Deep learning prediction of survival in patients with heart failure using chest radiographs.

Heart failure (HF) is associated with high rates of morbidity and mortality. The value of deep learning survival prediction models using chest radiographs in patients with heart failure is currently unclear. The aim of our study is to develop and validate a deep learning survival prediction model using chest X-ray (DLSPCXR) in patients with HF. The study retrospectively enrolled a cohort of 353 patients with HF who underwent chest X-ray (CXR) at our institution between March 2012 and March 2017. The dataset was randomly divided into training (n = 247) and validation (n = 106) datasets. Univariate and multivariate Cox analysis were conducted on the training dataset to develop clinical and imaging survival prediction models. The DLSPCXR was trained and the selected clinical parameters were incorporated into DLSPCXR to establish a new model called DLSPinteg. Discrimination performance was evaluated using the time-dependent area under the receiver operating characteristic curves (TD AUC) at 1, 3, and 5-years survival. Delong's test was employed for the comparison of differences between two AUCs of different models. The risk-discrimination capability of the optimal model was evaluated by the Kaplan-Meier curve. In multivariable Cox analysis, older age, higher N-terminal pro-B-type natriuretic peptide (NT-ProBNP), systolic pulmonary artery pressure (sPAP) > 50 mmHg, New York Heart Association (NYHA) functional class III-IV and cardiothoracic ratio (CTR) ≥ 0.62 in CXR were independent predictors of poor prognosis in patients with HF. Based on the receiver operating characteristic (ROC) curve analysis, DLSPCXR had better performance at predicting 5-year survival than the imaging Cox model in the validation cohort (AUC: 0.757 vs. 0.561, P = 0.01). DLSPinteg as the optimal model outperforms the clinical Cox model (AUC: 0.826 vs. 0.633, P = 0.03), imaging Cox model (AUC: 0.826 vs. 0.555, P < 0.001), and DLSPCXR (AUC: 0.826 vs. 0.767, P = 0.06). Deep learning models using chest radiographs can predict survival in patients with heart failure with acceptable accuracy.

The Association of Epicardial Adipose Tissue Volume and Atrial Fibrillation in Patients With Hypertrophic Cardiomyopathy: As Assessed by Cardiac MR.

BACKGROUND: Epicardial adipose tissue (EAT) is a metabolically active visceral fat linked to cardiovascular disease. Prior studies demonstrated the predictive value of EAT volume (EATV) in atrial fibrillation (AF) among hypertrophic obstructive cardiomyopathy patients. PURPOSE: To investigate the association between EATV and AF in hypertrophic cardiomyopathy (HCM). STUDY TYPE: Retrospective. POPULATION: Two hundred and twenty-four HCM patients (including 79 patients with AF and 145 patients without AF, 154 men) and 80 healthy controls (54 men). FIELD STRENGTH/SEQUENCE: 3.0 T scanner; balanced steady-state free precession (SSFP) cine sequence, gradient echo. ASSESSMENT: EAT thickness was assessed in the 4-chamber and basal short-axis planes. EAT volume was calculated by outlining the epicardial border and visceral pericardium layer on short-axis cine images. STATISTICAL TESTS: Shapiro-Wilk test, Student's t test or the Mann-Whitney U test, chi-square test or Fisher's exact test, Multivariate linear regression analyses, Multivariable binary logistic regression analysis. Intraclass correlation coefficient. Significance was determined at P < 0.05. RESULTS: EATV and EAT volume index (EATVI) were significantly greater in HCM patients with AF than those without AF (126.6 ± 25.9 mL vs. 90.5 ± 24.5 mL, and 73.0 ± 15.9 mL/m2 vs. 51.3 ± 13.4 mL/m2). EATVI was associated with AF in multivariable linear regression analysis among HCM patients (ß = 0.62). Multivariable logistic regression analysis revealed that compared to other indicators, the area under curve (AUC) of EATVI was 0.86 (cut-off, 53.9 mL/m2, 95% CI, 0.80-0.89), provided a better performance, with the sensitivity of 96.2% and specificity of 58.6%. The combined model exhibited superior association with AF presence compared to the clinical model (AUC 0.96 vs. 0.76) and the imaging model (AUC 0.96 vs. 0.93). DATA CONCLUSION: EATVI was associated with AF. EATVI was significantly correlated with incident AF, and provided a better performance in HCM patients compared to other indicators. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

A pretreatment multiparametric MRI-based radiomics-clinical machine learning model for predicting radiation-induced temporal lobe injury in patients with nasopharyngeal carcinoma.

