Cardiac remodelling in non-alcoholic fatty liver disease in the general population.

BACKGROUND AND AIMS: Non-alcoholic fatty liver disease (NAFLD) is associated with increased risk for cardiovascular disease. Our study investigates the contribution of NAFLD to changes in cardiac structure and function in a general population. METHODS: One thousand ninety-six adults (49.3% female) from the Study of Health in Pomerania underwent magnetic resonance imaging including cardiac and liver imaging. The presence of NAFLD by proton density fat fraction was related to left cardiac structure and function. Results were adjusted for clinical confounders using multivariable linear regression model. RESULTS: The prevalence for NAFLD was 35.9%. In adjusted multivariable linear regression models, NAFLD was positively associated with higher left ventricular mass index (ß = 0.95; 95% confidence interval (CI): 0.45; 1.45), left ventricular concentricity (ß = 0.043; 95% CI: 0.031; 0.056), left ventricular end-diastolic wall thickness (ß = 0.29; 95% CI: 0.20; 0.38), left atrial end-diastolic volume index (ß = 0.67; 95% CI: 0.01; 1.32) and inversely associated with left ventricular end-diastolic volume index (ß = -0.78; 95% CI: -1.51; -0.05). When stratified by sex, we only found significant positive associations of NAFLD with left ventricular mass index, left atrial end-diastolic volume index, left ventricular cardiac output and an inverse association with global longitudinal strain in women. In contrast, men had an inverse association with left ventricular end-diastolic volume index and left ventricular stroke volume. Higher liver fat content was stronger associated with higher left ventricular mass index, left ventricular concentricity and left ventricular end-diastolic wall thickness. CONCLUSION: NAFLD is associated with cardiac remodelling in the general population showing sex specific patterns in cardiac structure and function.

Addressing challenges in radiomics research: systematic review and repository of open-access cancer imaging datasets.

OBJECTIVES: Open-access cancer imaging datasets have become integral for evaluating novel AI approaches in radiology. However, their use in quantitative analysis with radiomics features presents unique challenges, such as incomplete documentation, low visibility, non-uniform data formats, data inhomogeneity, and complex preprocessing. These issues may cause problems with reproducibility and standardization in radiomics studies. METHODS: We systematically reviewed imaging datasets with public copyright licenses, published up to March 2023 across four large online cancer imaging archives. We included only datasets with tomographic images (CT, MRI, or PET), segmentations, and clinical annotations, specifically identifying those suitable for radiomics research. Reproducible preprocessing and feature extraction were performed for each dataset to enable their easy reuse. RESULTS: We discovered 29 datasets with corresponding segmentations and labels in the form of health outcomes, tumor pathology, staging, imaging-based scores, genetic markers, or repeated imaging. We compiled a repository encompassing 10,354 patients and 49,515 scans. Of the 29 datasets, 15 were licensed under Creative Commons licenses, allowing both non-commercial and commercial usage and redistribution, while others featured custom or restricted licenses. Studies spanned from the early 1990s to 2021, with the majority concluding after 2013. Seven different formats were used for the imaging data. Preprocessing and feature extraction were successfully performed for each dataset. CONCLUSION: RadiomicsHub is a comprehensive public repository with radiomics features derived from a systematic review of public cancer imaging datasets. By converting all datasets to a standardized format and ensuring reproducible and traceable processing, RadiomicsHub addresses key reproducibility and standardization challenges in radiomics. CRITICAL RELEVANCE STATEMENT: This study critically addresses the challenges associated with locating, preprocessing, and extracting quantitative features from open-access datasets, to facilitate more robust and reliable evaluations of radiomics models. KEY POINTS: - Through a systematic review, we identified 29 cancer imaging datasets suitable for radiomics research. - A public repository with collection overview and radiomics features, encompassing 10,354 patients and 49,515 scans, was compiled. - Most datasets can be shared, used, and built upon freely under a Creative Commons license. - All 29 identified datasets have been converted into a common format to enable reproducible radiomics feature extraction.

Automated localization and segmentation of cervical lymph nodes on contrast-enhanced CT using a 3D foveal fully convolutional neural network.

