AI-based hip prosthesis failure prediction through evolutional radiological indices.

INTRODUCTION: This study aimed to develop artificial intelligence models for predicting hip implant failure from radiological features. Analyzing the evolution of the periprosthetic bone and implant's position throughout the entire follow-up period has shown the potential to be more relevant in outcome prediction than simply considering the latest radiographic images. Thus, we investigated an AI-based model employing a small set of evolutional parameters derived from conventional radiological features to predict hip prosthesis failure. MATERIALS AND METHODS: One hundred sixty-nine radiological features were annotated from historical anteroposterior and lateral radiographs for 162 total hip arthroplasty patients, 32 of which later underwent implant failure. Linear regression on each patient's chronologically sorted radiological features was employed to derive 169 corresponding evolutional parameters per image. Three sets of machine learning predictors were developed: one employing the original features (standard model), one the evolutional ones (evolutional model), and the last their union (hybrid model). Each set included a model employing all the available features (full model) and a model employing the few most predictive ones according to Gini importance (minimal model). RESULTS: The evolutional and hybrid predictors resulted highly effective (area under the ROC curve (AUC) of full models = 0.94), outperforming the standard one, whose AUC was only 0.82. The minimal hybrid model, employing just four features, three of which evolutional, scored an AUC of 0.95, proving even more accurate than the full one, exploiting 173 features. This tool could be shaped to be either highly specific (sensitivity: 80%, specificity: 98.6%) or highly sensitive (sensitivity: 90%, specificity: 92.4%). CONCLUSION: The proposed predictor may represent a highly sensitive screening tool for clinicians, capable to predict THA failure with an advance between a few months and more than a year through only four radiological parameters, considering either their value at the latest visit or their evolution through time.

Relevance of apparent diffusion coefficient features for a radiomics-based prediction of response to induction chemotherapy in sinonasal cancer.

In this paper, several radiomics-based predictive models of response to induction chemotherapy (IC) in sinonasal cancers (SNCs) are built and tested. Models were built as a combination of radiomic features extracted from three types of MRI images: T1-weighted images, T2-weighted images and apparent diffusion coefficient (ADC) maps. Fifty patients (aged 54 ± 12 years, 41 men) were included in this study. Patients were classified according to their response to IC (25 responders and 25 nonresponders). Not all types of images were acquired for all of the patients: 49 had T1-weighted images, 50 had T2-weighted images and 34 had ADC maps. Only in a subset of 33 patients were all three types of image acquired. Eighty-nine radiomic features were extracted from the MRI images. Dimensionality reduction was performed by using principal component analysis (PCA) and by selecting only the three main components. Different algorithms (trees ensemble, K-nearest neighbors, support vector machine, naïve Bayes) were used to classify the patients as either responders or nonresponders. Several radiomic models (either monomodality or multimodality obtained by a combination of T1-weighted, T2-weighted and ADC images) were developed and the performance was assessed through 100 iterations of train and test split. The area under the curve (AUC) of the models ranged from 0.56 to 0.78. Trees ensemble, support vector machine and naïve Bayes performed similarly, but in all cases ADC-based models performed better. Trees ensemble gave the highest AUC (0.78 for the T1-weighted+T2-weighted+ADC model) and was used for further analyses. For trees ensemble, the models based on ADC features performed better than those models that did not use those features (P < 0.02 for one-tail Hanley test, AUC range 0.68-0.78 vs 0.56-0.69) except the T1-weighted+ADC model (AUC 0.71 vs 0.69, nonsignificant differences). The results suggest the relevance of ADC-based radiomics for prediction of response to IC in SNCs.

Diffusion-weighted MRI radiomics of spine bone tumors: feature stability and machine learning-based classification performance.