BACKGROUND: To establish and validate a machine learning model using pretreatment multiparametric magnetic resonance imaging-based radiomics data with clinical data to predict radiation-induced temporal lobe injury (RTLI) in patients with nasopharyngeal carcinoma (NPC) after intensity-modulated radiotherapy (IMRT). METHODS: Data from 230 patients with NPC who received IMRT (130 with RTLI and 130 without) were randomly divided into the training (n = 161) and validation cohort (n = 69) with a ratio of 7:3. Radiomics features were extracted from pretreatment apparent diffusion coefficient (ADC) map, T2-weighted imaging (T2WI), and CE-T1-weighted imaging (CE-T1WI). T-test, spearman rank correlation, and least absolute shrinkage and selection operator (LASSO) algorithm were employed to identify significant radiomics features. Clinical features were selected with univariate and multivariate analyses. Radiomics and clinical models were constructed using multiple machine learning classifiers, and a clinical-radiomics nomogram that combined clinical with radiomics features was developed. Receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were drawn to compare and verify the predictive performances of the clinical model, radiomics model, and clinical-radiomics nomogram. RESULTS: A total of 5064 radiomics features were extracted, from which 52 radiomics features were selected to construct the radiomics signature. The AUC of the radiomics signature based on multiparametric MRI was 0.980 in the training cohort and 0.969 in the validation cohort, outperforming the radiomics signature only based on T2WI and CE-T1WI (p < 0.05), which highlighted the significance of the DWI sequence in the prediction of temporal lobe injury. The area under the curve (AUC) of the clinical model was 0.895 in the training cohort and 0.905 in the validation cohort. The nomogram, which integrated radiomics and clinical features, demonstrated an impressive AUC value of 0.984 in the validation set; however, no statistically significant difference was observed compared to the radiomics model. The calibration curve and decision curve analysis of the nomogram demonstrated excellent predictive performance and clinical feasibility. CONCLUSIONS: The clinical-radiomics nomogram, integrating clinical features with radiomics features derived from pretreatment multiparametric MRI, exhibits compelling predictive performance for RTLI in patients diagnosed with NPC.

The Relationship Between Cardiac CT-based Left Atrial Structure and Epicardial Adipose Tissue and Postablation Atrial Fibrillation Recurrence Within 2 Years.

PURPOSE: The aim of this study was to explore the association of cardiac CT-based left atrium (LA) structural and functional parameters and left atrial epicardial adipose tissue (LA-EAT) parameters with postablation atrial fibrillation (AF) recurrence within 2 years. MATERIALS AND METHODS: Contrast-enhanced cardiac CT images of 286 consecutive AF patients (median age: 65 y; 97 females) who underwent initial ablation between June 2018 and June 2020 were retrospectively analyzed. Structural and functional parameters of LA, including maximum and minimum volume and ejection fraction of LA and left atrial appendage (LAA), and LA-EAT volume, were measured. The body surface area indexed maximum and minimum volume of LA (LAVImax, LAVImin) and LAA (LAAVImax, LAAVImin), and LA-EAT volume index (LA-EATVI) were calculated. Independent predictors of AF recurrence were determined using Cox regression analysis. The clinical predictors were added to the imaging predictors to build a combined model (clinical+imaging). The predictive performance of the clinical, imaging, and combined models was assessed using the area under the receiver operating characteristics curve (AUC). RESULTS: A total of 108 (37.8%) patients recurred AF within 2 years after ablation at a median follow-up of 24 months (IQR=11, 32). LA and LAA size and LA-EAT volume were significantly increased in patients with AF recurrence (P<0.05). After the multivariable regression analysis, LA-EATVI, LAAVImax, female sex, AF duration, and stroke history were independent predictors for AF recurrence. The combined model exhibited superior predictive performance compare to the clinical model (AUC=0.712 vs. 0.641, P=0.023) and the imaging model (AUC=0.712 vs. 0.663, P=0.018). CONCLUSION: Cardiac CT-based LA-EATVI and LAAVImax are independent predictors for postablation AF recurrence within 2 years and may provide a complementary value for AF recurrence risk assessment.

