT scans combined with ML can achieve comparable performance with an expert radiologist in differentiating ccRCCs from non-ccRCCs.

Tissue attenuation imaging and tissue scatter imaging for quantitative ultrasound evaluation of hepatic steatosis.

We aimed to assess the feasibility of ultrasound-based tissue attenuation imaging (TAI) and tissue scatter distribution imaging (TSI) for quantification of liver steatosis in patients with nonalcoholic fatty liver disease (NAFLD). We prospectively enrolled 101 participants with suspected NAFLD. The TAI and TSI measurements of the liver were performed with a Samsung RS85 Prestige ultrasound system. Based on the magnetic resonance imaging proton density fat fraction (MRI-PDFF), patients were divided into ≤5%, 5-10%, and ≥10% of MRI-PDFF groups. We determined the correlation between TAI, TSI, and MRI-PDFF and used multiple linear regression analysis to identify any association with clinical variables. The diagnostic performance of TAI, TSI was determined based on the area under the receiver operating characteristic curve (AUC). The intraclass correlation coefficient (ICC) was calculated to assess interobserver reliability. Both TAI (rs = 0.78, P < .001) and TSI (rs = 0.68, P < .001) showed significant correlation with MRI-PDFF. TAI overperformed TSI in the detection of both ≥5% MRI-PDFF (AUC = 0.89 vs 0.87) and ≥10% (AUC = 0.93 vs 0.86). MRI-PDFF proved to be an independent predictor of TAI (ß = 1.03; P < .001), while both MRI-PDFF (ß = 50.9; P < .001) and liver stiffness (ß = -0.86; P < .001) were independent predictors of TSI. Interobserver analysis showed excellent reproducibility of TAI (ICC = 0.95) and moderate reproducibility of TSI (ICC = 0.73). TAI and TSI could be used successfully to diagnose and estimate the severity of hepatic steatosis in routine clinical practice.

Focal Liver Lesion MRI Feature Identification Using Efficientnet and MONAI: A Feasibility Study.

Liver tumors constitute a major part of the global disease burden, often making regular imaging follow-up necessary. Recently, deep learning (DL) has increasingly been applied in this research area. How these methods could facilitate report writing is still a question, which our study aims to address by assessing multiple DL methods using the Medical Open Network for Artificial Intelligence (MONAI) framework, which may provide clinicians with preliminary information about a given liver lesion. For this purpose, we collected 2274 three-dimensional images of lesions, which we cropped from gadoxetate disodium enhanced T1w, native T1w, and T2w magnetic resonance imaging (MRI) scans. After we performed training and validation using 202 and 65 lesions, we selected the best performing model to predict features of lesions from our in-house test dataset containing 112 lesions. The model (EfficientNetB0) predicted 10 features in the test set with an average area under the receiver operating characteristic curve (standard deviation), sensitivity, specificity, negative predictive value, positive predictive value of 0.84 (0.1), 0.78 (0.14), 0.86 (0.08), 0.89 (0.08) and 0.71 (0.17), respectively. These results suggest that AI methods may assist less experienced residents or radiologists in liver MRI reporting of focal liver lesions.

Pharmacological cardioversion of atrial fibrillation--a double-blind, randomized, placebo-controlled, multicentre, dose-escalation study of AZD1305 given intravenously.

