Imaging-based Biomarkers Research Articles

Biomarkers of polyunsaturated fatty acids and cardiovascular risk factors and events in healthy Asian populations: a systematic review.

The associations between circulating polyunsaturated fatty acids (PUFAs) and cardiovascular risk factors and events in healthy Asian populations have been less examined robustly compared to Western populations. This systematic review aimed to summarise current evidence on the associations between n-3 and n-6 PUFAs biomarkers and cardiovascular risk factors and events in healthy Asian populations. PubMed, Embase, Web of Science and Cochrane Library were searched for observational studies from January 2010 until August 2024. Twenty-three studies were eligible, which covered six Asian countries and included events(n=7), traditional risk factors such as blood pressure and lipids(n=4), physical signs such as arterial stiffness(n=4), non-traditional lipid markers(n=1), markers of inflammation(n=4), markers of thrombosis(n=2), and non-invasive imaging-based markers such as carotid intima media thickness(n=5). Biological sample type included plasma(n=6), serum(n=14) and erythrocyte(n=3). Higher circulating total n-3 PUFAs appeared to be associated with lower hypertension risk and specifically eicosapentaenoic acid and docosahexaenoic acid to be associated with lower myocardial infarction risk, reduction in triglycerides and inflammation. Higher circulating linoleic acid was associated with improved lipid profiles and lower inflammation. Limited evidence led to inconclusive associations between circulating n-6 PUFAs biomarkers and cardiovascular disease events and blood pressure. No consistent associations with arterial stiffness, obesity, thrombosis and imaging-based biomarkers were observed for circulating PUFAs biomarkers in Asian populations. Limited studies exist for each outcome, hence results should be interpreted with caution. More high quality and prospective studies in Asian populations are warranted. Several recommendations such as sample size justification and reporting of non-respondents rate are proposed for future studies.

Artificial intelligence applied to electrocardiographic images for the risk stratification of cancer therapeutics-related cardiac dysfunction

Abstract Background Traditional risk stratification strategies for cancer therapeutics-related cardiac dysfunction (CTRCD) rely on serial monitoring by specialized imaging, limiting their scalability and affordability. Purpose To examine an artificial intelligence (AI)-enhanced electrocardiographic (AI-ECG) surrogate for imaging-based risk biomarkers, and its association with CTRCD. Methods Between 2013-2023, across 5 hospitals of a large U.S.-based health system, we identified patients with breast cancer or non-Hodgkin lymphoma (NHL) who received anthracyclines (AC) and/or trastuzumab (TZM), as well as a (negative) control cohort of patients receiving immune checkpoint inhibitors (ICI). We deployed a validated AI model of left ventricular systolic dysfunction (LVSD) to ECG images (≥0.1=positive screen) (ref 1) and explored the association between the model’s predictions and: i) global longitudinal strain (GLS) measurements within 15 days; ii) future CTRCD (defined as a new cardiomyopathy, heart failure, or left ventricular ejection fraction (LVEF) decrease to <50%), as well as a more specific secondary outcome of LVEF decrease <40%. In a negative control analysis of patients receiving ICI, we correlated baseline AI-ECG LVSD predictions with downstream myocarditis. Results Higher AI-ECG LVSD predictions were associated with progressively worse GLS (-18% [IQR: -20 to -17%] for AI-ECG predictions <0.1, to -12% [-15 to -9%] for ≥0.5, p<0.001, n=7,271) (A). In 1,308 patients receiving AC/TZM (age 59 [IQR 49-67] years, 999 [76.4%] women, 80 [IQR 42-115] follow-up months) a positive baseline AI-ECG LVSD screen was associated with higher incidence of CTRCD (n=404 events, adjusted HR 2.22 [95%CI: 1.63-3.02]) and/or LVEF <40% (n=72 events, adjusted HR 4.76 [95%CI: 2.62-8.66]) (B). In a negative control analysis of 2,056 patients receiving ICI (age 65 [IQR 57-73] years, 913 (44.4%) women, follow-up 63 [IQR 28-99] months) AI-ECG predictions were not associated with downstream myocarditis (adjusted HR 1.36 [0.47-3.93]). Conclusions AI applied directly to baseline ECG images may stratify the risk of CTRCD associated with anthracycline or trastuzumab exposure.