Radiomics Analysis of Pericoronary Adipose Tissue From Baseline Coronary Computed Tomography Angiography Enables Prediction of Coronary Plaque Progression.

PURPOSE: The relationship between plaque progression and pericoronary adipose tissue (PCAT) radiomics has not been comprehensively evaluated. We aim to predict plaque progression with PCAT radiomics features and evaluate their incremental value over quantitative plaque characteristics. PATIENTS AND METHODS: Between January 2009 and December 2020, 500 patients with suspected or known coronary artery disease who underwent serial coronary computed tomography angiography (CCTA) ≥2 years apart were retrospectively analyzed and randomly stratified into a training and testing data set with a ratio of 7:3. Plaque progression was defined with annual change in plaque burden exceeding the median value in the entire cohort. Quantitative plaque characteristics and PCAT radiomics features were extracted from baseline CCTA. Then we built 3 models including quantitative plaque characteristics (model 1), PCAT radiomics features (model 2), and the combined model (model 3) to compare the prediction performance evaluated by area under the curve. RESULTS: The quantitative plaque characteristics of the training set showed the values of noncalcified plaque volume (NCPV), fibrous plaque volume, lesion length, and PCAT attenuation were larger in the plaque progression group than in the nonprogression group ( P < 0.05 for all). In multivariable logistic analysis, NCPV and PCAT attenuation were independent predictors of coronary plaque progression. PCAT radiomics exhibited significantly superior prediction over quantitative plaque characteristics both in the training (area under the curve: 0.814 vs 0.615, P < 0.001) and testing (0.736 vs 0.594, P = 0.007) data sets. CONCLUSIONS: NCPV and PCAT attenuation were independent predictors of coronary plaque progression. PCAT radiomics derived from baseline CCTA achieved significantly better prediction than quantitative plaque characteristics.

Fragmented QRS complex could predict all-cause mortality in patients with connective tissue disease-associated pulmonary arterial hypertension.

OBJECTIVES: To investigate the prognostic impact and pathophysiological characteristics of fragmented QRS complex (fQRS) on patients with connective tissue disease-associated pulmonary arterial hypertension (CTD-PAH). METHODS: This was a multicentre retrospective study recruiting 141 patients with CTD-PAH diagnosed by right heart catheterization (114 cases in the discovery cohort and 27 cases in the validation cohort). fQRS and ST-T change were detected on conventional 12-lead electrocardiogram (ECG). Patients were followed up every 3 months to update their status and the primary end point was all-cause death. Clinical information and ECG characteristics were compared between survival and death groups and Kaplan-Meier curve was used for survival analysis. RESULTS: There were significant differences in age, gender, 6-min walk distance, NT-proBNP, WHO class, presence of fQRS and presence of ST-T change in inferior leads between survival group and death group. Inferior fQRS and ST-T change were significantly associated with right ventricular (RV) dilatation and reduced RV ejection fraction (RVEF). Kaplan-Meier curve showed that all-cause mortality was higher in CTD-PAH with fQRS (p= 0.003) and inferior ST-T change (p= 0.012). Low- and intermediate-risk CTD-PAH with inferior ST-T change had higher all-cause mortality (p= 0.005). The prognostic value of fQRS and inferior ST-T change was validated in external validation cohort. CONCLUSION: The presence of inferior fQRS and ST-T change could predict poor prognosis in CTD-PAH. CLINICAL TRIAL REGISTRATION NUMBER: NCT05980728, https://clinicaltrials.gov.

Value of dual-layer spectral detector CT in predicting lymph node metastasis of non-small cell lung cancer.