AIM: AZD1305 is a combined ion channel blocker developed for the treatment of atrial fibrillation (AF). The aim of this study was to determine whether AZD1305 was effective in converting AF to sinus rhythm (SR). METHODS AND RESULTS: Patients with AF episodes of duration 3 h to 3 months were randomized in a 3:1 ratio to receive a maximum 30 min intravenous infusion of AZD1305 or matching placebo. The primary efficacy endpoint was the proportion of patients converting within 90 min of the start of infusion, after which patients who had not converted were to undergo direct current (DC) cardioversion. Four ascending AZD1305 dose groups were assigned sequentially, with dose rates of 50, 100, 130, and 180 mg/h. A total of 171 patients were randomized. Pharmacological conversion was achieved in 0 of 43 patients (0%) in the placebo group, and in 2 of 26 (8%; P= 0.14 vs. placebo), 8 of 45 (18%; P= 0.006), 17 of 45 (38%; P< 0.001), and 6 of 12 patients (50%; P< 0.001) in AZD1305 dose groups 1-4, respectively. Maximum QTcF (QT interval corrected according to Fridericia's formula) generally increased dose-dependently up to a plateau, although there was wide variation between patients. Two patients experienced torsade de pointes (TdP): one patient without symptoms in dose group 3, and one patient requiring DC defibrillation in dose group 4. Both patients recovered without sequelae. CONCLUSIONS: AZD1305 was effective in converting AF to SR, but was associated with QT prolongation and TdP. The benefit-risk profile was judged as unfavourable and the AZD1305 development programme was discontinued. CLINICAL TRIAL REGISTRATION: http://clinicaltrials.gov identifier NCT00915356.

Safe and effective conversion of persistent atrial fibrillation to sinus rhythm by intravenous AZD7009.

BACKGROUND: Acute drug conversion of persistent atrial fibrillation usually fails. OBJECTIVES: The purpose of this study was to test the proarrhythmic potential, safety, and efficacy of the novel antiarrhythmic agent AZD7009 in patients with persistent atrial fibrillation (AF) or atrial flutter (mean duration 43 days) scheduled for direct current (DC) cardioversion. METHODS: Patients were randomized to AZD7009 (3-hour intravenous infusion; n = 86) or placebo (n = 36). AZD7009 was given in doses intended to produce target pseudo-steady-state plasma levels of 0.25, 0.50, 0.75, 1.0, 1.5, 2.0, or 2.5 micromol/L after 30 minutes of infusion. DC cardioversion was performed if conversion to sinus rhythm (SR) did not occur within 2 hours of infusion. RESULTS: AZD7009 in a concentration-dependent manner increased the rate of conversion of AF to SR and shortened the time to conversion. At the three highest target concentrations of AZD7009, 45%, 64%, and 70% of AF patients converted after a mean time of 62, 55, and 26 minutes, respectively, whereas no placebo-treated patients converted. SR was maintained for 24 hours in 21 of 22 patients with drug-associated conversion. AZD7009 treatment was associated with QT-interval prolongation; the increase in QT corrected according to Fridericia typically ranged from 40 to 80 ms at targeted pseudo-steady-state plasma concentrations >or=0.75 micromol/L, but a number of outliers with QT corrected according to Fridericia >550 ms were seen in the higher concentration groups, particularly after conversion to SR and prolonged infusion. None of the patients exhibited torsades de pointes according to predefined criteria; however, one patient exhibited a nonsustained, polymorphic ventricular tachycardia of eight beats with torsades de pointes-like features after AZD7009 infusion (asymptomatic and discovered only upon retrospective Holter tape analysis). Clinical adverse events (primarily dizziness, bradycardia, hypotension, and nausea) were significantly more common in the highest target concentration AZD7009 group vs placebo (P <.001). CONCLUSION: AZD7009 exhibited dose-dependent effects in converting AF to SR in AF patients and appeared to be associated with a low risk of proarrhythmia despite continued administration during a period of heightened vulnerability.

Effect of simvastatin on endothelial function in cardiac syndrome X patients.

Patients with cardiac syndrome X with mild hypercholesterolemia were randomized to placebo (n = 20) or simvastatin 20 mg/day (n = 20). In the simvastatin group, there was a significant (26%; p < 0.0001) decrease in total cholesterol, a 38% (p < 0.0001) decrease in low-density lipoprotein cholesterol levels, and 7% a (p < 0.0001) increase in high-density lipoprotein cholesterol levels, without significant changes in triglyceride levels. Brachial artery flow-mediated dilation increased significantly (52% relative increase, p < 0.0001), and the time to > 1-mm ST-segment depression during stress testing was longer by the end of the study (p < 0.0001).