MARCH family E3 ubiquitin ligases selectively target and degrade cadherin family proteins.

Cadherin family proteins play a central role in epithelial and endothelial cell-cell adhesion. The dynamic regulation of cell adhesion is achieved in part through endocytic membrane trafficking pathways that modulate cadherin cell surface levels. Here, we define the role for various MARCH family ubiquitin ligases in the regulation of cadherin degradation. We find that MARCH2 selectively downregulates VE-cadherin, resulting in loss of adherens junction proteins at cell borders and a loss of endothelial barrier function. Interestingly, N-cadherin is refractory to MARCH ligase expression, demonstrating that different classical cadherin family proteins are differentially regulated by MARCH family ligases. Using chimeric cadherins, we find that the specificity of different MARCH family ligases for different cadherins is conferred by the cadherin transmembrane domain. Further, juxta-membrane lysine residues are required for cadherin degradation by MARCH proteins. These findings expand our understanding of cadherin regulation and highlight a new role for mammalian MARCH family ubiquitin ligases in differentially regulating cadherin turnover.

SPECT-MPI for Coronary Artery Disease: A Deep Learning Approach.

Background: Worldwide, coronary artery disease (CAD) is a leading cause of mortality and morbidity and remains to be a top health priority in many countries. A non-invasive imaging modality for diagnosis of CAD such as single photon emission computed tomography-myocardial perfusion imaging (SPECT-MPI) is usually requested by cardiologists as it displays radiotracer distribution in the heart reflecting myocardial perfusion. The interpretation of SPECT-MPI is done visually by a nuclear medicine physician and is largely dependent on his clinical experience and showing significant inter-observer variability. Objective: The aim of the study is to apply a deep learning approach in the classification of SPECT-MPI for perfusion abnormalities using convolutional neural networks (CNN). Methods: A publicly available anonymized SPECT-MPI from a machine learning repository (https://www.kaggle.com/selcankaplan/spect-mpi) was used in this study involving 192 patients who underwent stress-test-rest Tc99m MPI. An exploratory approach of CNN hyperparameter selection to search for optimum neural network model was utilized with particular focus on various dropouts (0.2, 0.5, 0.7), batch sizes (8, 16, 32, 64), and number of dense nodes (32, 64, 128, 256). The base CNN model was also compared with the commonly used pre-trained CNNs in medical images such as VGG16, InceptionV3, DenseNet121 and ResNet50. All simulations experiments were performed in Kaggle using TensorFlow 2.6.0., Keras 2.6.0, and Python language 3.7.10. Results: The best performing base CNN model with parameters consisting of 0.7 dropout, batch size 8, and 32 dense nodes generated the highest normalized Matthews Correlation Coefficient at 0.909 and obtained 93.75% accuracy, 96.00% sensitivity, 96.00% precision, and 96.00% F1-score. It also obtained higher classification performance as compared to the pre-trained architectures. Conclusions: The results suggest that deep learning approaches through the use of CNN models can be deployed by nuclear medicine physicians in their clinical practice to further augment their decision skills in the interpretation of SPECT-MPI tests. These CNN models can also be used as a dependable and valid second opinion that can aid physicians as a decision-support tool as well as serve as teaching or learning materials for the less-experienced physicians particularly those still in their training career. These highlights the clinical utility of deep learning approaches through CNN models in the practice of nuclear cardiology.

Shock drives a STAT3 and JunB-mediated coordinated transcriptional and DNA methylation response in the endothelium.

Endothelial dysfunction is a crucial factor in promoting organ failure during septic shock. However, the underlying mechanisms are unknown. Here, we show that kidney injury after lipopolysaccharide (LPS) insult leads to strong endothelial transcriptional and epigenetic responses. Furthermore, SOCS3 loss leads to an aggravation of the responses, demonstrating a causal role for the STAT3-SOCS3 signaling axis in the acute endothelial response to LPS. Experiments in cultured endothelial cells demonstrate that IL-6 mediates this response. Furthermore, bioinformatics analysis of in vivo and in vitro transcriptomics and epigenetics suggests a role for STAT, AP1 and interferon regulatory family (IRF) transcription factors. Knockdown of STAT3 or the AP1 member JunB partially prevents the changes in gene expression, demonstrating a role for these transcription factors. In conclusion, endothelial cells respond with a coordinated response that depends on overactivated IL-6 signaling via STAT3, JunB and possibly other transcription factors. Our findings provide evidence for a critical role of IL-6 signaling in regulating shock-induced epigenetic changes and sustained endothelial activation, offering a new therapeutic target to limit vascular dysfunction.