PURPOSE: To evaluate stability and machine learning-based classification performance of radiomic features of spine bone tumors using diffusion- and T2-weighted magnetic resonance imaging (MRI). MATERIAL AND METHODS: This retrospective study included 101 patients with histology-proven spine bone tumor (22 benign; 38 primary malignant; 41 metastatic). All tumor volumes were manually segmented on morphologic T2-weighted sequences. The same region of interest (ROI) was used to perform radiomic analysis on ADC map. A total of 1702 radiomic features was considered. Feature stability was assessed through small geometrical transformations of the ROIs mimicking multiple manual delineations. Intraclass correlation coefficient (ICC) quantified feature stability. Feature selection consisted of stability-based (ICC > 0.75) and significance-based selections (ranking features by decreasing Mann-Whitney p-value). Class balancing was performed to oversample the minority (i.e., benign) class. Selected features were used to train and test a support vector machine (SVM) to discriminate benign from malignant spine tumors using tenfold cross-validation. RESULTS: A total of 76.4% radiomic features were stable. The quality metrics for the SVM were evaluated as a function of the number of selected features. The radiomic model with the best performance and the lowest number of features for classifying tumor types included 8 features. The metrics were 78% sensitivity, 68% specificity, 76% accuracy and AUC 0.78. CONCLUSION: SVM classifiers based on radiomic features extracted from T2- and diffusion-weighted imaging with ADC map are promising for classification of spine bone tumors. Radiomic features of spine bone tumors show good reproducibility rates.

Prognostic role of pre-treatment magnetic resonance imaging (MRI)-based radiomic analysis in effectively cured head and neck squamous cell carcinoma (HNSCC) patients.

OBJECTIVES: To identify and validate baseline magnetic resonance imaging (b-MRI) radiomic features (RFs) as predictors of disease outcomes in effectively cured head and neck squamous cell carcinoma (HNSCC) patients. MATERIALS AND METHODS: Training set (TS) and validation set (VS) were retrieved from preexisting datasets (HETeCo and BD2Decide trials, respectively). Only patients with both pre- and post-contrast enhancement T1 and T2-weighted b-MRI and at least 2 years of follow-up (FUP) were selected. The combination of the best extracted RFs was used to classify low risk (LR) vs. high risk (HR) of disease recurrence. Sensitivity, specificity, and area under the curve (AUC) of the radiomic model were computed on both TS and VS. Overall survival (OS) and 5-year disease-free survival (DFS) Kaplan-Meier (KM) curves were compared for LR vs. HR. The radiomic-based risk class was used in a multivariate Cox model, including well-established clinical prognostic factors (TNM, sub-site, human papillomavirus [HPV]). RESULTS: In total, 57 patients of TS and 137 of VS were included. Three RFs were selected for the signature. Sensitivity of recurrence risk classifier was 0.82 and 0.77, specificity 0.78 and 0.81, AUC 0.83 and 0.78 for TS and VS, respectively. VS KM curves for LR vs. HR groups significantly differed both for 5-year DFS (p<.0001) and OS (p=.0004). A combined model of RFs plus TNM improved prognostic performance as compared to TNM alone, both for VS 5-year DFS (C-index: 0.76 vs. 0.60) and OS (C-index: 0.74 vs. 0.64). CONCLUSIONS: Radiomics of b-MRI can help to predict recurrence and survival outcomes in HNSCC.

Radiomic analysis of soft tissues sarcomas can distinguish intermediate from high-grade lesions.

PURPOSE: To assess the feasibility of grading soft tissue sarcomas (STSs) using MRI features (radiomics). MATERIALS AND METHODS: MRI (echo planar SE, 1.5T) from 19 patients with STSs and a known histological grading, were retrospectively analyzed. The apparent diffusion coefficient (ADC) maps, obtained by diffusion-weighted imaging acquisitions, were analyzed through 65 radiomic features, intensity-based (first order statistics, FOS) and texture (gray level co-occurrence matrix, GLCM; and gray level run length matrix, GLRLM) features. Feature selection (sequential forward floating search) and classification (k-nearest neighbor classifier) were performed to distinguish intermediate- from high-grade STSs. Classification was performed using the three different sub-groups of features separately as well as all the features together. The entire dataset was divided in three subsets: the training, validation and test set, containing, respectively, 60, 30, and 10% of the data. RESULTS: Intermediate-grade lesions had a higher and less disperse ADC values compared with high-grade ones: most of FOS related to intensity are higher for the intermediate-grade STSs, while FOS related to signal variability were higher in the high grade (e.g., the feature variance is 2.6*105 ± 0.9*105 versus 3.3*105 ± 1.6*105 , P = 0.3). The GLCM features related to entropy and dissimilarity were higher in the high-grade. When performing classification, the best accuracy is obtained with a maximum of three features for each subgroup, FOS features being those leading to the best classification (validation set: FOS accuracy 0.90 ± 0.11, area under the curve [AUC] 0.85 ± 0.16; test set: FOS accuracy 0.88 ± 0.25, AUC 0.87 ± 0.34). CONCLUSION: Good accuracy and AUC could be obtained using only few Radiomic features, belonging to the FOS class. LEVEL OF EVIDENCE: 4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:829-840.