Background: The accurate assessment of lymph node metastasis (LNM) is crucial for the staging, treatment, and prognosis of lung cancer. In this study, we explored the potential value of dual-layer spectral detector computed tomography (SDCT) quantitative parameters in the prediction of LNM in non-small cell lung cancer (NSCLC). Methods: In total, 91 patients presenting with solid solitary pulmonary nodules (8 mm < diameter ≤30 mm) with pathologically confirmed NSCLC (57 without LNM, and 34 with LNM) were enrolled in the study. The patients' basic clinical data and the SDCT morphological features were analyzed using the chi-square test or Fisher's exact test. The Mann-Whitney U-test and independent sample t-test were used to analyze the differences in multiple SDCT quantitative parameters between the non-LNM and LNM groups. The diagnostic efficacy of the corresponding parameters in predicting LNM in NSCLC was evaluated by plotting the receiver operating characteristic (ROC) curves. A multivariate logistic regression analysis was conducted to determine the independent predictive factors of LNM in NSCLC. Interobserver agreement was assessed using intraclass correlation coefficients (ICCs) and Bland-Altman plots. Results: There were no significant differences between the non-LNM and LNM groups in terms of age, sex, and smoking history. Lesion size and vascular convergence sign differed significantly between the two groups (P<0.05), but there were no significant differences in the six tumor markers. The SDCT quantitative parameters [SAR40keV, SAR70keV, Δ40keV, Δ70keV, CER40keV, CER70keV, NEF40keV, NEF70keV, λ, normalized iodine concentration (NIC) and NZeff] were significantly higher in the non-LNM group than the LNM group (P<0.05). The ROC analysis showed that CER40keV, NIC, and CER70keV had higher diagnostic efficacy than other quantitative parameters in predicting LNM [areas under the curve (AUCs) =0.794, 0.791, and 0.783, respectively]. The multivariate logistic regression analysis showed that size, λ, and NIC were independent predictive factors of LNM. The combination of size, λ, and NIC had the highest diagnostic efficacy (AUC =0.892). The interobserver repeatability of the SDCT quantitative and derived quantitative parameters in the study was good (ICC: 0.801-0.935). Conclusions: The SDCT quantitative parameters combined with the clinical data have potential value in predicting LNM in NSCLC. The size + λ + NIC combined parameter model could further improve the prediction efficacy of LNM.

A comparative study of synthetic and venous hematocrit for calculating cardiovascular magnetic resonance-derived extracellular volume.

The extracellular volume (ECV) fraction derived from cardiac magnetic resonance (CMR) can reflect various pathologies. The application of ECVs was limited by the strict requirement that hematocrit (Hct0) should be obtained within 24 hours of CMR scan. The aim of this study was to obtain accurate and convenient ECV calculated from the venous Hct and synthetic Hct in CMR. A total of 839 subjects were retrospectively enrolled. The subjects were divided into derivation cohort for local sex-specific models and validation cohort for assessing the accuracy of different ECVs. In the validation cohort, venous Hcts from 7 days before the scan (Hct1 - 7), outside 7 days (Hct> 7), the closest day (Hctclosest), and Hctsyn were compared with Hct0. The agreement and correlation of the conventional ECV (ECV0) with the corresponding ECVs were analyzed. The factors affecting the accuracy of ECVsyn were assessed. ECV1-7 and ECVclosest had the best correlation and smallest bias with ECV0 (R = 0.959 and 0.951, bias = 0.02% and - 0.03%). When using an absolute 2% error as the standard, the performance of ECV1-7 was the best, with an accuracy of 81.0%, followed by ECVclosest (78.8%), ECV> 7 (77.2%) and ECVsyn (70.7%). Abnormally low and high Hcts and decreased left ventricular ejection fractions were associated with miscalculation of ECVsyn, especially patients with dilated cardiomyopathy. We recommend extending the time interval between a Hct and a CMR scan to 7 days for ECV calculation. The synthetic ECV should be used cautiously, especially for patients with extremely low or high Hcts, decreased cardiac function, and dilated cardiomyopathy.

Correlation of ventricle epicardial fat volume and triglyceride-glucose index in patients with chronic heart failure.