MARCH family E3 ubiquitin ligases selectively target and degrade cadherin family proteins.

Cadherin family proteins play a central role in epithelial and endothelial cell-cell adhesion. The dynamic regulation of cell adhesion is achieved in part through endocytic membrane trafficking pathways that modulate cadherin cell surface levels. Here, we define the role for various MARCH family ubiquitin ligases in the regulation of cadherin degradation. We find that MARCH2 selectively downregulates VE-cadherin, resulting in loss of adherens junction proteins at cell borders and a loss of endothelial barrier function. Interestingly, N-cadherin is refractory to MARCH ligase expression, demonstrating that different classical cadherin family proteins are differentially regulated by MARCH family ligases. Using chimeric cadherins, we find that the specificity of different MARCH family ligases for different cadherins is conferred by the cadherin transmembrane domain. Further, juxta-membrane lysine residues are required for cadherin degradation by MARCH proteins. These findings expand our understanding of cadherin regulation and highlight a new role for mammalian MARCH family ubiquitin ligases in differentially regulating cadherin turnover.

Decreased but diverse activity of cortical and thalamic neurons in consciousness-impairing rodent absence seizures.

Absence seizures are brief episodes of impaired consciousness, behavioral arrest, and unresponsiveness, with yet-unknown neuronal mechanisms. Here we report that an awake female rat model recapitulates the behavioral, electroencephalographic, and cortical functional magnetic resonance imaging characteristics of human absence seizures. Neuronally, seizures feature overall decreased but rhythmic firing of neurons in cortex and thalamus. Individual cortical and thalamic neurons express one of four distinct patterns of seizure-associated activity, one of which causes a transient initial peak in overall firing at seizure onset, and another which drives sustained decreases in overall firing. 40-60 s before seizure onset there begins a decline in low frequency electroencephalographic activity, neuronal firing, and behavior, but an increase in higher frequency electroencephalography and rhythmicity of neuronal firing. Our findings demonstrate that prolonged brain state changes precede consciousness-impairing seizures, and that during seizures distinct functional groups of cortical and thalamic neurons produce an overall transient firing increase followed by a sustained firing decrease, and increased rhythmicity.

A machine-learning approach for predicting impaired consciousness in absence epilepsy.

Behavior during 3-4 Hz spike-wave discharges (SWDs) in absence epilepsy can vary from obvious behavioral arrest to no detectible deficits. Knowing if behavior is impaired is crucial for clinical care but may be difficult to determine without specialized behavioral testing, often inaccessible in practice. We aimed to develop a pure electroencephalography (EEG)-based machine-learning method to predict SWD-related behavioral impairment. Our classification goals were 100% predictive value, with no behaviorally impaired SWDs misclassified as spared; and maximal sensitivity. First, using labeled data with known behavior (130 SWDs in 34 patients), we extracted EEG time, frequency domain, and common spatial pattern features and applied support vector machines and linear discriminant analysis to classify SWDs as spared or impaired. We evaluated 32 classification models, optimized with 10-fold cross-validation. We then generalized these models to unlabeled data (220 SWDs in 41 patients), where behavior during individual SWDs was not known, but observers reported the presence of clinical seizures. For labeled data, the best classifier achieved 100% spared predictive value and 93% sensitivity. The best classifier on the unlabeled data achieved 100% spared predictive value, but with a lower sensitivity of 35%, corresponding to a conservative classification of 8 patients out of 23 as free of clinical seizures. Our findings demonstrate the feasibility of machine learning to predict impaired behavior during SWDs based on EEG features. With additional validation and optimization in a larger data sample, applications may include EEG-based prediction of driving safety, treatment adjustment, and insight into mechanisms of impaired consciousness in absence seizures.

Validation of Convolutional Neural Networks for Fast Determination of Whole-Body Metabolic Tumor Burden in Pediatric Lymphoma.

