Background: Three-dimensional (3D) MR elastography (MRE) derives viscoelastic parameters that may reflect inflammation, but their frequency dependence and the influence of steatosis on inflammation grading and fibrosis staging remain unclear.

Purpose: To investigate 3D multifrequency MRE for assessing hepatic inflammation, fibrosis stage across frequencies, and the influence of steatosis.

Study Type: Prospective.

Population: Sixty-four (40 men, median age: 58 years) participants with chronic liver disease (CLD); 21 (8 men, median age:28 years) healthy volunteers.

Field Strength/Sequence: 3-T; gradient-echo sequence with mechanical vibrations at low (16.7 and 18 Hz), medium (33.4 and 36 Hz), and high (50.1 and 54 Hz) frequencies.

Assessment: In CLD participants, MRE-derived viscoelastic parameters, shear stiffness, storage modulus, loss modulus, and damping ratio were compared with histologically assessed fibrosis, inflammation, and steatosis. MRE test–retest repeatability over 10 min was evaluated in healthy volunteers.

Statistical Tests: Wilcoxon rank sum test, Spearman's correlation, multivariable regression analysis, and area under the receiver operating curve (AUROC). A p value of < 0.05 was considered statistically significant.

Results: Inflammation was significantly independently associated with damping ratio at medium frequency, which showed moderate performance for grading inflammation (AUROC = 0.76–0.83, sensitivity = 0.83–0.84, specificity = 0.70–0.79). Fibrosis staging using shear stiffness and moduli showed high diagnostic performance (AUROC = 0.82–0.95), with comparable accuracy between medium and high frequencies (p = 0.327–0.896). Steatosis was not significantly correlated with MRE overall (p = 0.212–0.459), but was significantly associated with 19% higher stiffness and 20% higher loss modulus at medium frequency in CLD participants without fibrosis or inflammation.

Data Conclusion: Medium frequency 3D MRE demonstrated an independent association with inflammation while preserving accurate fibrosis assessment. Steatosis seemed not to confound MRE-based evaluation.

Level of Evidence: 1.

Technical Efficacy: Stage 2.

Plain Language Summary: Chronic liver disease can cause both inflammation and scarring (fibrosis). Accurate assessment usually requires a biopsy, which is invasive. This study evaluated a noninvasive imaging method called three-dimensional magnetic resonance elastography (3D MRE) in patients who underwent same-day liver biopsy. The researchers tested whether different vibration frequencies could detect inflammation and fibrosis. They found that medium frequency measurements were associated with liver inflammation while still accurately identifying fibrosis. Fat accumulation in the liver did not significantly affect the results. These findings suggest that 3D MRE may help medical doctors assess liver inflammation and fibrosis noninvasively in a single examination.

Background:

The ultrasound-guided attenuation parameter is well established for hepatic steatosis detection in metabolic dysfunction-associated steatotic liver disease. The diagnostic performance of ultrasound-guided attenuation parameter was evaluated using different numbers of measurements at different lateral locations to detect hepatic steatosis ⩾ S1 in male and female patients with metabolic dysfunction-associated steatotic liver disease.

Methods:

A metabolic dysfunction-associated steatotic liver disease cohort was prospectively enrolled in autumn of 2022. Ultrasound-guided attenuation parameter values obtained through one to five measurements, performed at single and multiple locations, were compared with proton density fat fraction. Presence of hepatic steatosis (i.e. ⩾ S1) with ultrasound-guided attenuation parameter was defined as a proton density fat fraction of ⩾ 5%. Diagnostic performance was evaluated based on the area under the receiver operating characteristic curve.

Results:

Included 60 participants with an even sex distribution. Ultrasound-guided attenuation parameter diagnostic performance to detect hepatic steatosis ⩾ S1 did not significantly differ according to the number of measurements (from 1 to 5), different lateral locations, or patient sex. Ultrasound-guided attenuation parameter performed using five measurements in one location exhibited a receiver operating characteristic curve of 0.87 (95% confidence interval: 0.78, 0.97), and a threshold of 0.53 dB/cm/MHz, yielding 90% sensitivity and 65% specificity. Three measurements in multiple lateral locations exhibited a receiver operating characteristic curve of 0.91 (95% confidence interval: 0.84, 0.98), with a threshold of 0.58 dB/cm/MHz, yielding 95% sensitivity and 75% specificity.

