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A robust radiomic-based machine learning approach to detect cardiac amyloidosis using cardiac computed tomography. Frontiers in radiology Introduction:Cardiac amyloidosis (CA) shares similar clinical and imaging characteristics (e.g., hypertrophic phenotype) with aortic stenosis (AS), but its prognosis is generally worse than severe AS alone. Recent studies suggest that the presence of CA is frequent (1 out of 8 patients) in patients with severe AS. The coexistence of the two diseases complicates the prognosis and therapeutic management of both conditions. Thus, there is an urgent need to standardize and optimize the diagnostic process of CA and AS. The aim of this study is to develop a robust and reliable radiomics-based pipeline to differentiate the two pathologies. Methods:Thirty patients were included in the study, equally divided between CA and AS. For each patient, a cardiac computed tomography (CCT) was analyzed by extracting 107 radiomics features from the LV wall. Feature robustness was evaluated by means of geometrical transformations to the ROIs and intra-class correlation coefficient (ICC) computation. Various correlation thresholds (0.80, 0.85, 0.90, 0.95, 1), feature selection methods [-value, least absolute shrinkage and selection operator (LASSO), semi-supervised LASSO, principal component analysis (PCA), semi-supervised PCA, sequential forwards selection] and machine learning classifiers (k-nearest neighbors, support vector machine, decision tree, logistic regression and gradient boosting) were assessed using a leave-one-out cross-validation. Data augmentation was performed using the synthetic minority oversampling technique. Finally, explainability analysis was performed by using the SHapley Additive exPlanations (SHAP) method. Results:Ninety-two radiomic features were selected as robust and used in the further steps. Best performances of classification were obtained using a correlation threshold of 0.95, PCA (keeping 95% of the variance, corresponding to 9 PCs) and support vector machine classifier reaching an accuracy, sensitivity and specificity of 0.93. Four PCs were found to be mainly dependent on textural features, two on first-order statistics and three on shape and size features. Conclusion:These preliminary results show that radiomics might be used as non-invasive tool able to differentiate CA from AS using clinical routine available images. 10.3389/fradi.2023.1193046
Machine learning and radiomics for ventricular tachyarrhythmia prediction in hypertrophic cardiomyopathy: insights from an MRI-based analysis. Acta radiologica (Stockholm, Sweden : 1987) BACKGROUND:Myocardial fibrosis is often detected in patients with hypertrophic cardiomyopathy (HCM), which causes left ventricular (LV) dysfunction and tachyarrhythmias. PURPOSE:To evaluate the potential value of a machine learning (ML) approach that uses radiomic features from late gadolinium enhancement (LGE) and cine images for the prediction of ventricular tachyarrhythmia (VT) in patients with HCM. MATERIAL AND METHODS:Hyperenhancing areas of LV myocardium on LGE images were manually segmented, and the segmentation was propagated to corresponding areas on cine images. Radiomic features were extracted using the PyRadiomics library. The least absolute shrinkage and selection operator (LASSO) method was employed for radiomic feature selection. Our model development employed the TabPFN algorithm, an adapted Prior-Data Fitted Network design. Model performance was evaluated graphically and numerically over five-repeat fivefold cross-validation. SHapley Additive exPlanations (SHAP) were employed to determine the relative importance of selected radiomic features. RESULTS:Our cohort consisted of 60 patients with HCM (73.3% male; median age = 51.5 years), among whom 17 had documented VT during the follow-up. A total of 1612 radiomic features were extracted for each patient. The LASSO algorithm led to a final selection of 18 radiomic features. The model achieved a mean area under the receiver operating characteristic curve of 0.877, demonstrating good discrimination, and a mean Brier score of 0.119, demonstrating good calibration. CONCLUSION:Radiomics-based ML models are promising for predicting VT in patients with HCM during the follow-up period. Developing predictive models as clinically useful decision-making tools may significantly improve risk assessment and prognosis. 10.1177/02841851241283041
An Explainable Machine Learning Approach Reveals Prognostic Significance of Right Ventricular Dysfunction in Nonischemic Cardiomyopathy. JACC. Cardiovascular imaging OBJECTIVES:The authors implemented an explainable machine learning (ML) model to gain insight into the association between cardiac magnetic resonance markers and adverse outcomes of cardiovascular hospitalization and all-cause death (composite endpoint) in patients with nonischemic dilated cardiomyopathy (NICM). BACKGROUND:Risk stratification of patients with NICM remains challenging. An explainable ML model has the potential to provide insight into the contributions of different risk markers in the prediction model. METHODS:An explainable ML model based on extreme gradient boosting (XGBoost) machines was developed using cardiac magnetic resonance and clinical parameters. The study cohorts consist of patients with NICM from 2 academic medical centers: Beth Israel Deaconess Medical Center (BIDMC) and Brigham and Women's Hospital (BWH), with 328 and 214 patients, respectively. XGBoost was trained on 70% of patients from the BIDMC cohort and evaluated based on the other 30% as internal validation. The model was externally validated using the BWH cohort. To investigate the contribution of different features in our risk prediction model, we used Shapley additive explanations (SHAP) analysis. RESULTS:During a mean follow-up duration of 40 months, 34 patients from BIDMC and 33 patients from BWH experienced the composite endpoint. The area under the curve for predicting the composite endpoint was 0.71 for the internal BIDMC validation and 0.69 for the BWH cohort. SHAP analysis identified parameters associated with right ventricular (RV) dysfunction and remodeling as primary markers of adverse outcomes. High risk thresholds were identified by SHAP analysis and thus provided thresholds for top predictive continuous clinical variables. CONCLUSIONS:An explainable ML-based risk prediction model has the potential to identify patients with NICM at risk for cardiovascular hospitalization and all-cause death. RV ejection fraction, end-systolic and end-diastolic volumes (as indicators of RV dysfunction and remodeling) were determined to be major risk markers. 10.1016/j.jcmg.2021.11.029