Assessment of Stability and Discrimination Capacity of Radiomic Features on Apparent Diffusion Coefficient Images.

The objectives of the study are to develop a new way to assess stability and discrimination capacity of radiomic features without the need of test-retest or multiple delineations and to use information obtained to perform a preliminary feature selection. Apparent diffusion coefficient (ADC) maps were computed from diffusion-weighted magnetic resonance images (DW-MRI) of two groups of patients: 18 with soft tissue sarcomas (STS) and 18 with oropharyngeal cancers (OPC). Sixty-nine radiomic features were computed, using three different histogram discretizations (16, 32, and 64 bins). Geometrical transformations (translations) of increasing entity were applied to the regions of interest (ROIs), and the intra-class correlation coefficient (ICC) was used to compare the features computed on the original and modified ROIs. The distribution of ICC values for minimal and maximal entity translations (ICC10 and ICC100, respectively) was used to adjust thresholds of ICC (ICCmin and ICCmax) used to discriminate between good, unstable (ICC10 < ICCmin), and non-discriminative features (ICC100 > ICCmax). Fifty-four and 59 radiomic features passed the stability-based selection for all the three histogram discretizations for the OPC and STS datasets, respectively. The excluded features were similar across the different histogram discretizations (Jaccard's index 0.77 ± 0.13 and 0.9 ± 0.1 for OPC and STS, respectively) but different between datasets (Jaccard's index 0.19 ± 0.02). The results suggest that the observed radiomic features are mainly stable and discriminative, but the stability depends on the region of the body under observation. The method provides a way to assess stability without the need of test-retest or multiple delineations.

Autonomic influence on atrial fibrillatory process: head-up and head-down tilting.

BACKGROUND: Changes in the autonomic nervous system (ANS) tone are present before, during, and after episodes of atrial fibrillation (AF). Atrial fibrillatory rate (AFR, the inverse of the atrial cycle length) has been used as a surrogate marker for local refractoriness and is a key characteristic of the fibrillatory process in patients with AF. Aim of this study is to assess changes in AFR, as an effect of autonomic balance change. METHODS: Forty patients undergoing cardiac cardioversion for symptomatic persistent AF were included in the study. Surface ECG was recorded during rest, head-down (HDT, -30°), and head-up tilt (HUT, +60°). A median value of AFR was computed in each phase of the protocol. RESULTS: AFR decreased during HDT compared to the baseline (B) condition in all patients but three (median AFR_B = 391 fpm vs. AFR_HDT = 377 fpm, p < .0001). HUT increased AFR, making it significantly higher than HDT and baseline conditions (median AFR_HUT = 396 fpm, p < .0001 vs. B and HDT). Heart rate (HR) increased during HUT, but had a heterogeneous behavior in the population during HDT: about one third of the patients had an HR lower during HDT than during baseline, whereas the remaining two third had an increase in HR during HDT. CONCLUSIONS: Dominant sympathetic/vagal tone during HUT/HDT significantly affects AFR, increasing/decreasing in respect to baseline. It may be worth exploring the possibility that patients with AF of shorter duration can convert to sinus rhythm during HDT.

Circadian variation of variability and irregularity of heart rate in patients with permanent atrial fibrillation: relation to symptoms and rate control drugs.