Conclusion:

Ultrasound-guided attenuation parameter diagnostic performance to detect hepatic steatosis ⩾ S1 in metabolic dysfunction-associated steatotic liver disease is similar with regions of interest in single versus multiple lateral locations. Three measurements in multiple lateral locations appear sufficient to detect hepatic steatosis, which must be evaluated for all hepatic steatosis stages.

Brain tumors are the most common solid tumors in children and young adults, but the scarcity of large histopathology datasets has limited the application of computational pathology in this group. This study implements two weakly supervised multiple-instance learning (MIL) approaches on patch features obtained from state-of-the-art histology-specific foundation models to classify pediatric brain tumors in hematoxylin and eosin whole slide images (WSIs) from a multi-center Swedish cohort. WSIs from 540 subjects (age 8.5 ± 4.9 years) diagnosed with brain tumors were gathered from the six Swedish university hospitals. Instance (patch)-level features were obtained from WSIs using three pre-trained feature extractors: ResNet50, UNI, and CONCH. Instances were aggregated using attention-based MIL (ABMIL) or clustering-constrained attention MIL (CLAM) for patient-level classification. Models were evaluated on three classification tasks based on the hierarchical classification of pediatric brain tumors: tumor category, family, and type. Model generalization was assessed by training on data from two of the centers and testing on data from four other centers. Model interpretability was evaluated through attention mapping. The highest classification performance was achieved using UNI features and ABMIL aggregation, with Matthew's correlation coefficient of 0.76 ± 0.04, 0.63 ± 0.04, and 0.60 ± 0.05 for tumor category, family, and type classification, respectively. When evaluating generalization, models utilizing UNI and CONCH features outperformed those using ResNet50. However, the drop in performance from the in-site to out-of-site testing was similar across feature extractors. These results show the potential of state-of-the-art computational pathology methods in diagnosing pediatric brain tumors at different hierarchical levels with fair generalizability on a multi-center national dataset.

Objective: Primary sclerosing cholangitis (PSC) is a rare broin ammatory hepatobiliary disease with a highly variable clinical course. Identifying patients at risk for poor outcomes remains challenging. Magnetic resonance imaging (MRI)-based approaches such as DiStrict, Anali score, and relative enhancement (RE) show promise but are limited by operator dependency or static measurements. This study explored pharmacokinetic modelling of liver function as a quantitative imaging biomarker for risk assessment in PSC.

Materials and methods: A prospective cohort of 26 PSC patients underwent up to ve annual MRI examinations with follow-up up to 7.5 years. Clinical endpoints included liver transplantation, decompensated cirrhosis, and cholangiocarcinoma. Correlation and receiver operating characteristics (ROC) analyses compared the pharmacokinetic model with Anali scores, RE, model for end-stage liver disease (MELD), and the Amsterdam–Oxford Model (AOM).

Results: The pharmacokinetic model (ksingle) correlated signi cantly with MELD (r = -0.429, p= 0.029), AOM (r = -0.557, p= 0.003), and endpoint events (r = -0.605, p= 0.001). ROC analysis showed excellent discrimination for ki,single (area under the curve [AUC]= 0.943) outperformed Anali scores (AUC= 0.800–0.829) and comparable to MELD (AUC= 0.857) and AOM (AUC= 0.900).

Conclusion: Pharmacokinetic liver function modelling correlated strongly with MELD and AOM, effectively identifying high-risk PSC patients.

Relevance statement: Pharmacokinetic liver function modelling detects functional impairment in PSC, correlating well with established tools such as the AOM. As an objective, quantitative imaging biomarker, this method may complement established risk scores and aid in the identi cation of patients at risk of adverse outcomes.

Key Points:

● Pharmacokinetic modelling estimates changes in liver function based on MRI.

● These estimates can be used as a prognostic tool in PSC.

● The model’s prognostic performance was comparable to established clinical tests.