Development and Validation of an Explainable Radiomics Model to Predict High-Aggressive Prostate Cancer: A Multicenter Radiomics Study Based on Biparametric MRI. Cancers In the last years, several studies demonstrated that low-aggressive (Grade Group (GG) ≤ 2) and high-aggressive (GG ≥ 3) prostate cancers (PCas) have different prognoses and mortality. Therefore, the aim of this study was to develop and externally validate a radiomic model to noninvasively classify low-aggressive and high-aggressive PCas based on biparametric magnetic resonance imaging (bpMRI). To this end, 283 patients were retrospectively enrolled from four centers. Features were extracted from apparent diffusion coefficient (ADC) maps and T2-weighted (T2w) sequences. A cross-validation (CV) strategy was adopted to assess the robustness of several classifiers using two out of the four centers. Then, the best classifier was externally validated using the other two centers. An explanation for the final radiomics signature was provided through Shapley additive explanation (SHAP) values and partial dependence plots (PDP). The best combination was a naïve Bayes classifier trained with ten features that reached promising results, i.e., an area under the receiver operating characteristic (ROC) curve (AUC) of 0.75 and 0.73 in the construction and external validation set, respectively. The findings of our work suggest that our radiomics model could help distinguish between low- and high-aggressive PCa. This noninvasive approach, if further validated and integrated into a clinical decision support system able to automatically detect PCa, could help clinicians managing men with suspicion of PCa. 10.3390/cancers16010203
Breast cancer molecular subtype prediction: Improving interpretability of complex machine-learning models based on multiparametric-MRI features using SHapley Additive exPlanations (SHAP) methodology. Diagnostic and interventional imaging 10.1016/j.diii.2024.01.008
Predicting the conversion from clinically isolated syndrome to multiple sclerosis: An explainable machine learning approach. Multiple sclerosis and related disorders INTRODUCTION:Predicting the conversion of clinically isolated syndrome (CIS) to clinically definite multiple sclerosis (CDMS) is critical to personalizing treatment planning and benefits for patients. The aim of this study is to develop an explainable machine learning (ML) model for predicting this conversion based on demographic, clinical, and imaging data. METHOD:The ML model, Extreme Gradient Boosting (XGBoost), was employed on the public dataset of 273 Mexican mestizo CIS patients with 10-year follow-up. The data was divided into a training set for cross-validation and feature selection, and a holdout test set for final testing. Feature importance was determined using the SHapley Additive Explanations library (SHAP). Then, two experiments were conducted to optimize the model's performance by selectively adding variables and selecting the most contributive variables for the final model. RESULTS:Nine variables including age, gender, schooling, motor symptoms, infratentorial and periventricular lesion at imaging, oligoclonal band in cerebrospinal fluid, lesion and symptoms types were significant. The model achieved an accuracy of 83.6 %, AUC of 91.8 %, sensitivity of 83.9 %, and specificity of 83.4 % in cross-validation. In the final testing, the model achieved an accuracy of 78.3 %, AUC of 85.8 %, sensitivity of 75 %, and specificity of 81.1 %. Finally, a web-based demo of the model was created for testing purposes. CONCLUSION:The model, focusing on feature selection and interpretability, effectively stratifies risk for treatment decisions and disability prevention in MS patients. It provides a numerical risk estimate for CDMS conversion, enhancing transparency in clinical decision-making and aiding in patient care. 10.1016/j.msard.2024.105614
Predicting axillary lymph node metastasis in breast cancer patients: A radiomics-based multicenter approach with interpretability analysis. European journal of radiology PURPOSE:To develop a MRI-based radiomics model, integrating the intratumoral and peritumoral imaging information to predict axillary lymph node metastasis (ALNM) in patients with breast cancer and to elucidate the model's decision-making process via interpretable algorithms. METHODS:This study included 376 patients from three institutions who underwent contrast-enhanced breast MRI between 2021 and 2023. We used multiple machine learning algorithms to combine peritumoral, intratumoral, and radiological characteristics with the building of radiological, radiomics, and combined models. The model's performance was compared based on the area under the curve (AUC) obtained from the receiver operating characteristic analysis and interpretable machine learning techniques to analyze the operating mechanism of the model. RESULTS:The radiomics model, incorporating features from both intratumoral tissue and the 3 mm peritumoral region and utilizing the backpropagation neural network (BPNN) algorithm, demonstrated superior diagnostic efficacy, achieving an AUC of 0.820. The AUC of the combination of the RAD score, clinical T stage, and spiculated margin was as high as 0.855. Furthermore, we conducted SHapley Additive exPlanations (SHAP) analysis to evaluate the contributions of RAD score, clinical T stage, and spiculated margin in ALNM status prediction. CONCLUSIONS:The interpretable radiomics model we propose can better predict the ALNM status of breast cancer and help inform clinical treatment decisions. 10.1016/j.ejrad.2024.111522