The aim of the present study was to evaluate diurnal variations of the variability and irregularity of heart rate (HR) in patients with permanent atrial fibrillation (AF) with and without rate control drugs. Thirty-eight patients with permanent AF were part of an investigator-blind crossover study comparing diltiazem, verapamil, metoprolol, and carvedilol. We analyzed five Holter recordings per patient: at baseline (no rate control drug) and with each of the four drug regimens. HR, variability (SD; percentages of interval differences of successive RR intervals of >20, 50, and 80 ms; and root of the mean squared differences of successive RR intervals), and irregularity (approximate and sample entropy) parameters were computed in 20-min long nonoverlapping segments. Circadian rhythmicity was evaluated using cosinor analysis to each parameter series, which is characterized by the 24-h mean [midline statistic of rhythm (MESOR)] and excursion over the mean (amplitude). Arrhythmia-related symptoms were assessed by a questionnaire measuring symptom severity and frequency. HR and variability parameters showed a significant circadian variation in most patients, whereas only a small minority of the patients had circadian variations of irregularity parameters. Patients with circadian approximate entropy n at baseline had more severe symptoms (symptom severity: 9 ± 4 vs. 6 ± 5, P < 0.05, circadian vs. noncircadian variations). All drugs decreased the MESOR of HR and increased the MESOR of variability parameters. Only carvedilol and metoprolol decreased the normalized amplitude over 24 h of all parameters and HR. In conclusion, HR and RR variability parameters present a circadian variation in patients with permanent AF, whereas few patients demonstrated circadian fluctuations in irregularity parameters, suggesting different physiological mechanisms.

Rate-control drugs affect variability and irregularity measures of RR intervals in patients with permanent atrial fibrillation.

INTRODUCTION: Irregularity measures have been suggested as risk indicators in patients with atrial fibrillation (AF); however, it is not known to what extent they are affected by commonly used rate-control drugs. We aimed at evaluating the effect of metoprolol, carvedilol, diltiazem, and verapamil on the variability and irregularity of the ventricular response in patients with permanent AF. METHODS AND RESULTS: Sixty patients with permanent AF were part of an investigator-blind cross-over study, comparing 4 rate-control drugs (diltiazem, verapamil, metoprolol, and carvedilol). We analyzed five 20-minute segments per patient: baseline and the 4 drug regimens. On every segment, heart rate (HR) variability and irregularity of RR series were computed. The variability was assessed as standard deviation, pNN20, pNN50, pNN80, and rMSSD. The irregularity was assessed by regularity index, approximate (ApEn), and sample entropy. A significantly lower HR was obtained with all drugs, the HR was lowest using the calcium channel blockers. All drugs increased the variability of ventricular response in respect to baseline (as an example, rMSSD: baseline 171 ± 47 milliseconds, carvedilol 229 ± 58 milliseconds; P < 0.05 vs. baseline, metoprolol 226 ± 66 milliseconds; P < 0.05 vs. baseline, verapamil 228 ± 84; P < 0.05 vs. baseline, diltiazem 256 ± 87 milliseconds; P < 0.05 vs. baseline and all other drugs). Only ß-blockers significantly increased the irregularity of the RR series (as an example, ApEn: baseline 1.86 ± 0.13, carvedilol 1.92 ± 0.09; P < 0.05 vs. baseline, metoprolol 1.93 ± 0.08; P < 0.05 vs. baseline, verapamil 1.86 ± 0.22 ns, diltiazem 1.88 ± 0.16 ns). CONCLUSION: Modification of AV node conduction by rate-control drugs increase RR variability, while only ß-blockers affect irregularity.

Noninvasive Assessment of Atrioventricular Nodal Function: Effect of Rate-Control Drugs during Atrial Fibrillation.

BACKGROUND: During atrial fibrillation (AF), conventional electrophysiological techniques for assessment of refractory period or conduction velocity of the atrioventricular (AV) node cannot be used. We aimed at evaluating changes in AV nodal properties during administration of tecadenoson and esmolol using a novel ECG-based method. METHODS: Fourteen patients (age 58 ± 8 years, 10 men) with AF were randomly assigned to either 75 or 300 µg intravenous tecadenoson. After tecadenoson wash-out, patients received esmolol continuously (100 µg/kg per min for 10 mins, then 50 µg/kg per min for 50 mins). Atrial fibrillatory rate (AFR) and heart rate (HR) were assessed in 15-min segments. Using the novel method, we assessed the absolute refractory periods of the slow and fast pathways (aRPs and aRPf) of the AV node to produce an estimate of the functional refractory period. RESULTS: During esmolol infusion, AFR and HR were significantly decreased and the absolute refractory period was significantly prolonged in both pathways (aRPs: 387 ± 73 vs 409 ± 62 ms, P < 0.05; aRPf: 490 ± 80 vs 529 ± 58 ms, P < 0.05). During both tecadenoson doses, HR decreased significantly and AFR was unchanged. Both aRPs and aRPf were prolonged for a 75 µg dose (aRPs: 322 ± 97 vs 476 ± 75 ms, P < 0.05; aRPf: 456 ± 102 vs 512 ± 55 ms, P < 0.05) whereas a trend toward prolongation was observed for a 300 µg dose. CONCLUSIONS: The estimated parameters reflect expected changes in AV nodal properties, i.e., slower conduction through the AV node for tecadenoson and prolongation of the AV node refractory period for esmolol. Thus, the proposed approach may be used to assess drug effects on the AV node in AF patients.