ReMiND: Recovery of missing neuroimaging using diffusion models with application to Alzheimer’s disease

Abstract Missing data is a significant challenge in medical research. In longitudinal studies of Alzheimer’s disease (AD) where structural magnetic resonance imaging (MRI) is collected from individuals at multiple time points, participants may miss a study visit or drop out. Additionally, technical issues such as participant motion in the scanner may result in unusable imaging data at designated visits. Such missing data may hinder the development of high-quality imaging-based biomarkers. To address the problem of missing MRI data in studies of AD, we introduced a novel 3D diffusion model specifically designed for imputing missing structural MRI (Recovery of Missing Neuroimaging using Diffusion models (ReMiND)). The model generates a whole-brain image conditional on a single structural MRI observed at a past visit or conditional on one past and one future observed structural MRI relative to the missing observation. The performance of models was compared with two alternative imputation approaches: forward filling and image generation using variational autoencoders. Experimental results show that our method can generate 3D structural MRI with high similarity to ground-truth images at designated visits. Furthermore, images generated using ReMiND show relatively lower differences in volume estimation between the imputed and observed images compared to images generated by forward filling or autoencoders. Additionally, ReMiND provides more accurate estimated rates of atrophy over time in important anatomical brain regions than the two comparator methods. Our 3D diffusion model can impute missing structural MRI data at a single designated visit and outperforms alternative methods for imputing whole-brain images that are missing from longitudinal trajectories.

Perceptual super-resolution in multiple sclerosis MRI.

Magnetic resonance imaging (MRI) is crucial for diagnosing and monitoring of multiple sclerosis (MS) as it is used to assess lesions in the brain and spinal cord. However, in real-world clinical settings, MRI scans are often acquired with thick slices, limiting their utility for automated quantitative analyses. This work presents a single-image super-resolution (SR) reconstruction framework that leverages SR convolutional neural networks (CNN) to enhance the through-plane resolution of structural MRI in people with MS (PwMS). Our strategy involves the supervised fine-tuning of CNN architectures, guided by a content loss function that promotes perceptual quality, as well as reconstruction accuracy, to recover high-level image features. Extensive evaluation with MRI data of PwMS shows that our SR strategy leads to more accurate MRI reconstructions than competing methods. Furthermore, it improves lesion segmentation on low-resolution MRI, approaching the performance achievable with high-resolution images. Results demonstrate the potential of our SR framework to facilitate the use of low-resolution retrospective MRI from real-world clinical settings to investigate quantitative image-based biomarkers of MS.

Mechanical Mapping of the Common Carotid Artery in Healthy Individuals Aged 2 to 40 Years.

(1) Background: In 2022, the World Stroke Organization said there were more than 12.2 million new cases of stroke each year, between all ages and sexes. Six and a half million people die each year from stroke. Ischemic stroke accounts for 7.6 million (62%) cases, with 3.3 million (51%) deaths. Stroke is mainly linked to the atherosclerosis of a large artery. (2) Objective: Since the carotid artery directly supplies the brain, we used age-dependent mechanical mapping on the healthy common carotid artery (CCA) with the aim of being able to predict and thus potentially prevent ischemic stroke. (3) Methods: We assessed the CCA stiffness of 95 healthy control (CTL) females (2.23-39.46 years) and 107 healthy CTL males (2.85-40 years). Cine-loops of B-mode CCA data were digitally recorded with conventional medical ultrasound devices. Arterial wall elastic moduli were estimated offline using a proprietary non-invasive imaging-based biomarker algorithm (ImBioMark). Statistical analyzes were carried out with Excel software. (4) Results: Females showed a linear regression profile of CCA elastic moduli ranging from 41 ± 2 kPa to 54 ± 17 kPa (R2 = 0.88), while males showed one ranging from 38 ± 5 kPa to 63 ± 22 kPa (R2 = 0.83). For qualitative and quantitative illustrations, the elastic modulus data of CTLs were compared with those of subjects with Kawasaki disease and subjects born prematurely, respectively. (5) Conclusions: This study introduced some fundamental features of the mechanical evolution of the CCA as a function of age (2-40 years). Since atherosclerotic arteriopathy starts early in life, this gives the ability to predict risks of stroke and other cardiovascular diseases with the possibility of applying a more comprehensive range of potential preventive measures early in life. This is consistent with preventive medicine objectives which aim to be more predictive to implement pre-emptive measures as opposed to diagnostic and curative approaches.