Multiparametric MRI model to predict molecular subtypes of breast cancer using Shapley additive explanations interpretability analysis. Diagnostic and interventional imaging PURPOSE:The purpose of this study was to assess the predictive performance of multiparametric magnetic resonance imaging (MRI) for molecular subtypes and interpret features using SHapley Additive exPlanations (SHAP) analysis. MATERIAL AND METHODS:Patients with breast cancer who underwent pre-treatment MRI (including ultrafast dynamic contrast-enhanced MRI, magnetic resonance spectroscopy, diffusion kurtosis imaging and intravoxel incoherent motion) were recruited between February 2019 and January 2022. Thirteen semantic and thirteen multiparametric features were collected and the key features were selected to develop machine-learning models for predicting molecular subtypes of breast cancers (luminal A, luminal B, triple-negative and HER2-enriched) by using stepwise logistic regression. Semantic model and multiparametric model were built and compared based on five machine-learning classifiers. Model decision-making was interpreted using SHAP analysis. RESULTS:A total of 188 women (mean age, 53 ± 11 [standard deviation] years; age range: 25-75 years) were enrolled and further divided into training cohort (131 women) and validation cohort (57 women). XGBoost demonstrated good predictive performance among five machine-learning classifiers. Within the validation cohort, the areas under the receiver operating characteristic curves (AUCs) for the semantic models ranged from 0.693 (95% confidence interval [CI]: 0.478-0.839) for HER2-enriched subtype to 0.764 (95% CI: 0.681-0.908) for luminal A subtype, inferior to multiparametric models that yielded AUCs ranging from 0.771 (95% CI: 0.630-0.888) for HER2-enriched subtype to 0.857 (95% CI: 0.717-0.957) for triple-negative subtype. The AUCs between the semantic and the multiparametric models did not show significant differences (P range: 0.217-0.640). SHAP analysis revealed that lower iAUC, higher kurtosis, lower D*, and lower kurtosis were distinctive features for luminal A, luminal B, triple-negative breast cancer, and HER2-enriched subtypes, respectively. CONCLUSION:Multiparametric MRI is superior to semantic models to effectively predict the molecular subtypes of breast cancer. 10.1016/j.diii.2024.01.004
Data-Driven Prognostication in Distal Medium Vessel Occlusions Using Explainable Machine Learning. AJNR. American journal of neuroradiology BACKGROUND AND PURPOSE:Distal medium vessel occlusions (DMVOs) are estimated to cause acute ischemic stroke (AIS) in 25-40% of cases. Prognostic models can inform patient counseling and research by enabling outcome predictions. However, models designed specifically for DMVOs are lacking. MATERIALS AND METHODS:This retrospective study developed a machine learning model to predict 90-day unfavorable outcome [defined as a modified Rankin Scale (mRS) score of 3-6] in 164 primary DMVO patients. A model developed with the TabPFN algorithm utilized selected clinical, laboratory, imaging, and treatment data with the Least Absolute Shrinkage and Selection Operator feature selection. Performance was evaluated via 5-repeat 5-fold cross-validation. Model discrimination and calibration were evaluated. SHapley Additive Explanations (SHAP) identified influential features. A web application deployed the model for individualized predictions. RESULTS:The model achieved an area under the receiver operating characteristic curve of 0.815 (95% CI: 0.79-0.841) for predicting unfavorable outcome, demonstrating good discrimination, and a Brier score of 0.19 (95% CI: 0.177-0.202), demonstrating good calibration. SHAP analysis ranked admission National Institutes of Health Stroke Scale (NIHSS) score, premorbid mRS, type of thrombectomy, modified thrombolysis in cerebral infarction score, and history of malignancy as top predictors. The web application enables individualized prognostication. CONCLUSIONS:Our machine learning model demonstrated good discrimination and calibration for predicting 90-day unfavorable outcomes in primary DMVO strokes. This study demonstrates the potential for personalized prognostic counseling and research to support precision medicine in stroke care and recovery. ABBREVIATIONS:DMVO = Distal medium vessel occlusion; AIS = acute ischemic stroke; mRS = modified Rankin Scale; SHAP = SHapley Additive Explanations; NIHSS = National Institutes of Health Stroke Scale; ST = stroke thrombectomy; TRIPOD = Transparent Reporting of Multivariable Prediction Models for Individual Prognosis or Diagnosis; CT = computed tomography; CTP = CT perfusion; MRI = magnetic resonance imaging; CTA = CT angiography; DVT = deep vein thrombosis; PE = pulmonary emboli; TIA = transient ischemic attack; BMI = body mass index; ALP = alkaline phosphatase; ALT = alanine transaminase; AST = aspartate aminotransferase; NCCT-ASPECTS = Alberta Stroke Program Early CT Score; IVT = intravenous thrombolysis; mTICI = modified thrombolysis in cerebral infarction; ER = emergency room; kNN = k-nearest neighbor; LASSO = Least Absolute Shrinkage and Selection Operator; PDPs = partial dependence plots; ROC = receiver operating characteristic; PRC = precision-recall curve; AUROC = area under the ROC curve; AUPRC = area under the PRC; CI = confidence interval. 10.3174/ajnr.A8547