Noninvasive characterization of atrioventricular conduction in patients with atrial fibrillation.

The atrioventricular (AV) node plays a fundamental role in patients with atrial fibrillation (AF), acting as a filter to the numerous irregular atrial impulses which bombard the node. A phenomenological approach to better understand AV nodal electrophysiology is to analyze the ventricular response with respect to irregularity. In different cohorts of AF patients, such analysis has been performed with the aim to evaluate the association between ventricular response characteristics and long-term clinical outcome and to determine whether irregularity is affected by rate-control drugs. Another approach to studying AV nodal characteristics is to employ a mathematical model which accounts for the refractory periods of the two AV nodal pathways. With atrial fibrillatory rate and RR intervals as input, the model has been considered for analyzing data during (i) rest and head-up tilt test, (ii) tecadenoson and esmolol, and (iii) rate-control drugs. The present paper provides an overview of our recent work on the characterization and assessment of AV nodal conduction using these two approaches.

Non-invasive assessment of the effect of beta blockers and calcium channel blockers on the AV node during permanent atrial fibrillation.

AIM: We aimed at assessing changes in AV nodal properties during administration of the beta blockers metoprolol and carvedilol, and the calcium channel blockers diltiazem and verapamil from electrocardiographic data. METHODS: Parameters accounting for the functional refractory periods of the slow and fast pathways (aRPs and aRPf) were estimated using atrial fibrillatory rate (AFR) and ventricular response assessed from 15-min ECG segments recorded at baseline and on drug treatment from sixty patients with permanent AF. RESULTS: The results showed that AFR and HR were significantly reduced for all drugs, and that aRPs and aRPf were significantly prolonged in both pathways. The prolongation in aRP was significantly larger for the calcium channel blockers than for the beta blockers. CONCLUSIONS: The changes observed in the AV node parameters are in line with the results of previous electrophysiological studies performed in patients during sinus rhythm, therefore supporting the clinical value of the method.

Beta-blockade and A1-adenosine receptor agonist effects on atrial fibrillatory rate and atrioventricular conduction in patients with atrial fibrillation.

AIMS: Reduced 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. METHODS AND RESULTS: Twenty-one patients (age 58 ± 7 years, 13 men) with AF were randomly assigned to either 75, 150, or 300 µg intravenous tecadenoson. Tecadenoson was administered alone (Dose Period 1) and in combination (Dose Period 2) with esmolol (100 µg/kg/min for 10 min then 50 µg/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. CONCLUSION: Modification of AV node conduction can increase RR variability but does not affect regularity of RR intervals or AFR.

Atrial fibrillatory rate in the clinical context: natural course and prediction of intervention outcome.

Shortening 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.

Non-linear regularity of arterial blood pressure variability in patient with atrial fibrillation in tilt-test procedure.

AIMS: Dynamics of cardiovascular series may be explored with non-linear techniques. It is unknown if the arterial pressure irregularity commonly observed in patients with atrial fibrillation (AF) might be further increased by a sympathetic stimulus such as orthostatic tilt. METHODS AND RESULTS: Twenty patients (62 ± 14 years, 15 men) were recruited for the study. Continuous beat-to-beat non-invasive arterial pressure was acquired at rest and during a passive orthostatic stimulus ('tilt test'). Systolic (SAP) and diastolic (DAP) arterial pressure series of 300 samples were analysed in both conditions. Approximate (ApEn) and sample entropy (SampEn) were computed, as irregularity measures. Equivalent metrics (ApEnAR and SampEnAR) derived from an autoregressive model of the series were also obtained through numerical simulations, to further elucidate the non-linear mechanisms present in the series. In 11 patients (Group A), SAP significantly increased during tilt (from 103 ± 13 to 114 ± 17 mmHg, P < 0.001 rest vs. tilt), whereas in 9 patients (Group B) SAP remained almost unchanged (SAP: 110 ± 18 vs. 106 ± 19 mmHg, rest vs. tilt). No clinical differences were found between Groups A and B. When analysing Group A, all irregularity measures significantly increased in SAP (ApEn: 1.75 ± 0.20 vs. 1.88 ± 0.16, P < 0.05; SampEn: 1.71 ± 0.30 vs. 1.88 ± 0.27, P < 0.05; ApEnAR: 1.87 ± 0.20 vs. 1.96 ± 0.18, P < 0.05; SampEnAR: 1.94 ± 0.27 vs. 2.06 ± 0.18, P < 0.05; rest vs. tilt), whereas no differences were found in DAP series. No significant differences were found in Group B for either SAP or DAP. CONCLUSION: The alterations of SAP during tilt in AF patients are not uniform and seem associated with different regularity patterns. The pressor response to sympathetic stimulation was also associated with an increase of SAP series irregularity.