To explore the association of ventricle epicardial fat volume (EFV) calculated by cardiac magnetic resonance (CMR) and the insulin resistance indicator of triglyceride-glucose (TyG) index in patients with chronic HF (CHF), this retrospective cohort study included adult CHF patients with confirmed diagnosis of heart failure from January 2018 to December 2020. All patients underwent 3.0T CMR, and EFV were measured under short-axis cine. Spearman correlation, multivariate linear regression, and restricted cubic spline (RCS) regression were used to analyze their association. There were 516 patients with CHF, of whom 69.8% were male. Median EFV was 57.14mL and mean TyG index was 8.48. Spearman correlation analysis showed that TyG index was significantly correlated with the EFV in CHF patients (r = 0.247, P < 0.001). Further analysis showed that TyG index levels were significantly associated with EFV as both continuous variables (Unstandardized ß = 6.556, P < 0.001) and across the increasing quartiles (ß = 7.50, 95% CI [1.41, 13.59], P < 0.05). RCS demonstrated there were a positive trend and linear association between EFV and TyG index in CHF patients (P for nonliearity = 0.941). In patients with CHF, the TyG index was positively and linearly associated with the EFV, which supports the metabolic roles of epicardial adipose tissue regarding insulin resistance.

Primary left atrium osteosarcoma with extensive ossification: A case report.

Primary cardiac osteosarcoma is an extremely rare malignancy of the heart. Few studies have described the radiographic characteristics of primary cardiac osteosarcoma. This case report mainly demonstrates the computed tomography and magnetic resonance characteristics of the primary cardiac osteosarcoma in the left atrium.

Predicting coronary plaque progression with conventional plaque parameters and radiomics features derived from coronary CT angiography.

OBJECTIVES: To determine the value of combining conventional plaque parameters and radiomics features derived from coronary computed tomography angiography (CCTA) for predicting coronary plaque progression. MATERIALS AND METHODS: Clinical data and CCTA images of 400 patients who underwent at least two CCTA examinations between January 2009 and August 2020 were analyzed retrospectively. Diameter stenosis, total plaque volume and burden, calcified plaque volume and burden, noncalcified plaque volume and burden (NCPB), pericoronary fat attenuation index (FAI), and other conventional plaque parameters were recorded. The patients were assigned to a training cohort (n = 280) and a validation cohort (n = 120) in a 7:3 ratio using a stratified random splitting method. The area under the receiver operating characteristics curve (AUC) was used to evaluate the predictive abilities of conventional parameters (model 1), radiomics features (model 2), and their combination (model 3). RESULTS: FAI and NCPB were identified as independent risk factors for coronary plaque progression in the training cohort. Both model 2 (training cohort AUC: 0.814, p < 0.001; validation cohort AUC: 0.729, p = 0.288) and model 3 (training cohort AUC: 0.824, p < 0.001; validation cohort AUC: 0.758, p = 0.042) had better diagnostic performances in predicting plaque progression than model 1 (training cohort AUC: 0.646; validation cohort AUC: 0.654). Moreover, model 3 was slightly higher than model 2, although not statistically significant. CONCLUSIONS: The combination of conventional coronary plaque parameters and CCTA-derived radiomics features had a better ability to predict plaque progression than conventional parameters alone. CLINICAL RELEVANCE STATEMENT: The conventional coronary plaque characteristics such as noncalcified plaque burden, pericoronary fat attenuation index, and radiomics features derived from CCTA can identify plaques prone to progression, which is helpful for further clinical decision-making of coronary artery disease. KEY POINTS: • FAI and NCPB were identified as independent risk factors for predicting plaque progression. • Coronary plaque radiomics features were more advantageous than conventional parameters in predicting plaque progression. • The combination of conventional coronary plaque parameters and radiomics features could significantly improve the predictive ability of plaque progression over conventional parameters alone.

Prognostic value of ventricle epicardial fat volume by cardiovascular magnetic resonance in chronic heart failure.

The purpose of this study is to explore the prognostic values of ventricle epicardial fat volume (EFV) calculated by cardiac magnetic resonance in patients with chronic heart failure (CHF). A total of 516 patients with CHF (left ventricular ejection fraction ≤ 50%) were recruited, and 136 (26.4%) of whom experienced major adverse cardiovascular events (MACE) within median follow-up of 24 months. The target marker-EFV was found to be associated with MACE in both univariate and multivariable analysis adjusted for various clinical variables (p < 0.01), regardless as a continuous variable and categorized by X-tile program. EFV also showed promising predictive ability, with an area under the curve of 0.612, 0.618, and 0.687 for the prediction of 1-year, 2-year, and 3-year MACE, respectively. In conclusion, EFV could be a useful prognostic marker for CHF patients, helping to identify individuals at greater risk of MACE.