Machine learning‑based radiomics models accurately predict Crohn's disease‑related anorectal cancer. Oncology letters The radiological diagnosis of Crohn's disease (CD)-related anorectal cancer is difficult; it is often found in advanced stages and has a poor prognosis because of the difficulty of curative surgery. However, there are no studies on predicting the diagnosis of CD-related cancer. The present study aimed to develop a predictive model to diagnose CD cancerous lesions more accurately in a way that can be interpreted by clinicians. Patients with CD who developed anorectal CD lesions at Hyogo Medical University (Nishinomiya, Japan) between March 2009 and June 2022 were included in the present study. T2-weighted and T1-weighted magnetic resonance (MR) images were utilized for our analysis. Images of anorectal lesions were segmented using open-source 3D Slicer software, and radiomic features were extracted using PyRadiomics. Six machine learning models were investigated and compared: i) Support vector machine; ii) naive Bayes; iii) random forest; iv) light gradient boosting machine; v) extremely randomized trees; vi) and regularized greedy forest (RGF). SHapley Additive exPlanations (SHAP) values were calculated to assess the extent to which each radiomic feature contributed to the model's predictions compared to baseline, represented as the average of the model's predictions for all test data. The T2-weighted images of 28 patients with anorectal cancer and 40 non-cancer patients were analyzed and the contrast-enhanced T1-weighted images of 22 cancer and 40 non-cancer patients. The model with the highest area under the curve (AUC) was the RGF-based model constructed using T2-weighted image features, achieving an AUC of 0.944 (accuracy, 0.862; recall, 0.830). The SHAP-based model explanation suggested a strong association between the diagnosis of CD-related anorectal cancer and features such as complex lesion texture; greater pixel separation within the same coronal cross-section; larger, randomly distributed clumps of pixels with the same signal intensity; and a more spherical lesion shape on T2-weighted images. The MRI radiomics-based RGF model demonstrated outstanding performance in predicting CD-related anorectal cancer. These results may affect the diagnosis and surveillance strategies of CD-related colorectal cancer. 10.3892/ol.2024.14553
CMR-based cardiac phenotyping in different forms of heart failure. The international journal of cardiovascular imaging Heart failure (HF) is a heterogenous disease requiring precise diagnostics and knowledge of pathophysiological processes. Since structural and functional imaging data are scarce we hypothesized that cardiac magnetic resonance (CMR)-based analyses would provide accurate characterization and mechanistic insights into different HF groups comprising preserved (HFpEF), mid-range (HFmrEF) and reduced ejection fraction (HFrEF). 22 HFpEF, 17 HFmrEF and 15 HFrEF patients as well as 19 healthy volunteers were included. CMR image assessment contained left atrial (LA) and left ventricular (LV) volumetric evaluation as well as left atrioventricular coupling index (LACI). Furthermore, CMR feature-tracking included LV and LA strain in terms of reservoir (Es), conduit (Ee) and active boosterpump (Ea) function. CMR-based tissue characterization comprised T1 mapping as well as late-gadolinium enhancement (LGE) analyses. HFpEF patients showed predominant atrial impairment (Es 20.8%vs.25.4%, p = 0.02 and Ee 8.3%vs.13.5%, p = 0.001) and increased LACI compared to healthy controls (14.5%vs.23.3%, p = 0.004). Patients with HFmrEF showed LV enlargement but mostly preserved LA function with a compensatory increase in LA boosterpump (LA Ea: 15.0%, p = 0.049). In HFrEF LA and LV functional impairment was documented (Es: 14.2%, Ee: 5.4% p < 0.001 respectively; Ea: 8.8%, p = 0.02). This was paralleled by non-invasively assessed progressive fibrosis (T1 mapping and LGE; HFrEF > HFmrEF > HFpEF). CMR-imaging reveals insights into HF phenotypes with mainly atrial affection in HFpEF, ventricular affection with atrial compensation in HFmrEF and global impairment in HFrEF paralleled by progressive LV fibrosis. These data suggest a necessity for a personalized HF management based on imaging findings for future optimized patient management. 10.1007/s10554-024-03145-4
Prevalence of right ventricular dysfunction and prognostic significance in heart failure with preserved ejection fraction. Kanagala Prathap,Arnold Jayanth R,Singh Anvesha,Khan Jamal N,Gulsin Gaurav S,Gupta Pankaj,Squire Iain B,Ng Leong L,McCann Gerry P The international journal of cardiovascular imaging There is a paucity of data characterizing right ventricular performance in heart failure with preserved ejection fraction (HFpEF) using the gold standard of cardiovascular magnetic resonance imaging (CMR). We aimed to assess the proportion of right ventricular systolic dysfunction (RVD) in HFpEF and the relation to clinical outcomes. As part of a single-centre, prospective, observational study, 183 subjects (135 HFpEF, and 48 age- and sex-matched controls) underwent extensive characterization with CMR. transthoracic echocardiography, blood sampling and six-minute walk testing. Patients were followed for the composite endpoint of death or HF hospitalization. RVD (defined as right ventricular ejection fraction < 47%) controls was present in 19% of HFpEF. Patients with RVD presented more frequently with lower systolic blood pressure, atrial fibrillation, radiographic evidence of pulmonary congestion and raised cardiothoracic ratio and larger right ventricular volumes. During median follow-up of 1429 days, 47% (n = 64) of HFpEF subjects experienced the composite endpoint of death (n = 22) or HF hospitalization (n = 42). RVD was associated with an increased risk of composite events (Log-Rank p = 0.001). In multivariable Cox regression analysis, RVD was an independent predictor of adverse outcomes (adjusted Hazard Ratio [HR] 3.946, 95% CI 1.878-8.290, p = 0.0001) along with indexed extracellular volume (HR 1.742, CI 1.176-2.579, p = 0.006) and E/E' (HR 1.745, CI 1.230-2.477, p = 0.002). RVD as assessed by CMR is prevalent in nearly one-fifth of HFpEF patients and is independently associated with death and/or hospitalization with HF.The trial was registered retrospectively on ClinicalTrials.gov (Identifier: NCT03050593). The date of registration was February 06, 2017. 10.1007/s10554-020-01953-y