18F-FDG PET/CT quantification of whole-body tumor burden in lymphoma is not routinely performed because of the lack of fast methods. Although the semiautomatic method is fast, it is not fast enough to quantify tumor burden in daily clinical practice. Our purpose was to evaluate the performance of convolutional neural network (CNN) software in localizing neoplastic lesions in whole-body 18F-FDG PET/CT images of pediatric lymphoma patients. Methods: The retrospective image dataset, derived from the data pool of the International Atomic Energy Agency (coordinated research project E12017), included 102 baseline staging 18F-FDG PET/CT studies of pediatric lymphoma patients (mean age, 11 y). The images were quantified to determine the whole-body tumor burden (whole-body metabolic tumor volume [wbMTV] and whole-body total lesion glycolysis [wbTLG]) using semiautomatic software and CNN-based software. Both were displayed as semiautomatic wbMTV and wbTLG and as CNN wbMTV and wbTLG. The intraclass correlation coefficient (ICC) was applied to evaluate concordance between the CNN-based software and the semiautomatic software. Results: Twenty-six patients were excluded from the analysis because the software was unable to perform calculations for them. In the remaining 76 patients, CNN and semiautomatic wbMTV tumor burden metrics correlated strongly (ICC, 0.993; 95% CI, 0.989 - 0.996; P < 0.0001), as did CNN and semiautomatic wbTLG (ICC, 0.999; 95% CI, 0.998-0.999; P < 0.0001). However, the time spent calculating these metrics was significantly (<0.0001) less by CNN (mean, 19 s; range, 11-50 s) than by the semiautomatic method (mean, 21.6 min; range, 3.2-62.1 min), especially in patients with advanced disease. Conclusion: Determining whole-body tumor burden in pediatric lymphoma patients using CNN is fast and feasible in clinical practice.

Everything is connected: Inference and attractors in delusions.

Delusions are, by popular definition, false beliefs that are held with certainty and resistant to contradictory evidence. They seem at odds with the notion that the brain at least approximates Bayesian inference. This is especially the case in schizophrenia, a disorder thought to relate to decreased - rather than increased - certainty in the brain's model of the world. We use an active inference Markov decision process model (a Bayes-optimal decision-making agent) to perform a simple task involving social and non-social inferences. We show that even moderate changes in some model parameters - decreasing confidence in sensory input and increasing confidence in states implied by its own (especially habitual) actions - can lead to delusions as defined above. Incorporating affect in the model increases delusions, specifically in the social domain. The model also reproduces some classic psychological effects, including choice-induced preference change, and an optimism bias in inferences about oneself. A key observation is that no change in a single parameter is both necessary and sufficient for delusions; rather, delusions arise due to conditional dependencies that create 'basins of attraction' which trap Bayesian beliefs. Simulating the effects of antidopaminergic antipsychotics - by reducing the model's confidence in its actions - demonstrates that the model can escape from these attractors, through this synthetic pharmacotherapy.

Identification and Classification of Off-Vertex Critical Points for Contour Tree Construction on Unstructured Meshes of Hexahedra.

The topology of isosurfaces changes at isovalues of critical points, making such points an important feature when building contour trees or Morse-Smale complexes. Hexahedral elements with linear interpolants can contain additional off-vertex critical points in element bodies and on element faces. Moreover, a point on the face of a hexahedron which is critical in the element-local context is not necessarily critical in the global context. Weber et al. (2002) introduce a method to determine whether critical points on faces are also critical in the global context, based on the gradient of the asymptotic decider (G. M. Nielson and B. Hamann) (1991) in each element that shares the face. However, as defined, the method of Weber et al. contains an error, and can lead to incorrect results. In this work we correct the error.

Heart Alert: A heart disease prediction system using machine learning approach and optimization techniques

Background@#Cardiovascular diseases belong to the top three leading causes of mortality in the Philippines with 17.8 % of the total deaths. Lifestyle-related habits such as alcohol consumption, smoking, poor diet and nutrition, high sedentary behavior, overweight, and obesity have been increasingly implicated in the high rates of heart disease among Filipinos leading to a significant burden to the country's healthcare system. The objective of this study was to predict the presence of heart disease using various machine learning algorithms (support vector machine, naïve Bayes, random forest, logistic regression, decision tree, and adaptive boosting) evaluated on an anonymized publicly available cardiovascular disease dataset. @*Methodology@#Various machine learning algorithms were applied on an anonymized publicly available cardiovascular dataset from a machine learning data repository (IEEE Dataport). A web-based application system named Heart Alert was developed based on the best machine learning model that would predict the risk of developing heart disease. An assessment of the effects of different optimization techniques as to the imputation methods (mean, median, mode, and multiple imputation by chained equations) and as to the feature selection method (recursive feature elimination) on the classification performance of the machine learning algorithms was made. All simulation experiments were implemented via Python 3.8 and its machine learning libraries (Scikit-learn, Keras, Tensorflow, Pandas, Matplotlib, Seaborn, NumPy). @*Results@#The support vector machine without imputation and feature selection obtained the highest performance metrics (90.2% accuracy, 87.7% sensitivity, 93.6% specificity, 94.9% precision, 91.2% F1-score and an area under the receiver operating characteristic curve of 0.902 ) and was used to implement the heart disease prediction system (Heart Alert). Following very closely were random forest with mean or median imputation and logistic regression with mode imputation, all having no feature selection which also performed well. @*Conclusion@#The performance of the best four machine learning models suggests that for this dataset, imputation technique for missing values may or may not be done. Likewise, recursive feature elimination for feature selection may not apply as all variables seem to be important in heart disease prediction. An early accurate diagnosis leading to prompt intervention efforts is very crucial as it improves the patient's quality of life and diminishes the risk of developing cardiac events.