Identification of key biomarkers associated with immune cells infiltration for myocardial injury in dermatomyositis by integrated bioinformatics analysis.

BACKGROUND: Dermatomyositis (DM) is an acquired autoimmune disease that can cause damage to various organs, including the heart muscle. However, the mechanisms underlying myocardial injury in DM are not yet fully understood. METHODS: In this study, we utilized publicly available datasets from the Gene Expression Omnibus (GEO) database to identify hub-genes that are enriched in the immune system process in DM and myocarditis. Weighted gene co-expression network analysis (WGCNA), differentially expressed genes (DEGs) analysis, protein-protein interaction (PPI), and gene ontology (GO) analysis were employed to identify these hub-genes. We then used the CIBERSORT method to analyze immune cell infiltration in skeletal muscle specimens of DM and myocardium specimens of myocarditis respectively. Correlation analysis was performed to investigate the relationship between key genes and infiltrating immune cells. Finally, we predicted regulatory miRNAs of hub-genes through miRNet and validated their expression in online datasets and clinical samples. RESULTS: Using integrated bioinformatics analysis, we identified 10 and 5 hub-genes that were enriched in the immune system process in the database of DM and myocarditis respectively. The subsequent intersections between hub-genes were IFIT3, OAS3, ISG15, and RSAD2. We found M2 macrophages increased in DM and myocarditis compared to the healthy control, associating with the expression of IFIT3, OAS3, ISG15, and RSAD2 in DM and myocarditis positively. Gene function enrichment analysis (GSEA) showed that IFIT3, OAS3, ISG15, and RSAD2 were mainly enriched in type I interferon (IFN) signaling pathway, cellular response to type I interferon, and response to type I interferon. Finally, we verified that the expression of miR-146a-5p was significantly higher in the DM with myocardial injury than those without myocardial injury (p = 0.0009). CONCLUSION: Our findings suggest that IFIT3, OAS3, ISG15, and RSAD2 may play crucial roles in the underlying mechanism of myocardial injury in DM. Serum miR-146a-5p could be a potential biomarker for myocardial injury in DM.

Multi-Task Learning for Pulmonary Arterial Hypertension Prognosis Prediction Via Memory Drift and Prior Prompt Learning on 3D Chest CT.

Pulmonary arterial hypertension (PAH) prognosis prediction on 3D non-contrast CT images is one of the most important tasks for PAH treatment. It will help clinicians stratify patients into different groups for early diagnosis and timely intervention via automatically extracting the potential biomarkers of PAH to predict mortality. However, it is still a task of great challenges due to the large volume and low-contrast regions of interest in 3D chest CT images. In this paper, we propose the first multi-task learning-based PAH prognosis prediction framework, P 2-Net, which effectively optimizes the model and powerfully represents task-dependent features via our Memory Drift (MD) and Prior Prompt Learning (PPL) strategies. 1) Our MD maintains a large memory bank to provide a dense sampling of the deep biomarkers' distribution. Therefore, although the batch size is very small caused by our large volume, a reliable (negative log partial) likelihood loss is still able to be calculated on a representative probability distribution for robust optimization. 2) Our PPL simultaneously learns an additional manual biomarkers prediction task to embed clinical prior knowledge into our deep prognosis prediction task in hidden and explicit ways. Therefore, it will prompt the prediction of deep biomarkers and improve the perception of task-dependent features in our low-contrast regions. Our P 2-Net achieves a high prognostic correlation of the prediction and great generalization with the highest 70.19% C-index and 2.14 HR. Extensive experiments with promising results on our PAH prognosis prediction reveal powerful prognosis performance and great clinical significance in PAH treatment. All of our code will be made publicly available online Opened source: https://github.com/YutingHe-list/P2-Net.

Early cardiac involvement detected by cardiac magnetic resonance feature tracking in idiopathic inflammatory myopathy with preserved ejection fraction.