Association Between Heart Failure With Preserved Left Ventricular Ejection Fraction and Impaired Left Atrial Phasic Function in Hypertrophic Cardiomyopathy: Evaluation by Cardiac MRI Feature Tracking. Journal of magnetic resonance imaging : JMRI BACKGROUND:The majority of heart failure (HF) in hypertrophic cardiomyopathy (HCM) manifests as a phenotype with preserved left ventricular (LV) ejection fraction; however, the exact contribution of left atrial (LA) phasic function to HF with preserved ejection fraction (HFpEF) in HCM remains unresolved. PURPOSE:To define the association between LA function and HFpEF in HCM patients using cardiac magnetic resonance imaging (MRI) feature tracking. STUDY TYPE:Retrospective. POPULATION:One hundred and fifty-four HCM patients (HFpEF vs. non-HF: 55 [34 females] vs. 99 [43 females]). FIELD STRENGTH/SEQUENCE:3.0 T/balanced steady-state free precession. ASSESSMENT:LA reservoir function (reservoir strain [ε ], total ejection fraction [EF]), conduit function (conduit strain [ε ], passive EF), booster-pump function (booster strain [ε ] and active EF), LA volume index, and LV global longitudinal strain (LV GLS) were evaluated in HCM patients. STATISTICAL TESTS:Chi-square test, Student's t-test, Mann-Whitney U test, multivariate linear regression, logistic regression, and net reclassification analysis were used. Two-sided P < 0.05 was considered statistically significant. RESULTS:No significant difference was found in LV GLS between the non-HF and HFpEF group (-10.67 ± 3.14% vs. -10.14 ± 4.01%, P = 0.397), whereas the HFpEF group had more severely impaired LA phasic strain (ε : 27.40 [22.60, 35.80] vs. 18.15 [11.98, 25.90]; ε : 13.80 [9.20, 18.90] vs. 7.95 [4.30, 14.35]; ε : 13.50 [9.90, 17.10] vs. 7.90 [5.40, 14.15]). LA total EF (37.91 [29.54, 47.94] vs. 47.49 [39.18, 55.01]), passive EF (14.70 [7.41, 21.49] vs. 18.07 [9.32, 24.78]), and active EF (27.19 [17.79, 36.60] vs. 36.64 [26.63, 42.71]) were all significantly decreased in HFpEF patients compared with non-HF patients. LA reservoir (β = 0.90 [0.85, 0.96]), conduit (β = 0.93 [0.87, 0.99]), and booster (β = 0.86 [0.78, 0.95]) strain were independently associated with HFpEF in HCM patients. The model including reservoir strain (Net Reclassification Index [NRI]: 0.260) or booster strain (NRI: 0.325) improved the reclassification of HFpEF based on LV GLS and minimum left atrial volume index (LAVI ). DATA CONCLUSION:LA phasic function was severely impaired in HCM patients with HFpEF, whereas LV function was not further impaired compared with non-HF patients. LEVEL OF EVIDENCE:4 TECHNICAL EFFICACY: Stage 3. 10.1002/jmri.28000
Layer-specific strain in patients with heart failure using cardiovascular magnetic resonance: not all layers are the same. Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance BACKGROUND:Global longitudinal strain (GLS), most commonly measured at the endocardium, has been shown to be superior to left ventricular (LV) ejection fraction (LVEF) for the identification of systolic dysfunction and prediction of outcomes in heart failure (HF). We hypothesized that strains measured at different myocardial layers (endocardium = ENDO, epicardium = EPI, average = AVE) will have distinct diagnostic and predictive performance for patients with HF. METHODS:Layer-specific GLS, layer-specific global circumferential strain (GCS) and global radial strain (GRS) were evaluated by cardiovascular magnetic resonance imaging (CMR) feature tracking in the Alberta HEART study. A total of 453 subjects consisted of healthy controls (controls, n = 77), at-risk for HF (at-risk, n = 143), HF with preserved ejection fraction (HFpEF, n = 87), HF with mid-range ejection fraction (HFmrEF, n = 88) and HF with reduced ejection fraction (HFrEF, n = 58). For outcomes analysis, CMR-derived imaging parameters were adjusted with a base model that included age and N-terminal prohormone of b-type natriuretic peptide (NT-proBNP) to test their independent association with 5-year all-cause mortality. RESULTS:GLS_EPI distinguished all groups with preserved LVEF (controls - 16.5 ± 2.4% vs. at-risk - 15.5 ± 2.7% vs. HFpEF - 14.1 ± 3.0%, p < 0.001) while GLS_ENDO and all GCS (all layers) were similar among these groups. GRS was reduced in HFpEF (41.1 ± 13.8% versus 48.9 ± 10.7% in controls, p < 0.001) and the difference between GLS_EPI and GLS_ENDO were significantly larger in HFpEF as compared to controls. Within the preserved LVEF groups, reduced GRS and GLS_EPI were significantly associated with increased LV mass (LVM) and LVM/LV end-diastolic volume EDV (concentricity). In multivariable analysis, only GLS_AVE and GRS predicted 5-year all-cause mortality (all ps < 0.05), with the strongest association with 5-year all-cause mortality by Akaike Information Criterion analysis and significant incremental value for outcomes prediction beyond LVEF or GLS_ENDO by the likelihood ratio test. CONCLUSION:Global strains measured on endocardium, epicardium or averaged across the wall thickness are not equivalent for the identification of systolic dysfunction or outcomes prediction in HF. The endocardium-specific strains were shown to have poorest all-around performance. GLS_AVE and GRS were the only CMR parameters to be significantly associated with 5-year all-cause mortality in multivariable analysis. GLS_EPI and GRS, as well as the difference in endocardial and epicardial strains, were sensitive to systolic dysfunction among HF patients with normal LVEF (> 55%), in whom lower strains were associated with increased concentricity. 10.1186/s12968-020-00680-6