Non-invasive evaluation of the effect of metoprolol on the atrioventricular node during permanent atrial fibrillation.

AIMS: During atrial fibrillation (AF), conventional electrophysiological techniques for assessment of refractory period or conduction velocity of the atrioventricular (AV) node cannot be used. We aimed at evaluating changes in AV nodal properties during administration of metoprolol from electrocardiogram data, and to support our findings with simulated data based on results from an electrophysiological study. METHODS AND RESULTS: Sixty patients (age 71 ± 9 years, 42 men) with permanent AF were included in the RATe control in Atrial Fibrillation (RATAF) study. Two 15 min segments, during baseline and metoprolol administration, starting at 2 pm were analysed in this study. Atrial fibrillatory rate (AFR), heart rate (HR), and AV nodal parameters were assessed. The AV nodal parameters account for the probability of an impulse not taking the fast pathway, the absolute refractory periods of the slow and fast pathways (aRPs and aRPf), representing the functional refractory period, and their respective prolongation in refractory period. In addition, simulated RR series were generated that mimic metoprolol administration through prolonged AV conduction interval and AV node effective refractory period. During metoprolol administration, AFR and HR were significantly decreased and aRP was significantly prolonged in both pathways (aRPs: 337 ± 60 vs. 398 ± 79 ms, P < 0.01; aRPf: 430 ± 91 vs. 517 ± 100 ms, P < 0.01). Similar results were found for the simulated RR series, both aRPs and aRPf being prolonged with metoprolol (aRPs: 413 ± 33 vs. 437 ± 43 ms, P = 0.01; aRPf: 465 ± 40 vs. 502 ± 69 ms, P = 0.02). CONCLUSION: The AV nodal parameters reflect expected changes after metoprolol administration, i.e. a prolongation in functional refractory period. The simulations confirmed that aRPs and aRPf may serve as an estimate of the functional refractory period.

Lung nodule classification using radiomics model trained on degraded SDCT images.

BACKGROUND AND OBJECTIVE: Low-dose computed tomography (LDCT) screening has shown promise in reducing lung cancer mortality; however, it suffers from high false positive rates and a scarcity of available annotated datasets. To overcome these challenges, we propose a novel approach using synthetic LDCT images generated from standard-dose CT (SDCT) scans from the LIDC-IDRI dataset. Our objective is to develop and validate an interpretable radiomics-based model for distinguishing likely benign from likely malignant pulmonary nodules. METHODS: From a total of 1010 CT images (695 SDCTs and 315 LDCTs), we degraded SDCTs in the sinogram domain and obtained 1950 nodules as the training set. The 675 nodules from the LDCTs were stratified into 50%-50% partitions for validation and testing. Radiomic features were extracted from nodules, and three feature sets were assessed using: a) only shape and size (SS) features, b) all features but SS features, and c) all features. A systematic pipeline was developed to optimize the feature set and evaluate multiple machine learning models. Models were trained using degraded SDCT, validated and tested on the LDCT nodules. RESULTS: Training a logistic regression model using three SS features yielded the most promising results, achieving on the test set mean balanced accuracy, sensitivity, specificity, and AUC-ROC scores of 0.81, 0.76, 0.85, and 0.87, respectively. CONCLUSIONS: Our study demonstrates the feasibility and effectiveness of using synthetic LDCT images for developing a relatively accurate radiomics-based model in lung nodule classification. This approach addresses challenges associated with LDCT screening, offering potential implications for improving lung cancer detection and reducing false positives.