BACKGROUND: In the management of cancer patients, determination of TNM status is essential for treatment decision-making and therefore closely linked to clinical outcome and survival. Here, we developed a tool for automatic three-dimensional (3D) localization and segmentation of cervical lymph nodes (LNs) on contrast-enhanced computed tomography (CECT) examinations. METHODS: In this IRB-approved retrospective single-center study, 187 CECT examinations of the head and neck region from patients with various primary diseases were collected from our local database, and 3656 LNs (19.5 ± 14.9 LNs/CECT, mean ± standard deviation) with a short-axis diameter (SAD) ≥ 5 mm were segmented manually by expert physicians. With these data, we trained an independent fully convolutional neural network based on 3D foveal patches. Testing was performed on 30 independent CECTs with 925 segmented LNs with an SAD ≥ 5 mm. RESULTS: In total, 4,581 LNs were segmented in 217 CECTs. The model achieved an average localization rate (LR), i.e., percentage of localized LNs/CECT, of 78.0% in the validation dataset. In the test dataset, average LR was 81.1% with a mean Dice coefficient of 0.71. For enlarged LNs with a SAD ≥ 10 mm, LR was 96.2%. In the test dataset, the false-positive rate was 2.4 LNs/CECT. CONCLUSIONS: Our trained AI model demonstrated a good overall performance in the consistent automatic localization and 3D segmentation of physiological and metastatic cervical LNs with a SAD ≥ 5 mm on CECTs. This could aid clinical localization and automatic 3D segmentation, which can benefit clinical care and radiomics research. RELEVANCE STATEMENT: Our AI model is a time-saving tool for 3D segmentation of cervical lymph nodes on contrast-enhanced CT scans and serves as a solid base for N staging in clinical practice and further radiomics research. KEY POINTS: • Determination of N status in TNM staging is essential for therapy planning in oncology. • Segmenting cervical lymph nodes manually is highly time-consuming in clinical practice. • Our model provides a robust, automated 3D segmentation of cervical lymph nodes. • It achieves a high accuracy for localization especially of enlarged lymph nodes. • These segmentations should assist clinical care and radiomics research.

Transfer-Learning Deep Radiomics and Hand-Crafted Radiomics for Classifying Lymph Nodes from Contrast-Enhanced Computed Tomography in Lung Cancer.

OBJECTIVES: Positron emission tomography (PET) is currently considered the non-invasive reference standard for lymph node (N-)staging in lung cancer. However, not all patients can undergo this diagnostic procedure due to high costs, limited availability, and additional radiation exposure. The purpose of this study was to predict the PET result from traditional contrast-enhanced computed tomography (CT) and to test different feature extraction strategies. METHODS: In this study, 100 lung cancer patients underwent a contrast-enhanced 18F-fluorodeoxyglucose (FDG) PET/CT scan between August 2012 and December 2019. We trained machine learning models to predict FDG uptake in the subsequent PET scan. Model inputs were composed of (i) traditional "hand-crafted" radiomics features from the segmented lymph nodes, (ii) deep features derived from a pretrained EfficientNet-CNN, and (iii) a hybrid approach combining (i) and (ii). RESULTS: In total, 2734 lymph nodes [555 (20.3%) PET-positive] from 100 patients [49% female; mean age 65, SD: 14] with lung cancer (60% adenocarcinoma, 21% plate epithelial carcinoma, 8% small-cell lung cancer) were included in this study. The area under the receiver operating characteristic curve (AUC) ranged from 0.79 to 0.87, and the scaled Brier score (SBS) ranged from 16 to 36%. The random forest model (iii) yielded the best results [AUC 0.871 (0.865-0.878), SBS 35.8 (34.2-37.2)] and had significantly higher model performance than both approaches alone (AUC: p < 0.001, z = 8.8 and z = 22.4; SBS: p < 0.001, z = 11.4 and z = 26.6, against (i) and (ii), respectively). CONCLUSION: Both traditional radiomics features and transfer-learning deep radiomics features provide relevant and complementary information for non-invasive N-staging in lung cancer.

Development of practice recommendations based on the Canadian Syncope Risk Score and identification of barriers and facilitators for implementation.