Cardiac involvement is common in idiopathic inflammatory myopathy (IIM) but often subclinical. Cardiac magnetic resonance (CMR) is a promising tool in detecting cardiac involvement in patients with IIM. The aim of this study was to assess cardiac involvement in IIM patients by CMR feature tracking (CMR-FT). Thirty-seven IIM patients and 25 controls were enrolled in this retrospective study. The left ventricular (LV) functional parameters such as volume and ejection fraction were measured. Global and regional LV peak strain (PS) in radial, circumferential and longitudinal directions were derived from cine images. Left atrial (LA) volume, longitudinal strain and strain rate (SR) parameters and LA reservoir function, conduit function and booster pump function were assessed, respectively. IIM patients with preserved LVEF showed significantly reduced global and regional LV PS in longitudinal direction (all p < 0.05). Compared with controls, LA reservoir and conduit function were significantly impaired in IIM patients (all p < 0.05). The global LV longitudinal PS, LAVpre-ac and SRe were independent predictors of IIM. By Pearson's correlation analysis, the LV global radial, circumferential and longitudinal PS were all correlated to LVEF in IIM patients (r = 0.526, p < 0.001 vs. r = - 0.514, p < 0.001 vs. r = - 0.288, p = 0.023). CMR-FT based LV and LA deformation performance could early detect cardiac involvement in IIM patients with preserved LVEF.

Myocardial extracellular volume quantified by cardiac magnetic resonance predicts left ventricular aneurysm following acute myocardial infarction.

OBJECTIVE: This study aimed to investigate the correlation between increased extracellular matrix estimated by cardiac magnetic resonance (CMR) and left ventricular aneurysm after acute myocardial infarction. METHODS: A total of 175 patients from 3 centers with an isolated left anterior descending culprit vessel underwent CMR examinations within 1 week and at a 6-month follow-up. Of these, 92 were identified to have left ventricular aneurysms (LVAs): 74 with functional aneurysm and 18 with anatomical aneurysm. The predictive significance of acute extracellular volume (ECV), left gadolinium enhancement (LGE), and other characteristics were analyzed using binary logistic regression analysis. RESULTS: Patients with LVA were more likely to present with left ventricular adverse remodeling (LVAR) than those without (p = 0.009). With optimal cutoff values of 30.90% for LGE and 33% for ECV to discriminate LVA from non-LVA, the area under the curve (AUC) by receiver operator characteristic curve (ROC) analysis was 0.92 (95% CI: 0.87-0.96; p < 0.001) and 0.93 (95% CI: 0.88-0.96; p < 0.001), respectively. ECV was significantly better than LGE at discriminating between functional and anatomical LVA (p < 0.001). Both acute LGE and ECV were predictors of LVA, with an odds ratio of 1.35 (95% CI: 1.21-1.52, p < 0.001) and 1.23 (95% CI: 1.13-1.33, p < 0.001), respectively, by multivariable logistic regression analysis. CONCLUSIONS: Acute LGE and ECV of the myocardium provided predictive significance for LVA. The discriminative significance of ECV for functional versus anatomical LVA was better than the discriminative significance of LGE. KEY POINTS: • Patients with LVA were more likely to present with LVAR. • Acute LGE and ECV of the myocardium provided the strongest predictive significance for LVA. • The discriminative significance of ECV for functional versus anatomical LVA was better than that of LGE.

The diagnostic value of serum YKL-40 for myocardial involvement in idiopathic inflammatory myopathy.

OBJECTIVE: To investigate the diagnostic value of serum chitinase-3-like-1 protein (YKL-40) levels for myocardial involvement in idiopathic inflammatory myopathies (IIM). METHODS: A total of 74 patients with definite IIM who visited Jiangsu Province People's Hospital between May 2018 and January 2022 were enrolled in this retrospective study. Baseline clinical evaluation, laboratory index, electrocardiogram (ECG), echocardiography (ECHO) and cardiac magnetic resonance (CMR) parameters were collected. Serum YKL-40 of all participants was determined by ELISA. Receiver-operating characteristic (ROC) curve was used to assess the diagnostic value of YKL-40 in assessing myocardial involvement in IIM patients. RESULTS: 1. YKL-40 concentration was significantly higher in IIM patients with myocardial injury than without myocardial injury.2. Multivariate logistic regression analysis demonstrated that serum YKL-40 was an independent risk factor for myocardial involvement in IIM.3. YKL-40 > 66.4 ng/ml (AUC = 0.85, 95 % CI 0.75-0.95) predicted myocardial injury in IIM with a sensitivity of 0.75 and specificity of 0.95. CONCLUSION: Serum YKL-40 could serve as a potential biomarker to predict myocardial injury in IIM patients.