Background and aims The prevalence of metabolic dysfunction-associated steatotic liver disease (MASLD) has increased during the epidemic of obesity. Type 2 diabetes mellitus (T2DM) is associated with progressive MASLD. Therefore, many guidelines recommend screening for MASLD in patients with T2DM. Most studies in patients with MASLD have been conducted in specialist care. We investigated the prevalence and severity of MASLD in patients with T2DM from primary care. Methods Patients with T2DM were prospectively included from primary care facilities to undergo transient elastography with controlled attenuation parameter and whole-body magnetic resonance imaging (MRI) to assess liver fat, cardiac function, muscle composition, and distribution of body fat. Results Among 308 participants, 59% had MASLD, 7% had suspected advanced fibrosis (transient elastography &gt;= 10 kPa), and 1.9% had cirrhosis. The mean age was 63.9 +/- 8.1 years; 37% were female, with no differences between the MASLD and the non-MASLD groups. Participants with MASLD had greater body mass index (31.1 +/- 4.4 vs. 27.4 +/- 4.1 kg/m(2), p &lt; 0.001) and a higher prevalence of obesity (60% vs. 21%, p &lt; 0.001). Obesity increased the risk of fibrotic MASLD eightfold, as confirmed by multivariable analysis. Participants with MASLD also had increased visceral and abdominal subcutaneous adipose tissue and muscle fat infiltration. On cardiac MRI, participants with MASLD had a lower left ventricular (LV) stroke volume index, a lower LV end-diastolic volume index, and an increased LV concentricity. Conclusions In this cohort of primary care patients with T2DM, 59% had MASLD, and 7% had suspected advanced fibrosis. Obesity was a strong predictor of fibrotic MASLD. MASLD was associated with alterations to the left ventricle and increased deposition of ectopic fat.

Centralized collection and processing of healthcare data across national borders pose significant challenges, including privacy concerns, data heterogeneity, and legal barriers. To study some of these challenges, we formed an interdisciplinary consortium to develop a federated health data network, comprised of six institutions across five countries, to facilitate Nordic-Baltic cooperation on secondary use of health data. The objective of this report is to offer early insights into our experiences developing this network. We employed a mixed-methods approach, combining both experimental design and implementation science to assess the factors influencing the implementation of our network. Technically, our experiments indicate that the network functions without significant performance degradation compared to centralized simulation. While use of interdisciplinary approaches holds a potential to solve challenges associated with establishing such collaborative networks, our findings turn the spotlight on the uncertain regulatory landscape playing catch up and the significant operational costs.

Introduction: The pathophysiology behind essential tremor (ET) and the mechanisms behind the clinical effect after deep brain stimulation (DBS) is not fully understood. This article aims to increase the understanding of ET pathophysiology and the mechanisms behind DBS using magnetic resonance spectroscopy (1H-MRS). Patients with ET underwent MRS scans of the cerebellum and thalamus before and after DBS, and the results were compared to a healthy control group.

Methods: Ten ET patients and seven healthy controls were included. Preoperatively and ~5 months postoperatively, single-voxel MRS was performed on a 1.5 T (tesla) system. Voxels were placed in the thalamus (14 × 13 × 13 mm3), dentate nucleus (13 × 13 × 13 mm3), and cerebellar cortex (13 × 13 × 13 mm3). Metabolite concentrations of total N-acetylaspartate + N-acetyl-aspartyl-glutamate (tNA), total creatine + phosphocreatine (tCr), total choline + phosphocholine + glycerophosphocholine (tCho), and total glutamate and glutamine, which together constitute Glx, were quantified. The patients were evaluated with the Essential Tremor Rating Scale (ETRS) preoperatively and postoperatively.

Results: A total of 14 leads were implanted, and ETRS scores improved significantly following surgery. Thalamic tNA concentrations reduced significantly within the patient group after surgery, as well as in comparison to healthy control values. A significant difference in tNA concentration following surgery was observed only in the thalamus that had been targeted with a lead, not elsewhere. No additional differences in metabolite concentrations (tCr, tCho, Glx) were observed in the thalamic voxel, and none of the studied metabolites (tNA, tCr, tCho, Glx) showed detectable differences in the cerebellar voxels (dentate nucleus and cerebellar cortex).

Conclusion: In a highly selected patient group affected by ET, we present novel metabolite information using MRS. Specifically, a reduced thalamic tNA concentration was observed on the lead-implanted side following DBS, suggesting a possible treatment effect.

Purpose: To implement and evaluate deep learning-based methods for the classification of pediatric brain tumors in MR data.