Investigating brain–gut microbiota dynamics and inflammatory processes in an autistic-like rat model using MRI biomarkers during childhood and adolescence

Autism spectrum disorder (ASD) is characterized by social interaction deficits and repetitive behaviors. Recent research has linked that gut dysbiosis may contribute to ASD-like behaviors. However, the exact developmental time point at which gut microbiota alterations affect brain function and behavior in patients with ASD remains unclear. We hypothesized that ASD-related brain microstructural changes and gut dysbiosis induce metabolic dysregulation and proinflammatory responses, which collectively contribute to the social behavioral deficits observed in early childhood. We used an autistic-like rat model that was generated via prenatal valproic acid exposure. We analyzed brain microstructural changes using diffusion tensor imaging (DTI) and examined microbiota, blood, and fecal samples for inflammation biomarkers. The ASD model rats exhibited significant brain microstructural changes in the anterior cingulate cortex, hippocampus, striatum, and thalamus; reduced microbiota diversity (Prevotellaceae and Peptostreptococcaceae); and altered metabolic signatures. The shift in microbiota diversity and density observed at postnatal day (PND) 35, which is a critical developmental period, underscored the importance of early ASD interventions. We identified a unique metabolic signature in the ASD model, with elevated formate and reduced acetate and butyrate levels, indicating a dysregulation in short-chain fatty acid (SCFA) metabolism. Furthermore, increased astrocytic and microglial activation and elevated proinflammatory cytokines—interleukin-1 beta (IL-1β), interleukin-6 (IL-6), interferon-gamma (IFN-γ), and tumor necrosis factor-alpha (TNF-α)—were observed, indicating immune dysregulation. This study provided insights into the complex interplay between the brain and the gut, and indicated DTI metrics as potential imaging-based biomarkers in ASD, thus emphasizing the need for early childhood interventions.

Biomarkers of Hemodynamic Congestion in Heart Failure.

The purpose of this review is to describe the evidence behind various blood and imaging-based biomarkers that can improve the identification of congestion when not clearly evident on routine examination. The natriuretic peptides (NPs) BNP and NT-proBNP have been shown to closely correlate with intra-cardiac filling pressures, both at baseline and when trended following improvement in congestion. Additionally, NPs rise well before clinical congestion is apparent so can be used as a tool to help identify subclinical HF decompensation. Additional serum-based biomarkers including MR-proANP and CA-125 can be helpful in assisting with diagnostic certainty when BNP or NT-proBNP are in the "grey zone" or when factors are present which may confound NP levels. Additionally, the emerging use of ultrasound techniques may enhance our ability to fine-tune the assessment and treatment of congestion. Biomarkers, including the blood-based natriuretic peptides and markers on bedside point of care ultrasound, can be used as non-invasive indices of hemodynamic congestion. These biomarkers are particularly valuable to incorporate when the degree of a patient's congestion is not apparent on clinical exam, and they can provide important prognostic information and help guide clinical management.

Chronic Traumatic Encephalopathy: A Comprehensive Narrative Review of Its Biomarkers.

Chronic traumatic encephalopathy (CTE) is a progressive and fatal neurological disorder linked to repeated traumatic brain injuries (TBIs), including concussions and blows to the head. This condition is characterized by the accumulation of abnormally structured hyperphosphorylated tau proteins (p-tau), forming neurofibrillary tangles, astrocytic tangles, and neurites in the brain. CTE is often diagnosed post-mortem, making it challenging to diagnose and predict its progression in living individuals. Despite recent advancements, no definitive pathological, radiological, or neurobiological marker consistently shows promise in diagnosing and predicting the disease. This review aims to summarize the available techniques and advancements in imaging-based, genetic, neuropsychological, and fluid biomarkers for CTE, evaluating their specificity and sensitivity. It will also highlight the limitations of each marker in diagnosing CTE and provide future research directions to enhance the accuracy of CTE diagnosis in living individuals.