Major depressive disorder prediction using data science

Background@#Major depressive disorder is a mood disorder that has affected many people worldwide. It is characterized by persistently low or depressed mood, anhedonia or decreased interest in pleasurable activities, feelings of guilt or worthlessness, lack of energy, poor concentration, appetite changes, psychomotor retardation or agitation, sleep disturbances, or suicidal thoughts. @*Objective@#The objective of the study was to predict the presence of major depressive disorder using a variety of machine learning classification algorithms (logistic regression, Naive Bayes, support vector machine, random forest, adaptive boosting, and extreme gradient boosting) on a publicly available depression dataset. @*Methodology@#After data pre-processing, several experiments were performed to assess the recursive feature elimination with cross validation as a feature selection method and synthetic minority over-sampling technique to address dataset imbalance. Several machine learning algorithms were applied on an anonymized publicly available depression dataset. Feature importance of the top performing models were also generated. All simulation experiments were implemented via Python 3.8 and its machine learning libraries (Scikit-learn, Keras, Tensorflow, Pandas, Matplotlib, Seaborn, NumPy). @*Results@#The top performing model was obtained by logistic regression with excellent performance metrics (91% accuracy, 93% sensitivity, 85% specificity, 93% recall, 93% F1-score, and 0.78 Matthews correlation coefficient). Feature importance scores of the most relevant attribute were also generated for the best model. @*Conclusion@#The findings suggest the utility of data science techniques powered by machine learning models to make a diagnosis of major depressive disorders with acceptable results. The potential deployment of these machine learning models in clinical practice can further enhance the diagnostic acumen of health professionals. Using data analytics and machine learning, data scientists can have a better understanding of mental health illness contributing to prompt and improved diagnosis thereby leading to the institution of early intervention and medical treatments ensuring the best quality of care for our patients.

Thyroid disorder classification using machine learning models

Introduction@#Thyroid hormones are produced by the thyroid gland and are essential for regulating the basal metabolic rate. Abnormalities in the levels of these hormones lead to two classes of thyroid diseases – hyperthyroidism and hypothyroidism. Detection and monitoring of these two general classes of thyroid diseases require accurate measurement and interpretation of thyroid function tests. The clinical utility of machine learning models to predict a class of thyroid disorders has not been fully elucidated. @*Objective@#The objective of this study is to develop machine learning models that classify the type of thyroid disorder on a publicly available thyroid disease dataset extracted from a machine learning data repository. @*Methods@#Several machine learning algorithms for classifying thyroid disorders were utilized after a series of pre-processing steps applied on the dataset. @*Results@#The best performing model was obtained by with XGBoost with a 99% accuracy and showing very good recall, precision, and F1-scores for each of the three thyroid classes. Generally, all models with the exception of Naïve Bayes did well in predicting the negative class generating over 90% in all metrics. For predicting hypothyroidism, XGBoost, decision tree and random forest obtained the most superior performance with metric values ranging from 96-100%. On the other end in predicting hyperthyroidism, all models have lower classification performance as compared to the negative and hypothyroid classes Needless to say, XGBoost and random forest did obtain good metric values ranging from 71-89% in predicting hyperthyroid class. @*Conclusion@#The findings of this study were encouraging and had generated useful insights in the application and development of faster automated models with high reliability which can be of use to clinicians in the assessment of thyroid diseases. The early and prompt clinical assessment coupled with the integration of these machine learning models in practice can be used to determine prompt and precise diagnosis and to formulate personalized treatment options to ensure the best quality of care to our patients.