Noncontrast Cardiac Magnetic Resonance Imaging Predictors of Heart Failure Hospitalization in Heart Failure With Preserved Ejection Fraction. Journal of magnetic resonance imaging : JMRI BACKGROUND:Heart failure patients with preserved ejection fraction (HFpEF) are at increased risk of future hospitalization. Contrast agents are often contra-indicated in HFpEF patients due to the high prevalence of concomitant kidney disease. Therefore, the prognostic value of a noncontrast cardiac magnetic resonance imaging (MRI) for HF-hospitalization is important. PURPOSE:To develop and test an explainable machine learning (ML) model to investigate incremental value of noncontrast cardiac MRI for predicting HF-hospitalization. STUDY TYPE:Retrospective. POPULATION:A total of 203 HFpEF patients (mean, 64 ± 12 years, 48% women) referred for cardiac MRI were randomly split into training validation (143 patients, ~70%) and test sets (60 patients, ~30%). FIELD STRENGTH:A 1.5 T, balanced steady-state free precession (bSSFP) sequence. ASSESSMENT:Two ML models were built based on the tree boosting technique and the eXtreme Gradient Boosting model (XGBoost): 1) basic clinical ML model using clinical and echocardiographic data and 2) cardiac MRI-based ML model that included noncontrast cardiac MRI markers in addition to the basic model. The primary end point was defined as HF-hospitalization. STATISTICAL TESTS:ML tool was used for advanced statistics, and the Elastic Net method for feature selection. Area under the receiver operating characteristic (ROC) curve (AUC) was compared between models using DeLong's test. To gain insight into the ML model, the SHapley Additive exPlanations (SHAP) method was leveraged. A P-value <0.05 was considered statistically significant. RESULTS:During follow-up (mean, 50 ± 39 months), 85 patients (42%) reached the end point. The cardiac MRI-based ML model using the XGBoost algorithm provided a significantly superior prediction of HF-hospitalization (AUC: 0.81) compared to the basic model (AUC: 0.64). The SHAP analysis revealed left atrium (LA) and right atrium (RV) strains as top imaging markers contributing to its performance with cutoff values of 17.5% and -15%, respectively. DATA CONCLUSIONS:Using an ML model, RV and LA strains measured in noncontrast cardiac MRI provide incremental value in predicting future hospitalization in HFpEF. EVIDENCE LEVEL:3 TECHNICAL EFFICACY: Stage 2. 10.1002/jmri.27932
Role of Multi-parameter-based Cardiac Magnetic Resonance in the Evaluation of Patients with Coronary Heart Disease Combined with Heart Failure. Current medical imaging BACKGROUND:Coronary Heart Disease (CHD) is one of the most common types of cardiovascular disease, and Heart Failure (HF) is an important factor in its progression. We aimed to evaluate the diagnostic value and predictors of multiparametric Cardiac Magnetic Resonance (CMR) in CHD patients with HF. METHODS:The study retrospectively included 145 CHD patients who were classified into CHD (HF+) (n = 91) and CHD (HF-) (n = 54) groups according to whether HF occurred. CMR assessed LV function, myocardial strain and T1 mapping. Multivariate linear regression analyses were performed to identify predictors of LV dysfunction, myocardial fibrosis, and LV remodeling. RESULTS:CHD (HF+) group had impaired strain, with increased native T1, ECV, and LVM index. The impaired strain was associated with LVM index (p < 0.05), where native T1 and ECV were affected by log-transformed amino-terminal pro-B-type natriuretic peptide (NT-proBNP) levels. ROC analysis showed the combination of global circumferential strain (GCS), native T1, and LVM had a higher diagnostic value for the occurrence of HF in CHD patients.<P> Meanwhile, log-transformed NT-proBNP was an independent determinant of impaired strain, increased LVM index, native T1 and ECV. CONCLUSION:HF has harmful effects on LV systolic function in patients with CHD. In CHD (HF+) group, LV dysfunction is strongly correlated with the degree of LV remodeling and myocardial fibrosis. The combination of the three is more valuable in diagnosing HF than conventional indicators. 10.2174/0115734056283569240227062332
Myocardial mechanical function measured by cardiovascular magnetic resonance in patients with heart failure. Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance BACKGROUND:Strain analysis offers a valuable tool to assess myocardial mechanics, allowing for the detection of impairments in heart function. This study aims to evaluate the pattern of myocardial strain in patients with heart failure (HF). METHODS:In the present study, myocardial strain was measured by cardiac magnetic resonance imaging feature tracking in 35 control subjects without HF and 195 HF patients. The HF patients were further categorized as HF with preserved ejection fraction (HFpEF, n = 80), with mid-range ejection fraction (HFmrEF, n = 34), and with reduced ejection fraction (HFrEF, n = 81). Additionally, quantitative tissue evaluation parameters, including native T1 relaxation time and extracellular volume (ECV), were examined. RESULTS:Compared to controls, patients in all HF groups (HFpEF, HFmrEF, and HFrEF) demonstrated impaired left ventricular (LV) strains and systolic and diastolic strain rates in all three directions (radial, circumferential, and longitudinal) (p < 0.05 for all). LV strains also showed significant correlations with LV ejection fraction and brain natriuretic peptide levels (p < 0.001 for all). Notably, septal contraction was significantly affected in HFpEF compared to controls. While LV torsion was slightly increased in HFpEF, it was decreased in HFrEF. Native T1 relaxation times and ECV fractions were significantly higher in HFrEF compared to HFpEF (p < 0.05). Overall, myocardial strain parameters demonstrated good performance in differentiating HF categories. CONCLUSIONS:The myocardial strain impairments exhibit a spectrum of severity in patients with HFpEF, HFmrEF, and HFrEF compared to controls. Assessment of myocardial mechanics using strain analysis may offer a clinically useful tool for monitoring the progression of systolic and diastolic dysfunction in HF patients. 10.1016/j.jocmr.2024.101111