Radiogenomic profiling of global DNA methylation associated with molecular phenotypes and immune features in glioma

BackgroundThe radiogenomic analysis has provided valuable imaging biomarkers with biological insights for gliomas. The radiogenomic markers for molecular profile such as DNA methylation remain to be uncovered to assist the molecular diagnosis and tumor treatment.MethodsWe apply the machine learning approaches to identify the magnetic resonance imaging (MRI) features that are associated with molecular profiles in 146 patients with gliomas, and the fitting models for each molecular feature (MoRad) are developed and validated. To provide radiological annotations for the molecular profiles, we devise two novel approaches called radiomic oncology (RO) and radiomic set enrichment analysis (RSEA).ResultsThe generated MoRad models perform well for profiling each molecular feature with radiomic features, including mutational, methylation, transcriptional, and protein profiles. Among them, the MoRad models have a remarkable performance in quantitatively mapping global DNA methylation. With RO and RSEA approaches, we find that global DNA methylation could be reflected by the heterogeneity in volumetric and textural features of enhanced regions in T2-weighted MRI. Finally, we demonstrate the associations of global DNA methylation with clinicopathological, molecular, and immunological features, including histological grade, mutations of IDH and ATRX, MGMT methylation, multiple methylation-high subtypes, tumor-infiltrating lymphocytes, and long-term survival outcomes.ConclusionsGlobal DNA methylation is highly associated with radiological profiles in glioma. Radiogenomic global methylation is an imaging-based quantitative molecular biomarker that is associated with specific consensus molecular subtypes and immune features.

Improving performance in colorectal cancer histology decomposition using deep and ensemble machine learning

In routine colorectal cancer management, histologic samples stained with hematoxylin and eosin are commonly used. Nonetheless, their potential for defining objective biomarkers for patient stratification and treatment selection is still being explored. The current gold standard relies on expensive and time-consuming genetic tests. However, recent research highlights the potential of convolutional neural networks (CNNs) to facilitate the extraction of clinically relevant biomarkers from these readily available images. These CNN-based biomarkers can predict patient outcomes comparably to golden standards, with the added advantages of speed, automation, and minimal cost. The predictive potential of CNN-based biomarkers fundamentally relies on the ability of CNNs to accurately classify diverse tissue types from whole slide microscope images. Consequently, enhancing the accuracy of tissue class decomposition is critical to amplifying the prognostic potential of imaging-based biomarkers. This study introduces a hybrid deep transfer learning and ensemble machine learning model that improves upon previous approaches, including a transformer and neural architecture search baseline for this task. We employed a pairing of the EfficientNetV2 architecture with a random forest classification head. Our model achieved 96.74% accuracy (95% CI: 96.3%-97.1%) on the external test set and 99.89% on the internal test set. Recognizing the potential of these models in the task, we have made them publicly available.

Identification and Quantification of Precursory Changes of Rheumatoid Vasculitis in the Dorsalis Pedis Artery

ObjectiveRheumatoid arthritis (RA) is a systemic connective tissue autoimmune disease that can infiltrate arterial walls. The delay in diagnosis and treatment of rheumatoid vasculitis (RV) in patients with RA may lead to irreversible damage to the arterial walls of small-to-medium vessels, which has serious and devastating consequences, most notably lung and cardiac damage. In this work an ultrasound image-based biomarker was developed to detect precursory changes in RV. MethodsThe ground truth was initiated from a medical diagnosis of RA, with arterial wall thickening of the proximal dorsalis pedis artery (DPA) indicating precursory changes of RV identified with ultrasound scanning. Ultrasound images of the DPA from 49 healthy subjects in the control group and 46 patients in the RA group were obtained. In total, 187 texture features were extracted from the images, followed by principal component analysis and linear discriminant analysis. ResultsThe proposed biomarker detected a significant difference between the two groups (p = 5.74 × 10-18) with an area under the receiver operating characteristic curve of 0.85. Ten major textural features contributing most heavily to the biomarker were identified, with these textures being consistent with clinical observations of RV identified in previous studies. Interscan reproducibility was assessed by computing the biomarker twice based on repeated scans of each ankle. High interscan reproducibility was demonstrated by a strong and significant Pearson's coefficient (r = 0.85, p < 0.01) between the two repeated measurements of the proposed biomarker. ConclusionThe proposed biomarker can discriminate image textural differences seen in images acquired from RA patients, demonstrating precursory changes in RV compared with healthy controls. The major discriminative features identified in this study may facilitate the early identification and treatment of RV.