Materials and methods: A subset of the “Children’s Brain Tumor Network” dataset was retrospectively used (n=178 subjects, female=72, male=102, NA=4, age-range [0.01, 36.49] years) with tumor types being low-grade astrocytoma (n=84), ependymoma (n=32), and medulloblastoma (n=62). T1w post-contrast (n=94 subjects), T2w (n=160 subjects), and ADC (n=66 subjects) MR sequences were used separately. Two deep-learning models were trained on transversal slices showing tumor. Joint fusion was implemented to combine image and age data, and two pre-training paradigms were utilized. Model explainability was investigated using gradient-weighted class activation mapping (Grad-CAM), and the learned feature space was visualized using principal component analysis (PCA).

Results: The highest tumor-type classification performance was achieved when using a vision transformer model pre-trained on ImageNet and fine-tuned on ADC images with age fusion (MCC: 0.77 ± 0.14 Accuracy: 0.87 ± 0.08), followed by models trained on T2w (MCC: 0.58 ± 0.11, Accuracy: 0.73 ± 0.08) and T1w post-contrast (MCC: 0.41 ± 0.11, Accuracy: 0.62 ± 0.08) data. Age fusion marginally improved the model’s performance. Both model architectures performed similarly across the experiments, with no differences between the pre-training strategies. Grad-CAMs showed that the models’ attention focused on the brain region. PCA of the feature space showed greater separation of the tumor-type clusters when using contrastive pre-training.

Conclusion: Classification of pediatric brain tumors on MR-images could be accomplished using deep learning, with the top-performing model being trained on ADC data, which is used by radiologists for the clinical classification of these tumors.

Objective: In non-alcoholic fatty liver disease (NAFLD), liver fibrosis is the strongest predictor of adverse outcomes. We sought to investigate the relationship between liver fibrosis and cardiac remodeling in participants from the general population using magnetic resonance imaging (MRI), as well as explore potential mechanistic pathways by analyzing circulating cardiovascular biomarkers.

Methods: In this cross-sectional study, we prospectively included participants with type 2 diabetes and individually matched controls from the SCAPIS (Swedish CArdioPulmonary bioImage Study) cohort in Linköping, Sweden. Between November 2017 and July 2018, participants underwent MRI at 1.5 Tesla for quantification of liver proton density fat fraction (spectroscopy), liver fibrosis (stiffness from elastography), left ventricular (LV) structure and function, as well as myocardial native T1 mapping. We analyzed 278 circulating cardiovascular biomarkers using a Bayesian statistica lapproach.

Results: In total, 92 participants were enrolled (mean age 59.5 ± 4.6 years, 32 women). The mean liver stiffness was 2.1 ± 0.4 kPa. 53 participants displayed hepatic steatosis. LV concentricity increased across quartiles of liver stiffness. Neither liver fat nor liver stiffness displayed any relationships to myocardial tissue characteristics (native T1). In a regression analysis, liver stiffness was related to increased LV concentricity. This association was independent of diabetes and liver fat (Beta = 0.26, p = 0.0053), but was attenuated (Beta = 0.17, p = 0.077) when also adjusting for circulating levels of interleukin-1 receptor type 2.

Conclusion: MRI reveals that liver fibrosis is associated to structural LV remodeling, in terms of increased concentricity, in participants from the general population. This relationship could involve the interleukin-1 signaling.

BACKGROUND: Drug induced liver injury (DILI) is a major concern when developing new drugs. A promising biomarker for DILI is the hepatic uptake rate of the contrast agent gadoxetate. This rate can be estimated using a novel approach combining magnetic resonance imaging and mathematical modeling. However, previous work has used different mathematical models to describe liver function in humans or rats, and no comparative study has assessed which model is most optimal to use, or focused on possible translatability between the two species.

AIMS: Our aim was therefore to do a comparison and assessment of models for DILI biomarker assessment, and to develop a conceptual basis for a translational framework between the species.

METHODS AND RESULTS: We first established which of the available pharmacokinetic models to use by identifying the most simple and identifiable model that can describe data from both human and rats. We then developed an extension of this model for how to estimate the effects of a hepatotoxic drug in rats. Finally, we illustrated how such a framework could be useful for drug dosage selection, and how it potentially can be applied in personalized treatments designed to avoid DILI.

CONCLUSION: Our analysis provides clear guidelines of which mathematical model to use for model-based assessment of biomarkers for liver function, and it also suggests a hypothetical path to a translational framework for DILI.