Left ventricular size and heart failure: A cardiac MRI assessment of 38,129 individuals from the UK Biobank. International journal of cardiology BACKGROUND:Previous studies suggest that prevalent heart failure (HF) differs based on left ventricular ejection fraction (LVEF) and left ventricular (LV) chamber size. Furthermore, the prevalence of HF with preserved ejection fraction (HFpEF) is often considered approaching, or exceeding that of HF with reduced ejection fraction in the community. AIM:The aim of this study was to evaluate prevalent and incident HF based on LVEF and CMR-determined LV size within a large community-dwelling cohort. METHODS:Individuals from the United Kingdom Biobank (UKB) who underwent CMR and had available health record linkage to allow ascertainment of HF diagnosis were included. The cohort was analysed according to LVEF, LV end-diastolic volume (LVEDV) quartiles and LVEDV indexed to body surface area (LVEDVi). RESULTS:38,129 individuals were included, comprising those with reduced LVEF (LVEF<50 %, n = 5096) and preserved LVEF (LVEF 50-60 %, n = 22,907, LVEF≥60 %, n = 10,126). Prevalent HF was highest in males with LVEF<50 %, and participants with reduced LVEF had higher rates of incident HF (p < 0.001) during the follow-up period (median = 2.46 years from CMR). Mean LVEDV and LVEDVi were largest in individuals with EF < 50 % (146.9 ± 36.2 ml and 76.8 ± 16.4 ml/m respectively). Compared to the smallest quartiles, the largest quartiles for LVEDV were associated with increased odds of HF (odds ratio 2.14 [95 % confidence interval 1.47-3.12], p < 0.001). CONCLUSIONS:Over 50 % of HF cases occur in individuals with LVEF ≥50 %, however HF prevalence is highest in those with reduced LVEF, particularly in males. Larger LV size is associated with increased HF across the LVEF spectrum. 10.1016/j.ijcard.2024.132687
[State of the art for value of cardiac MRI in the assessment of heart failure with preserved ejection fraction]. Zhonghua xin xue guan bing za zhi 10.3760/cma.j.cn112148-20210923-00814
How Should Physicians Assess Myocardial Contraction?: Redefining Heart Failure With a Preserved Ejection Fraction. Maurer Mathew S,Packer Milton JACC. Cardiovascular imaging 10.1016/j.jcmg.2019.12.021
Cardiac MRI for the prognostication of heart failure with preserved ejection fraction: A systematic review and meta-analysis. Assadi Hosamadin,Jones Rachel,Swift Andrew J,Al-Mohammad Abdallah,Garg Pankaj Magnetic resonance imaging BACKGROUND:Cardiac magnetic resonance imaging (MRI) is emerging as an important imaging tool in the assessment of heart failure with preserved ejection fraction (HFpEF). This systematic review and meta-analysis aim to synthesise and consolidate the current literature on cardiac MRI for prognostication of HFpEF. METHODS DESIGN:Systematic review and meta-analysis. DATA SOURCES:Scopus (PubMed and Embase) for studies published between 2008 and 2019. Eligibility criteria for study selection were studies that evaluated the prognostic role of cardiac MRI in HFpEF. Random effects meta-analyses of the reported hazard ratios (HR) for clinical outcomes was performed. RESULTS:Initial screening identified 97 studies. From these, only nine (9%) studies met all the criteria. The main cardiac MRI methods that demonstrated association to prognosis in HFpEF included late gadolinium enhancement (LGE) assessment of scar (n = 3), tissue characterisation with T1-mapping (n = 4), myocardial ischaemia (n = 1) and right ventricular dysfunction (RVSD) (n = 1). The pooled HR for all 9 studies was 1.52 (95% CI 1.05-1.99, P < 0.01). Sub-evaluation by cardiac MRI methods revealed varying HRs: LGE (net n = 402, HR = 1.6, 95% CI 0.42-2.78, P = 0.008); T1-mapping (n = 1623, HR = 1.25, 95% CI 0.891-1.60, P < 0.001); myocardial ischaemia or RVSD (n = 325, HR = 3.19, 95% CI 0.30-6.08, P = 0.03). CONCLUSION:This meta-analysis demonstrates that multiparametric cardiac MRI has value in prognostication of patients with HFpEF. HFpEF patients with a detectable scar on LGE, fibrosis on T1-mapping, myocardial ischaemia or RVSD appear to have a worse prognosis. PROSPERO REGISTRATION NUMBER:CRD42020187228. 10.1016/j.mri.2020.11.011
[Severe right ventricular heart failure]. Kardiologiia The article presents a clinical case of isolated, severe right ventricular heart failure in the absence of cardiac magnetic resonance imaging confirmation of myocardial injury. 10.18087/cardio.2021.9.n1176
Prognostic significance of T1 mapping parameters in heart failure with preserved ejection fraction: a systematic review. Moustafa Abdelmoniem,Khan Mohammad Saud,Alsamman Mohd Amer,Jamal Faisal,Atalay Michael K Heart failure reviews Heart failure with preserved ejection fraction (HFpEF) accounts for almost one-half of all heart failure (HF) patients and continues to increase in prevalence. While mortality with heart failure with reduced ejection fraction (HFrEF) has decreased over the past few decades with use of evidence-based HFrEF therapy, mortality related to heart failure with HFpEF has not changed significantly over the same time period. The combination of poor prognosis and lack of effective treatment options creates a pressing need for novel strategies for better patient characterization. We conducted a systematic review to evaluate the prognostic value of cardiac magnetic resonance (CMR)-derived T1 relaxation time and extracellular volume fraction (ECV) in HFpEF patients. PubMed, Embase, and Cochrane Central were searched for relevant studies. The primary outcomes of interest were hospitalization for HF and all-cause mortality. Five studies with 2741 patients were included. Four studies reported correlation of outcomes with ECV, 2 studies reported correlation of outcomes with native T1 time, and 1 study reported correlation of outcomes with post-contrast T1 time. All five studies showed significant correlation of CMR-derived parameters with adverse outcomes including event-free survival to cardiac event, all cause, and cardiac mortality. CMR-determined ECV is strongly correlated with adverse outcomes in HFpEF cohorts. 10.1007/s10741-020-09958-4