ImAge quantitates aging and rejuvenation.

For efficient, cost-effective and personalized healthcare, biomarkers that capture aspects of functional, biological aging, thus predicting disease risk and lifespan more accurately and reliably than chronological age, are essential. We developed an imaging-based chromatin and epigenetic age (ImAge) that captures intrinsic age-related trajectories of the spatial organization of chromatin and epigenetic marks in single nuclei, in mice. We show that such trajectories readily emerge as principal changes in each individual dataset without regression on chronological age, and that ImAge can be computed using several epigenetic marks and DNA labeling. We find that interventions known to affect biological aging induce corresponding effects on ImAge, including increased ImAge upon chemotherapy treatment and decreased ImAge upon caloric restriction and partial reprogramming by transient OSKM expression in liver and skeletal muscle. Further, ImAge readouts from chronologically identical mice inversely correlated with their locomotor activity, suggesting that ImAge may capture elements of biological and functional age. In sum, we developed ImAge, an imaging-based biomarker of aging with single-cell resolution rooted in the analysis of spatial organization of epigenetic marks.

Aneurysm growth evaluation and detection: a computer-assisted follow-up MRA analysis

Growing intracranial aneurysms pose a high risk of rupture, making the detection and quantification of the growth crucial for timely treatment strategy adoption. In this paper we propose a computer-assisted approach based on the extraction of IA shapes from associated baseline and follow-up angiographic scans and non-rigid morphing of the two shapes. From the obtained shape deformations we computed four novel features, including differential volume (dV), surface area (dSA), aneurysm-size normalized median deformation path length (dMPL), and integral of cumulative deformation distances (dICDD). An experienced neuroradiologist manually extracted the IA shape models from the baseline and follow-up MRAs and, by utilizing size change and visual assessments, classified each aneurysm into stable with morphology changes, stable or growing. We investigated the classification performance and found that three of the novel and one cross-sectional feature exhibited significantly different mean values (p-value <0.05\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$< 0.05$$\\end{document}; Tukey’s HSD test) between the stable and growing IA groups, while the mean dICDD was significantly different between all the three groups. The cross-sectional features has sensitivity to growing IAs in range 0.05–0.86, while novel features had generally higher sensitivity in range 0.81–0.90, making them promising candidates as surrogate follow-up imaging-based biomarkers for IA growth detection. These findings may offer valuable information for clinical management of patients with IAs based on follow-up imaging.

Spleen Volume Reduction Is a Reliable and Independent Biomarker for Long-Term Risk of Leukopenia Development in Peptide Receptor Radionuclide Therapy.

177Lu-DOTATATE therapy is an effective treatment for advanced neuroendocrine tumors, despite its dose-limiting hematotoxicity. Herein, the significance of off-target splenic irradiation is unknown. Our study aims to identify predictive markers of peptide receptor radionuclide therapy-induced leukopenia. Methods: We retrospectively analyzed blood counts and imaging data of 88 patients with histologically confirmed, unresectable metastatic neuroendocrine tumors who received 177Lu-DOTATATE treatment at our institution from February 2009 to July 2021. Inclusion criterium was a tumor uptake equivalent to or greater than that in the liver on baseline receptor imaging. We excluded patients with less than 24 mo of follow-up and those patients who received fewer than 4 treatment cycles, additional therapies, or blood transfusions during follow-up. Results: Our study revealed absolute and relative white blood cell counts and relative spleen volume reduction as independent predictors of radiation-induced leukopenia at 24 mo. However, a 30% decline in spleen volume 12 mo after treatment most accurately predicted patients proceeding to leukopenia at 24 mo (receiver operating characteristic area under the curve of 0.91, sensitivity of 0.93, and specificity of 0.90), outperforming all other parameters by far. Conclusion: Automated splenic volume assessments demonstrated superior predictive capabilities for the development of leukopenia in patients undergoing 177Lu-DOTATATE treatment compared with conventional laboratory parameters. The reduction in spleen size proves to be a valuable, routinely available, and quantitative imaging-based biomarker for predicting radiation-induced leukopenia. This suggests potential clinical applications for risk assessment and management.