Endothelial SOCS3 maintains homeostasis and promotes survival in endotoxemic mice.

SOCS3 is the main inhibitor of the JAK/STAT3 pathway. This pathway is activated by interleukin 6 (IL-6), a major mediator of the cytokine storm during shock. To determine its role in the vascular response to shock, we challenged mice lacking SOCS3 in the adult endothelium (SOCS3iEKO) with a nonlethal dose of lipopolysaccharide (LPS). SOCS3iEKO mice died 16-24 hours postinjection after severe kidney failure. Loss of SOCS3 led to an LPS-induced type I IFN-like program and high expression of prothrombotic and proadhesive genes. Consistently, we observed intraluminal leukocyte adhesion and neutrophil extracellular trap-osis (NETosis), as well as retinal venular leukoembolization. Notably, heterozygous mice displayed an intermediate phenotype, suggesting a gene dose effect. In vitro studies were performed to study the role of SOCS3 protein levels in the regulation of the inflammatory response. In human umbilical vein endothelial cells, pulse-chase experiments showed that SOCS3 protein had a half-life less than 20 minutes. Inhibition of SOCS3 ubiquitination and proteasomal degradation led to protein accumulation and a stronger inhibition of IL-6 signaling and barrier function loss. Together, our data demonstrate that the regulation of SOCS3 protein levels is critical to inhibit IL-6-mediated endotheliopathy during shock and provide a promising therapeutic avenue to prevent multiorgan dysfunction through stabilization of endothelial SOCS3.

Blood DNA methylation and COVID-19 outcomes.

BACKGROUND: There are no prior reports that compare differentially methylated regions of DNA in blood samples from COVID-19 patients to samples collected before the SARS-CoV-2 pandemic using a shared epigenotyping platform. We performed a genome-wide analysis of circulating blood DNA CpG methylation using the Infinium Human MethylationEPIC BeadChip on 124 blood samples from hospitalized COVID-19-positive and COVID-19-negative patients and compared these data with previously reported data from 39 healthy individuals collected before the pandemic. Prospective outcome measures such as COVID-19-GRAM risk-score and mortality were combined with methylation data. RESULTS: Global mean methylation levels did not differ between COVID-19 patients and healthy pre-pandemic controls. About 75% of acute illness-associated differentially methylated regions were located near gene promoter regions and were hypo-methylated in comparison with healthy pre-pandemic controls. Gene ontology analyses revealed terms associated with the immune response to viral infections and leukocyte activation; and disease ontology analyses revealed a predominance of autoimmune disorders. Among COVID-19-positive patients, worse outcomes were associated with a prevailing hyper-methylated status. Recursive feature elimination identified 77 differentially methylated positions predictive of COVID-19 severity measured by the GRAM-risk score. CONCLUSION: Our data contribute to the awareness that DNA methylation may influence the expression of genes that regulate COVID-19 progression and represent a targetable process in that setting.

The pulse: transient fMRI signal increases in subcortical arousal systems during transitions in attention.

Studies of attention emphasize cortical circuits for salience monitoring and top-down control. However, subcortical arousal systems have a major influence on dynamic cortical state. We hypothesize that task-related increases in attention begin with a "pulse" in subcortical arousal and cortical attention networks, which are reflected indirectly through transient fMRI signals. We conducted general linear model and model-free analyses of fMRI data from two cohorts and tasks with mixed block and event-related design. 46 adolescent subjects at our center and 362 normal adults from the Human Connectome Project participated. We identified a core shared network of transient fMRI increases in subcortical arousal and cortical salience/attention networks across cohorts and tasks. Specifically, we observed a transient pulse of fMRI increases both at task block onset and with individual task events in subcortical arousal areas including midbrain tegmentum, thalamus, nucleus basalis and striatum; cortical-subcortical salience network regions including the anterior insula/claustrum and anterior cingulate cortex/supplementary motor area; in dorsal attention network regions including dorsolateral frontal cortex and inferior parietal lobule; as well as in motor regions including cerebellum, and left hemisphere hand primary motor cortex. The transient pulse of fMRI increases in subcortical and cortical arousal and attention networks was consistent across tasks and study populations, whereas sustained activity in these same networks was more variable. The function of the transient pulse in these networks is unknown. However, given its anatomical distribution, it could participate in a neuromodulatory surge of activity in multiple parallel neurotransmitter systems facilitating dynamic changes in conscious attention.