Multimodality imaging toward diagnosis of an intraventricular bronchogenic cyst presenting with complex components: A case report with literature review.

Cardiac bronchogenic cysts are rare lesions with very thin walls. We present a case of a 49-year-old man with intermittent chest pain and distress who was found to have a bronchogenic cyst in the right ventricle with complex tissue changes. The clinical manifestations, image findings, and differential diagnoses of bronchogenic cysts are discussed.

CCTA-derived strain analysis in detection of regional myocardial dysfunction in coronary artery disease patients with preserved left ventricular ejection fraction: A feasibility study.

OBJECTIVES: To evaluate the feasibility of using coronary computed tomography angiography (CCTA)-derived strain to detect regional myocardial dysfunction in coronary artery disease (CAD) patients with normal left ventricular ejection fraction (LVEF). METHODS: A total of 1,580 segments from 101 patients who underwent stressed CT myocardial perfusion imaging (CT-MPI) and CCTA were retrospectively enrolled in this study. The CT-derived global and segmental strain values were evaluated using the feature tracking technique. Segments with myocardial blood flow (MBF) < 125 ml/min/100 ml and 95 ml/min/100 ml were categorized as ischemic and infarcted, respectively. RESULTS: Segmental radial strain (SRS) and segmental circumferential strain (SCS) in the abnormal segments (including all segments with MBF < 125 ml/min/100 ml) were significantly lower than those in the normal segments (14.81±8.65% vs 17.17±9.13%, p < 0.001; -10.21±5.79% vs -11.86±4.52%, p < 0.001, respectively). SRS and SCS values in infarcted segments were significantly impaired compared with the ischemic segments (12.43±8.03% vs. 15.32±8.71%, p = 0.038; -7.72±5.91% vs. -10.67±5.66%, p = 0.010, respectively). The AUCs for SRS and SCS in detecting infarcted segments were 0.622 and 0.698, respectively (p < 0.05). CONCLUSIONS: It is feasible for using CCTA-derived strain parameters to detect regional myocardial dysfunction in CAD patients with preserved LVEF. Segmental radial and circumferential strain have the potential ability to distinguish myocardial ischemia from infarction, and normal from ischemic myocardium.

Occurrence and morphology of ventricular arrhythmias in apparently normal hearts in relation to late gadolinium enhancement on cardiovascular magnetic resonance.

Cardiac magnetic resonance (CMR) is the gold standard for evaluating myocardial fibrosis. Few studies have explored the association between ventricular arrhythmias (VAs) and fibrosis in apparently normal hearts. We aimed to investigate the association between the occurrence and morphology of VAs and left ventricular late gadolinium enhancement (LV-LGE) in patients without known structural heart diseases. This study enrolled 78 patients with apparently normal hearts who underwent 24-h ambulatory Holter electrocardiogram (ECG) and CMR examinations simultaneously. The presence and extent of LGE was determined using CMR imaging and compared based on occurrence and morphology of VAs. The clinical characteristics were also recorded and calculated. LV-LGE was observed in 19 (37.3%) and 4 (14.8%) patients with and without VAs, respectively (P = 0.039). It was more frequently observed in patients with polymorphic VAs (P = 0.024). The polymorphic VAs had a higher tendency of LGE extent than monomorphic VAs, while the difference did not reach statistical significance (P = 0.055). In multivariable analyses, the presence of polymorphic VAs [hazard ratio (HR) 11.19, 95% CI 1.64-76.53, P = 0.014] and hypertension (HR 4.64, 95% CI 1.08-19.99, P = 0.039) were associated with greater prevalence of LV-LGE. In patients without structural heart diseases, besides hypertension, multiple VA morphologies on Holter ambulatory ECG measurements is another important marker of increased incidence of myocardial fibrosis.