Multimodal Data Integration for Early Alzheimer’s Detection Using Random Forest and Support Vector Machines

Alzheimer's is a very challenging brain disease to recognize, diagnose, and treat correctly when it appears in its earliest forms. The primary contribution of this research study is about machine learning models, techniques, and approaches. In contrast, Random Forest and Support Vector Machine (SVM) are particularly suitable for identifying and staging Alzheimer's disease stages using multimodal data sources. In this paper, the aim was to develop well-performing predictive models to help diagnose Alzheimer's disease at an early stage by combining neuroimaging data (MRI/PET images), imaging-based biomarkers, both structural and functional measures from MRI(P) /PET image analysis along with subject-specific demographics like age using clinical features in a probabilistic fashion obtained from the Alzheimer's Disease Neuro-Imaging Initiative (ADNI) database. The methodology focuses on data pre-processing, feature selection, and model building using supervised learning algorithms. The accuracy of the Random Forest model is 78%, having a high performance in classifying some classes while showing different marks of performances across other courses. SVM reached an accuracy of 61%, or the model's performance is good in some classes and not reliable to identify samples from the others. The findings of this study underscore the capabilities and limits of these machine learning models in identifying Alzheimer’s disease and highlight the importance of feature engineering, data pre-processing, and model tuning to increase performance and correct class unevenness and misclassification.

Deep learning-based and BI-RADS guided radiomics model for automatic tumor-infiltrating lymphocytes evaluation in breast cancer.

To investigate an interpretable radiomics model consistent with clinical decision-making process and realize automatic prediction of tumor-infiltrating lymphocytes (TILs) levels in breast cancer (BC) from ultrasound (US) images. A total of 378 patients with invasive BC confirmed by pathological results were retrospectively enrolled in this study. Radiomics features were extracted guided by the BI-RADS lexicon from the regions of interest(ROIs) segmented with deep learning models. After features selected using the least absolute shrinkage and selection operator(LASSO) regression, four machine learning classifiers were used to establish the radiomics signature(Rad-score). Then, the integrated model was developed on the basis of the best Rad-score incorporating the independent clinical factors for TILs levels prediction. Tumors were segmented using the deep learning models with accuracy of 97.2%, sensitivity of 93.4%, specificity of 98.1%, and the posterior areas were also obtained. Eighteen morphology and texture related features were extracted from the ROIs and fourteen features were selected to construct the Rad-score models. Combined with independent clinical characteristics, the integrated model achieved an area under the curve (AUC) of 0.889(95% CI,0.739,0.990) in the validation cohort and outperformed the traditional radiomics model with AUC of 0.756(0.649-0862) depended on hundreds of feature items. This study established a promising model for TILs levels prediction with numerable interpretable features and showed great potential to help decision-making and clinical applications. Imaging-based biomarkers has provides non-invasive ways for TILs levels evaluation in BC. Our model combining the BI-RADS guided radiomics features and clinical data outperformed the traditional radiomics approaches.

La imagen de estasis predice el riesgo de eventos cardioembólicos tras el infarto agudo de miocardio: el estudio ISBITAMI

