1,721,159 research outputs found
Integrating radiomic and 3D autoencoder-based features for Non-Small Cell Lung Cancer survival analysis
Full text linkBackground and objectives: The aim of this study is to develop a radiomic and deep learning-based signature for survival analysis of patients with Non-Small Cell Lung Cancer. Methods: Four-hundred twenty-two patients from “Lung1” dataset were included in the study. A 3D convolutional autoencoder (AE) was built and features from the latent space extracted for further analysis. Radiomic features were derived from the 3D volume of the tumor region using PyRadiomics. Both radiomic and AE-based features underwent feature selection, by removing: i) highly correlated and ii) constant features. The selected variables were then used to derive both mono-domain (radiomics, AE and clinic) and multi-domain signatures fitting a Cox Proportional Hazard model with LASSO penalization and evaluated considering the concordance (C)-index as performance metric. Results: Both mono-domain and multi-domain signatures could significantly differentiate high risk from low risk patients. Among the mono-domain signatures, the highest hazard ratio (HR) in the test set was obtained using radiomics (HR = 1.5428) followed by the AE-based signature (HR = 1.5012) and the clinical signature (HR = 1.4770). The best overall performance was achieved by combining all three signatures, resulting in the highest HR (HR = 1.7383), while the combination of AE-based and clinical signatures yielded the highest C-index (C-index = 0.6309). Conclusions: These preliminary results show that combining information carried by AE, radiomic and clinical domain shows potential for improving the prediction of overall survival in NSCLC patients
Assessing the Performance of MRI-Radiomic Prognostic Signatures in Head and Neck Cancer Patients: A Comparative Analysis
Full text linkAutomatic Right Ventricular Hypertrophic Detection Integrating Electrocardiography-based QRS Biomarkers with Machine Learning
Full text linkSpectral Analysis of Blood Pressure Variability in Atrial Fibrillation: the Effect of Tilting
No full textOn the reliability of frequency components in systolic arterial pressure in patients with atrial fibrillation
No full text
Analysis of Surface Atrial Signal Using Spectral Method for Time Series with Missing Data
No full textA Statistical Atrioventricular Node Model Accounting for Pathway Switching During Atrial Fibrillation
No full textObjective: The atrioventricular (AV) node plays a central role in atrial fibrillation (AF) as it influences the conduction of impulses from the atria into the ventricles. In the present paper, the statistical dual pathway AV node model, previously introduced by us, is modified so that it accounts for atrial impulse pathway switching even if the preceding impulse did not cause a ventricular activation. Methods: The proposed change in model structure implies that the number of model parameters subjected to maximum likelihood estimation is reduced from five to four. The model is evaluated using the data acquired in the RATe control in Atrial Fibrillation (RATAF) study, involving 24- h ECG recordings from 60 patients with permanent AF. Results: When fitting the models to the RATAF database, similar results were obtained for both the present and the previous model, with a median fit of 86%. The results show that the parameter estimates characterizing refractory period prolongation exhibit considerably lower variation when using the present model, a finding that may be ascribed to fewer model parameters. Conclusion: The new model maintains the capability to model RR intervals, while providing more reliable parameters estimates. Significance: The model parameters are expected to convey novel clinical information, and may be useful for predicting the effect of rate control drugs
Atrial fibrillatory rate in the clinical context: natural course and prediction of intervention outcome
Full text linkShortening of atrial refractory period during atrial fibrillation has been considered a hallmark of atrial electrical remodelling. The atrial fibrillatory cycle length, which is intimately related to the atrial fibrillatory rate (AFR), is generally accepted as a surrogate marker for local refractoriness. The value of using AFR to monitor the progress of atrial ablation therapy has been demonstrated and gradual slowing of AFR has consistently been observed to precede arrhythmia termination during paroxysmal or permanent atrial fibrillation ablation. Today, AFR is the key characteristic of the fibrillatory process, repeatedly validated against intracardiac recordings and extensively studied in clinical contexts. This paper provides an overview of clinical data accumulated since the method was introduced in 1998, and to present the current state of knowledge regarding ECG-derived AFR: its time course and dynamics, clinical factors affecting AFR, and available evidence of its value in the clinical context. We conclude that AFR is a promising, easily available AF characteristic that can be derived from the conventional surface ECG. It is clearly a useful tool for monitoring drug effects. Reference values for predicting intervention effect, however, are likely to be population- and context-specific and related to age, clinical types of atrial fibrillation, as well as to presence and advancement of underlying structural heart disease. Prospective studies in homogeneous patient populations are still needed to establish the clinical value of AFR
Beta-blockade and A1-adenosine receptor agonist effects on atrial fibrillatory rate and atrio-ventricular conduction in patients with atrial fibrillation
Full text linkReduced irregularity of RR intervals in permanent atrial fibrillation (AF) has been associated with poor outcome. It is not fully understood, however, whether modification of atrioventricular (AV) conduction using rate-control drugs affects RR variability and irregularity measures. We aimed at assessing whether atrial fibrillatory rate (AFR) and variability and irregularity of the ventricular rate are modified by a selective A1-adenosine receptor agonist tecadenoson, beta-blocker esmolol, and their combination. Twenty-one patients (age 58 7 years, 13 men) with AF were randomly assigned to either 75, 150, or 300 g intravenous tecadenoson. Tecadenoson was administered alone (Dose Period 1) and in combination (Dose Period 2) with esmolol (100 g/kg/min for 10 min then 50 g/kg/min for 50 min). Heart rate (HR) and AFR were estimated for every 10 min long recording segment. Similarly, for every 10 min segment, the variability of RR intervals was assessed, as standard deviation, pNN20, pNN50, pNN80, and the root of the mean squared differences of successive RR intervals, and irregularity was assessed by non-linear measures such as regularity index (R) and approximate entropy. A marked decrease in HR was observed after both tecadenoson injections, whereas almost no changes could be seen in the AFR. The variability parameters were increased after the first tecadenoson bolus injection. In contrast, the irregularity parameters did not change after tecadenoson. When esmolol was infused, all the variability parameters further increased. Modification of AV node conduction can increase RR variability but does not affect regularity of RR intervals or AFR
- …