MEF2 (Myocyte Enhancer Factor 2) Is Essential for Endothelial Homeostasis and the Atheroprotective Gene Expression Program.

OBJECTIVE: Atherosclerosis predominantly forms in regions of oscillatory shear stress while regions of laminar shear stress are protected. This protection is partly through the endothelium in laminar flow regions expressing an anti-inflammatory and antithrombotic gene expression program. Several molecular pathways transmitting these distinct flow patterns to the endothelium have been defined. Our objective is to define the role of the MEF2 (myocyte enhancer factor 2) family of transcription factors in promoting an atheroprotective endothelium. Approach and Results: Here, we show through endothelial-specific deletion of the 3 MEF2 factors in the endothelium, Mef2a, -c, and -d, that MEF2 is a critical regulator of vascular homeostasis. MEF2 deficiency results in systemic inflammation, hemorrhage, thrombocytopenia, leukocytosis, and rapid lethality. Transcriptome analysis reveals that MEF2 is required for normal regulation of 3 pathways implicated in determining the flow responsiveness of the endothelium. Specifically, MEF2 is required for expression of Klf2 and Klf4, 2 partially redundant factors essential for promoting an anti-inflammatory and antithrombotic endothelium. This critical requirement results in phenotypic similarities between endothelial-specific deletions of Mef2a/c/d and Klf2/4. In addition, MEF2 regulates the expression of Notch family genes, Notch1, Dll1, and Jag1, which also promote an atheroprotective endothelium. In contrast to these atheroprotective pathways, MEF2 deficiency upregulates an atherosclerosis promoting pathway through increasing the amount of TAZ (transcriptional coactivator with PDZ-binding motif). CONCLUSIONS: Our results implicate MEF2 as a critical upstream regulator of several transcription factors responsible for gene expression programs that affect development of atherosclerosis and promote an anti-inflammatory and antithrombotic endothelium. Graphic Abstract: A graphic abstract is available for this article.

Worldwide Diagnostic Reference Levels for Single-Photon Emission Computed Tomography Myocardial Perfusion Imaging: Findings From INCAPS.

OBJECTIVES: This study sought to establish worldwide and regional diagnostic reference levels (DRLs) and achievable administered activities (AAAs) for single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI). BACKGROUND: Reference levels serve as radiation dose benchmarks to compare individual laboratories against aggregated data, helping to identify sites in greatest need of dose reduction interventions. DRLs for SPECT MPI have previously been derived from national or regional registries. To date there have been no multiregional reports of DRLs for SPECT MPI from a single standardized dataset. METHODS: Data were submitted voluntarily to the INCAPS (International Atomic Energy Agency Nuclear Cardiology Protocols Study), a cross-sectional, multinational registry of MPI protocols. A total of 7,103 studies were included. DRLs and AAAs were calculated by protocol for each world region and for aggregated worldwide data. RESULTS: The aggregated worldwide DRLs for rest-stress or stress-rest studies employing technetium Tc 99m-labeled radiopharmaceuticals were 11.2 mCi (first dose) and 32.0 mCi (second dose) for 1-day protocols, and 23.0 mCi (first dose) and 24.0 mCi (second dose) for multiday protocols. Corresponding AAAs were 10.1 mCi (first dose) and 28.0 mCi (second dose) for 1-day protocols, and 17.8 mCi (first dose) and 18.7 mCi (second dose) for multiday protocols. For stress-only technetium Tc 99m studies, the worldwide DRL and AAA were 18.0 mCi and 12.5 mCi, respectively. Stress-first imaging was used in 26% to 92% of regional studies except in North America where it was used in just 7% of cases. Significant differences in DRLs and AAAs were observed between regions. CONCLUSIONS: This study reports reference levels for SPECT MPI for each major world region from one of the largest international registries of clinical MPI studies. Regional DRLs may be useful in establishing or revising guidelines or simply comparing individual laboratory protocols to regional trends. Organizations should continue to focus on establishing standardized reporting methods to improve the validity and comparability of regional DRLs.

Comparison of MRI, [18F]FDG PET/CT, and 99mTc-UBI 29-41 scintigraphy for postoperative spondylodiscitis-a prospective multicenter study.