Introduction and objectivesIn the setting of ST-segment elevation myocardial infarction (STEMI), imaging-based biomarkers could be useful for guiding oral anticoagulation to prevent cardioembolism. Our objective was to test the efficacy of intraventricular blood stasis imaging for predicting a composite primary endpoint of cardioembolic risk during the first 6 months after STEMI. MethodsWe designed a prospective clinical study, Imaging Silent Brain Infarct in Acute Myocardial Infarction (ISBITAMI), including patients with a first STEMI, an ejection fraction ≤ 45% and without atrial fibrillation to assess the performance of stasis metrics to predict cardioembolism. Patients underwent ultrasound-based stasis imaging at enrollment followed by heart and brain magnetic resonance at 1-week and 6-month visits. From the stasis maps, we calculated the average residence time, RT, of blood inside the left ventricle and assessed its performance to predict the primary endpoint. The longitudinal strain of the 4 apical segments was quantified by speckle tracking. ResultsA total of 66 patients were assigned to the primary endpoint. Of them, 17 patients had 1 or more events: 3 strokes, 5 silent brain infarctions, and 13 mural thromboses. No systemic embolisms were observed. RT (OR, 3.73; 95%CI, 1.75-7.9; P<.001) and apical strain (OR, 1.47; 95%CI, 1.13-1.92; P=.004) showed complementary prognostic value. The bivariate model showed a c-index=0.86 (95%CI, 0.73-0.95), a negative predictive value of 1.00 (95%CI, 0.94-1.00), and positive predictive value of 0.45 (95%CI, 0.37-0.77). The results were confirmed in a multiple imputation sensitivity analysis. Conventional ultrasound-based metrics were of limited predictive value. ConclusionsIn patients with STEMI and left ventricular systolic dysfunction in sinus rhythm, the risk of cardioembolism may be assessed by echocardiography by combining stasis and strain imaging. Registered at ClinicalTrials.gov (NCT02917213).

Analyzing heterogeneity in Alzheimer Disease using multimodal normative modeling on imaging-based ATN biomarkers.

Previous studies have applied normative modeling on a single neuroimaging modality to investigate Alzheimer Disease (AD) heterogeneity. We employed a deep learning-based multimodal normative framework to analyze individual-level variation across ATN (amyloid-tau-neurodegeneration) imaging biomarkers. We selected cross-sectional discovery (n = 665) and replication cohorts (n = 430) with available T1-weighted MRI, amyloid and tau PET. Normative modeling estimated individual-level abnormal deviations in amyloid-positive individuals compared to amyloid-negative controls. Regional abnormality patterns were mapped at different clinical group levels to assess intra-group heterogeneity. An individual-level disease severity index (DSI) was calculated using both the spatial extent and magnitude of abnormal deviations across ATN. Greater intra-group heterogeneity in ATN abnormality patterns was observed in more severe clinical stages of AD. Higher DSI was associated with worse cognitive function and increased risk of disease progression. Subject-specific abnormality maps across ATN reveal the heterogeneous impact of AD on the brain.

Protocol for the development of a core outcome set for clinical trials in primary sclerosing cholangitis

BackgroundPrimary sclerosing cholangitis (PSC) is a progressive immune-mediated liver disease, for which no medical therapy has been shown to slow disease progression. However, the horizon for new therapies is encouraging,...

Impact of quantitative CT texture analysis on the outcome of CT-guided bone biopsy

Texture analysis can provide new imaging-based biomarkers. Texture analysis derived from computed tomography (CT) might be able to better characterize patients undergoing CT-guided percutaneous bone biopsy. The present study evaluated this and correlated texture features with bioptic outcome in patients undergoing CT-guided bone biopsy. Overall, 123 patients (89 female patients, 72.4 %) were included into the present study. All patients underwent CT-guided percutaneous bone biopsy with an 11 Gauge coaxial needle. Clinical parameters and quantitative imaging features were investigated. Random forest classifier was used to predict a positive biopsy result. Overall, 69 patients had osteolytic metastasis (56.1 %) and 54 had osteoblastic metastasis (43.9 %). The overall positive biopsy rate was 72 %. The developed radiomics model demonstrated a prediction accuracy of a positive biopsy result with an AUC of 0.75 [95 %CI 0.65 – 0.85]. In a subgroup of breast cancer patients, the model achieved an AUC of 0.85 [95 %CI 0.73 – 0.96]. In the subgroup of non-breast cancer patients, the signature achieved an AUC of 0.80 [95 %CI 0.60 – 0.99]. Quantitative CT imaging findings comprised of conventional and texture features can aid to predict the bioptic result of CT-guided bone biopsies. The developed radiomics signature aids in clinical decision-making, and could identify patients at risk for a negative biopsy.