BACKGROUND: Wide variations in emergency department (ED) syncope management exist. The Canadian Syncope Risk Score (CSRS) was developed to predict the probability of 30-day serious outcomes after ED disposition. Study objectives were to evaluate the acceptability of proposed CSRS practice recommendations among providers and patients, and identify barriers and facilitators for CSRS use to guide disposition decisions. METHODS: We conducted semi-structured interviews with 41 physicians involved in ED syncope and 35 ED patients with syncope. We used purposive sampling to ensure a variety of physician specialties and CSRS patient risk levels. Thematic analysis was completed by two independent coders with consensus meetings to resolve conflicts. Analysis proceeded in parallel with interviews until data saturation. RESULTS: The majority (97.6%; 40/41) of physicians agreed with discharge of low risk (CSRS ≤ 0) but opined that 'no follow up' changed to 'follow-up as needed'. Physicians indicated current practices do not align with the medium-risk recommendation to discharge patients with 15-day monitoring (CSRS = 1-3; due to lack of access to monitors and timely follow-up) and the high-risk recommendation (CSRS ≥ 4) to potentially discharge patients with 15-day monitoring. Physicians recommended brief hospitalization of high-risk patients due to patient safety concerns. Facilitators included the CSRS-based patient education and scores supporting their clinical gestalt. Patients reported receiving varying levels of information regarding syncope and post-ED care, were satisfied with care received and preferred less resource intensive options. CONCLUSION: Our recommendations based on the study results were: discharge of low-risk patients with physician follow-up as needed; discharge of medium-risk patients with 15-day cardiac monitoring and brief hospitalization of high-risk patients with 15-day cardiac monitoring if discharged. Patients preferred less resource intensive options, in line with CSRS recommended care. Implementation should leverage identified facilitators (e.g., patient education) and address the barriers (e.g., monitor access) to improve ED syncope care.

RéSUMé: CONTEXTE: La prise en charge des syncopes par les services d'urgence varie considérablement. Le Canadian Syncope Risk Score (CSRS) a été mis au point pour prédire la probabilité d'une issue grave à 30 jours après la prise en charge par le service des urgences. Les objectifs de l'étude étaient d'évaluer l'acceptabilité des recommandations pratiques proposées par le CSRS parmi les prestataires et les patients, et d'identifier les barrières et les facilitateurs de l'utilisation du CSRS pour guider les décisions de disposition. MéTHODES: Nous avons mené des entretiens semi-structurés avec 41 médecins impliqués dans la syncope aux urgences et 35 patients souffrant de syncope aux urgences. Nous avons utilisé un échantillonnage raisonné pour assurer une variété de spécialités médicales et de niveaux de risque pour les patients du CSRS. L'analyse thématique a été réalisée par deux codeurs indépendants, avec des réunions de consensus pour résoudre les conflits. L'analyse s'est déroulée parallèlement aux entretiens jusqu'à saturation des données. RéSULTATS: La majorité (97,6 % ; 40/41) des médecins étaient d'accord avec la sortie des patients à faible risque (CSRS ≤ 0), mais ont estimé que " pas de suivi " devait être remplacée par " suivi en fonction des besoins ". Les médecins ont indiqué que leurs pratiques actuelles ne sont pas conformes à la recommandation à risque moyen de faire sortir les patients avec une surveillance de 15 jours (CSRS = 1-3 ; en raison du manque d'accès aux moniteurs et au suivi en temps opportun) et à la recommandation à risque élevé (CSRS ≥ 4) de potentiellement faire sortir les patients avec une surveillance de 15 jours. Les médecins ont recommandé une brève hospitalisation des patients à haut risque pour des raisons de sécurité. Les facilitateurs comprenaient l'éducation des patients basée sur le CSRS et les scores soutenant leur gestalt clinique. Les patients ont déclaré avoir reçu différents niveaux d'information concernant la syncope et les soins post-urgence, étaient satisfaits des soins reçus et préféraient des options moins gourmandes en ressources. CONCLUSIONS: Nos recommandations basées sur les résultats de l'étude sont les suivantes : sortie des patients à faible risque avec suivi par un médecin si nécessaire ; la sortie des patients à risque moyen avec une surveillance cardiaque de 15 jours et une brève hospitalisation des patients à risque élevé avec une surveillance cardiaque de 15 jours en cas de sortie. Les patients ont préféré des options moins gourmandes en ressources, conformément aux soins recommandés par le CSRS. La mise en œuvre devrait s'appuyer sur les facilitateurs identifiés (par exemple, l'éducation des patients) et s'attaquer aux obstacles (par exemple, le contrôle de l'accès) pour améliorer les soins aux urgences en cas de syncope.