PURPOSE: Postoperative infection still constitutes an important complication of spine surgery, and the optimal imaging modality for diagnosing postoperative spine infection has not yet been established. The aim of this prospective multicenter study was to assess the diagnostic performance of three imaging modalities in patients with suspected postoperative spine infection: MRI, [18F]FDG PET/CT, and SPECT/CT with 99mTc-UBI 29-41. METHODS: Patients had to undergo at least 2 out of the 3 imaging modalities investigated. Sixty-three patients enrolled fulfilled such criteria and were included in the final analysis: 15 patients underwent all 3 imaging modalities, while 48 patients underwent at least 2 imaging modalities (MRI + PET/CT, MRI + SPECT/CT, or PET/CT + SPECT/CT). Final diagnosis of postoperative spinal infection was based either on biopsy or on follow-up for at least 6 months. The MRI, PET/CT, and SPECT/CT scans were read blindly by experts at designated core laboratories. Spine surgery included metallic implants in 46/63 patients (73%); postoperative spine infection was diagnosed in 30/63 patients (48%). RESULTS: Significant discriminants between infection and no infection included fever (P = 0.041), discharge at the wound site (P < 0.0001), and elevated CRP (P = 0.042). There was no difference in the frequency of infection between patients who underwent surgery involving spinal implants versus those who did not. The diagnostic performances of MRI and [18F]FDG PET/CT analyzed as independent groups were equivalent, with values of the area under the ROC curve equal to 0.78 (95% CI: 0.64-0.92) and 0.80 (95% CI: 0.64-0.98), respectively. SPECT/CT with 99mTc-UBI 29-41 yielded either unacceptably low sensitivity (44%) or unacceptably low specificity (41%) when adopting more or less stringent interpretation criteria. The best diagnostic performance was observed when combining the results of MRI with those of [18F]FDG PET/CT, with an area under the ROC curve equal to 0.938 (95% CI: 0.80-1.00). CONCLUSION: [18F]FDG PET/CT and MRI both possess equally satisfactory diagnostic performance in patients with suspected postoperative spine infection, the best diagnostic performance being obtained by combining MRI with [18F]FDG PET/CT. The diagnostic performance of SPECT/CT with 99mTc-UBI 29-41 was suboptimal in the postoperative clinical setting explored with the present study.

Pre-trained convolutional neural networks in the assessment of bone scans for metastasis

Background@#Numerous applications of artificial intelligence have been applied in radiological imaging ranging from computer-aided diagnosis based on machine learning to deep learning using convolutional neural networks. One of the nuclear medicine imaging tests being commonly performed today is bone scan. The use of deep learning methods through convolutional neural networks in bone scintigrams has not been fully explored. Very few studies have been published on its diagnostic capability of convolutional neural networks in assessing osseous metastasis. @*Objective@#The aim of our study is to assess the classification performance of the pre-trained convolutional neural networks in the diagnosis of bone metastasis from whole body bone scintigrams of a local institutional dataset. @*Methods@#Bone scintigrams from all types of cancer were retrospectively reviewed during the period 2019-2020 at the University of Perpetual Help Medical Center in Las Pinas City, Metro Manila. The study was approved by the Institutional Ethical Review Board and Technical Review Board of the medical center. Bone scan studies should be mainly for metastasis screening. The pre-processing techniques consisting of image normalization, image augmentation, data shuffling, and train-test split (testing at 30% and the rest (70%) was split 85% for training and 15% for validation) were applied to image dataset. Three pre-trained architectures (ResNet50, VGG19, DenseNet121) were applied to the processed dataset. Performance metrics such as accuracy, recall (sensitivity), precision (positive predictive value), and F1-scores were obtained.@*Results@#A total of 570 bone scan images with dimension 220 x 646 pixel sizes in .tif file format were included in this study with 40% classified with bone metastasis while 60% were classified as without bone metastasis. DenseNet121 yielded the highest performance metrics with an accuracy rate of 83%, 76% recall, 86% precision, and 81% F1-score. ResNet50 and VGG19 had similar performance with each other across all metrics but generally lower predictive capability as compared to DenseNet121.@*Conclusion@#A bone metastasis machine learning classification study using three pre-trained convolutional neural networks was performed on a local medical center bone scan dataset via transfer learning. DenseNet121 generated the highest performance metrics with 83% accuracy, 76% recall, 86% precision and 81% F1-score. Our simulation experiments generated promising outcomes and potentially could lead to its deployment in the clinical practice of nuclear medicine physicians. The use of deep learning techniques through convolutional neural networks has the potential to improve diagnostic capability of nuclear medicine physicians using bone scans for the assessment of metastasis.