Developing a robust two-step machine learning multiclassification pipeline to predict primary site in head and neck carcinoma from lymph nodes.

This study aimed to develop a robust multiclassification pipeline to determine the primary tumor location in patients with head and neck carcinoma of unknown primary using radiomics and machine learning techniques. The dataset included 400 head and neck cancer patients with primary tumor in oropharynx, OPC (n = 162), nasopharynx, NPC (n = 137), oral cavity, OC (n = 63), larynx and hypopharynx, HL (n = 38). Two radiomic-based multiclassification pipelines (P1 and P2) were developed. P1 consisted in a direct identification of the primary sites, whereas P2 was based on a two-step approach: in the first step, the number of classes was reduced by merging the two minority classes which were reclassified in the second step. Diverse correlation thresholds (0.75, 0.80, 0.85), feature selection methods (sequential forwards/backwards selection, sequential floating forward selection, neighborhood component analysis and minimum redundancy maximum relevance), and classification models (neural network, decision tree, naïve Bayes, bagged trees and support vector machine) were assessed. P2 outperformed P1, with the best results obtained with the support vector machine classifier including radiomic and clinical features (accuracies of 75.3 % (HL), 75.4 % (OC), 71.3 % (OPC), 92.9 % (NPC)). These results indicate that the two-step multiclassification pipeline integrating radiomics and clinical information is a promising approach to predict the tumor site of unknown primary.

Detection of Non-Sustained Supraventricular Tachycardia in Atrial Fibrillation Screening.

OBJECTIVE: Non-sustained supraventricular tachycardia (nsSVT) is associated with a higher risk of developing atrial fibrillation (AF), and, therefore, detection of nsSVT can improve AF screening efficiency. However, the detection is challenged by the lower signal quality of ECGs recorded using handheld devices and the presence of ectopic beats which may mimic the rhythm characteristics of nsSVT. METHODS: The present study introduces a new nsSVT detector for use in single-lead, 30-s ECGs, based on the assumption that beats in an nsSVT episode exhibits similar morphology, implying that episodes with beats of deviating morphology, either due to ectopic beats or noise/artifacts, are excluded. A support vector machine is used to classify successive 5-beat sequences in a sliding window with respect to similar morphology. Due to the lack of adequate training data, the classifier is trained using simulated ECGs with varying signal-to-noise ratio. In a subsequent step, a set of rhythm criteria is applied to similar beat sequences to ensure that episode duration and heart rate is acceptable. RESULTS: The performance of the proposed detector is evaluated using the StrokeStop II database, resulting in sensitivity, specificity, and positive predictive value of 84.6%, 99.4%, and 18.5%, respectively. CONCLUSION: The results show that a significant reduction in expert review burden (factor of 6) can be achieved using the proposed detector.Clinical and Translational Impact: The reduction in the expert review burden shows that nsSVT detection in AF screening can be made considerably more efficiently.

MRI radiomics in head and neck cancer from reproducibility to combined approaches.

The clinical applicability of radiomics in oncology depends on its transferability to real-world settings. However, the absence of standardized radiomics pipelines combined with methodological variability and insufficient reporting may hamper the reproducibility of radiomic analyses, impeding its translation to clinics. This study aimed to identify and replicate published, reproducible radiomic signatures based on magnetic resonance imaging (MRI), for prognosis of overall survival in head and neck squamous cell carcinoma (HNSCC) patients. Seven signatures were identified and reproduced on 58 HNSCC patients from the DB2Decide Project. The analysis focused on: assessing the signatures' reproducibility and replicating them by addressing the insufficient reporting; evaluating their relationship and performances; and proposing a cluster-based approach to combine radiomic signatures, enhancing the prognostic performance. The analysis revealed key insights: (1) despite the signatures were based on different features, high correlations among signatures and features suggested consistency in the description of lesion properties; (2) although the uncertainties in reproducing the signatures, they exhibited a moderate prognostic capability on an external dataset; (3) clustering approaches improved prognostic performance compared to individual signatures. Thus, transparent methodology not only facilitates replication on external datasets but also advances the field, refining prognostic models for potential personalized medicine applications.