Radiomics for the Prediction of Overall Survival in Patients with Bladder Cancer Prior to Radical Cystectomy.

(1) Background: To evaluate radiomics features as well as a combined model with clinical parameters for predicting overall survival in patients with bladder cancer (BCa). (2) Methods: This retrospective study included 301 BCa patients who received radical cystectomy (RC) and pelvic lymphadenectomy. Radiomics features were extracted from the regions of the primary tumor and pelvic lymph nodes as well as the peritumoral regions in preoperative CT scans. Cross-validation was performed in the training cohort, and a Cox regression model with an elastic net penalty was trained using radiomics features and clinical parameters. The models were evaluated with the time-dependent area under the ROC curve (AUC), Brier score and calibration curves. (3) Results: The median follow-up time was 56 months (95% CI: 48−74 months). In the follow-up period from 1 to 7 years after RC, radiomics models achieved comparable predictive performance to validated clinical parameters with an integrated AUC of 0.771 (95% CI: 0.657−0.869) compared to an integrated AUC of 0.761 (95% CI: 0.617−0.874) for the prediction of overall survival (p = 0.98). A combined clinical and radiomics model stratified patients into high-risk and low-risk groups with significantly different overall survival (p < 0.001). (4) Conclusions: Radiomics features based on preoperative CT scans have prognostic value in predicting overall survival before RC. Therefore, radiomics may guide early clinical decision-making.

Virtual Monoenergetic Images of Dual-Energy CT-Impact on Repeatability, Reproducibility, and Classification in Radiomics.

The purpose of this study was to (i) evaluate the test-retest repeatability and reproducibility of radiomic features in virtual monoenergetic images (VMI) from dual-energy CT (DECT) depending on VMI energy (40, 50, 75, 120, 190 keV), radiation dose (5 and 15 mGy), and DECT approach (dual-source and split-filter DECT) in a phantom (ex vivo), and (ii) to assess the impact of VMI energy and feature repeatability on machine-learning-based classification in vivo in 72 patients with 72 hypodense liver lesions. Feature repeatability and reproducibility were determined by concordance-correlation-coefficient (CCC) and dynamic range (DR) ≥0.9. Test-retest repeatability was high within the same VMI energies and scan conditions (percentage of repeatable features ranging from 74% for SFDE mode at 40 keV and 15 mGy to 86% for DSDE at 190 keV and 15 mGy), while reproducibility varied substantially across different VMI energies and DECTs (percentage of reproducible features ranging from 32.8% for SFDE at 5 mGy comparing 40 with 190 keV to 99.2% for DSDE at 15 mGy comparing 40 with 50 keV). No major differences were observed between the two radiation doses (<10%) in all pair-wise comparisons. In vivo, machine learning classification using penalized regression and random forests resulted in the best discrimination of hemangiomas and metastases at low-energy VMI (40 keV), and for cysts at high-energy VMI (120 keV). Feature selection based on feature repeatability did not improve classification performance. Our results demonstrate the high repeatability of radiomics features when keeping scan and reconstruction conditions constant. Reproducibility diminished when using different VMI energies or DECT approaches. The choice of optimal VMI energy improved lesion classification in vivo and should hence be adapted to the specific task.

Easy ultrastructural insight into the internal morphology of biological specimens by Atomic Force Microscopy.

As a topographical technique, Atomic Force Microscopy (AFM) needs to establish direct interactions between a given sample and the measurement probe in order to create imaging information. The elucidation of internal features of organisms, tissues and cells by AFM has therefore been a challenging process in the past. To overcome this hindrance, simple and fast embedding, sectioning and dehydration techniques are presented, allowing the easy access to the internal morphology of virtually any organism, tissue or cell by AFM. The study at hand shows the applicability of the proposed protocol to exemplary biological samples, the resolution currently allowed by the approach as well as advantages and shortcomings compared to classical ultrastructural microscopic techniques like electron microscopy. The presented cheap, facile, fast and non-toxic experimental protocol might introduce AFM as a universal tool for the elucidation of internal ultrastructural detail of virtually any given organism, tissue or cell.