Automatic classification of heart failure based on Cine-CMR images. International journal of computer assisted radiology and surgery PURPOSE:Heart failure (HF) is a serious and complex syndrome with a high mortality rate. In clinical diagnosis, the correct classification of HF is helpful. In our previous work, we proposed a self-supervised learning framework of HF classification (SSLHF) on cine cardiac magnetic resonance images (Cine-CMR). However, this method lacks the integration of three dimensions of spatial information and temporal information. Thus, this study aims at proposing an automatic 4D HF classification algorithm. METHODS:To construct a 4D classification model, we proposed an extensional framework called 4D-SSLHF. It mainly consists of self-supervised image restoration and HF classification. The image restoration proxy task utilizes three image transformation methods to enhance the exploration of spatial and temporal information in the Cine-CMR. In the classification task, we proposed a Siamese Conv-LSTM network by combining the Siamese network and bi-directional Conv-LSTM to integrate the features of the four dimensions simultaneously. RESULTS:Experimental results on 184 patients from Shanghai Chest Hospital achieved an AUC of 0.8794 and an ACC of 0.8402 in the five-fold cross-validation. Compared with our previous work, the improvements in AUC and ACC were 2.89 % and 1.94 %, respectively. CONCLUSIONS:In this study, we proposed a novel self-supervised learning framework named 4D-SSLHF for HF classification based on Cine-CMR. The proposed 4D-SSLHF can mine 3D spatial information and temporal information in Cine-CMR images well and accurately classify different categories of HF. The good classification results show our method's potential to assist physicians in choosing personalized treatment. 10.1007/s11548-023-03028-4
Comprehensive Assessment of Heart Failure with Preserved Ejection Fraction Using Cardiac MRI. Vega-Adauy Julián,Tok Ozge Ozden,Celik Ahmet,Barutcu Ahmet,Vannan Mani A Heart failure clinics Heart failure with preserved ejection fraction (HFpEF) burden is increasing. Its diagnostic process is challenging and imprecise due to absence of a single diagnostic marker, and the multiparametric echocardiography evaluation needed. Left ventricular (LV) ejection fraction (LVEF) is a limited marker of LV function; thus, allocating HF phenotypes based on LVEF can be misleading. HFpEF encompasses a broad spectrum of causes, and its diagnostic criteria give a central role to echocardiography, a first-line technique with inherent limitations related to ultrasound capabilities. Conversely, cardiac magnetic resonance provides superior anatomic and functional assessment, enabling tissue characterization, offering unprecedented diagnostic precision. 10.1016/j.hfc.2021.03.006
CMR to characterize myocardial structure and function in heart failure with preserved left ventricular ejection fraction. European heart journal. Cardiovascular Imaging Despite remarkable progress in therapeutic drugs, morbidity, and mortality for heart failure (HF) remains high in developed countries. HF with preserved ejection fraction (HFpEF) now accounts for around half of all HF cases. It is a heterogeneous disease, with multiple aetiologies, and as such poses a significant diagnostic challenge. Cardiac magnetic resonance (CMR) has become a valuable non-invasive modality to assess cardiac morphology and function, but beyond that, the multi-parametric nature of CMR allows novel approaches to characterize haemodynamics and with magnetic resonance spectroscopy (MRS), the study of metabolism. Furthermore, exercise CMR, when combined with lung water imaging provides an in-depth understanding of the underlying pathophysiological and mechanistic processes in HFpEF. Thus, CMR provides a comprehensive phenotyping tool for HFpEF, which points towards a targeted and personalized therapy with improved diagnostics and prevention. 10.1093/ehjci/jeae224
Prognostic significance of left atrial strain combined with left ventricular strain using cardiac magnetic resonance feature tracking in patients with heart failure with preserved ejection fraction. Heart and vessels We aimed to evaluate the prognostic value of left ventricular global longitudinal strain (LVGLS) and left atrial strain (LAS) obtained from magnetic resonance imaging (MRI) feature tracking in patients with heart failure with preserved ejection fraction (HFpEF). We retrospectively enrolled consecutive patients with HFpEF admitted to our hospital who underwent cardiac MRI. LVGLS and LAS were obtained from cine MRI by feature tracking. The end point was defined as a composite of all-cause death, myocardial infarction, and hospitalization due to decompensated HF. One-hundred patients with HFpEF were enrolled. Mean LVGLS and LAS were - 13.7 ± 3.7% and 22.5 ± 11.6%, respectively. During follow-up of 4.4 ± 1.9 years, 24 events occurred. Multivariate Cox proportional hazards model analysis demonstrated LAS was independently associated with adverse cardiac events. Kaplan-Meier curve analysis revealed that the patients with both LVGLS and LAS worse than the median (LVGLS ≥ - 12.2% and LAS ≤ 13.8%) had a significantly lower event-free rate compared to those with preserved strain (Log-rank P < 0.001). Simultaneous assessment of LVGLS and LAS using MRI was useful for risk stratification in the patients with HFpEF. 10.1007/s00380-023-02351-9
Diagnostic and prognostic utility of cardiovascular magnetic resonance imaging in heart failure with preserved ejection fraction - implications for clinical trials. Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance BACKGROUND:Heart failure with preserved ejection fraction (HFpEF) is a poorly characterized condition. We aimed to phenotype patients with HFpEF using multiparametric stress cardiovascular magnetic resonance imaging (CMR) and to assess the relationship to clinical outcomes. METHODS:One hundred and fifty four patients (51% male, mean age 72 ± 10 years) with a diagnosis of HFpEF underwent transthoracic echocardiography and CMR during a single study visit. The CMR protocol comprised cine, stress/rest perfusion and late gadolinium enhancement imaging on a 3T scanner. Follow-up outcome data (death and heart failure hospitalization) were captured after a minimum of 6 months. RESULTS:CMR detected previously undiagnosed pathology in 42 patients (27%), who had similar baseline characteristics to those without a new diagnosis. These diagnoses consisted of: coronary artery disease (n = 20, including 14 with 'silent' infarction), microvascular dysfunction (n = 11), probable or definite hypertrophic cardiomyopathy (n = 10) and constrictive pericarditis (n = 5). Four patients had dual pathology. During follow-up (median 623 days), patients with a new CMR diagnosis were at higher risk of adverse outcome for the composite endpoint (log rank test: p = 0.047). In multivariate Cox proportional hazards analysis, a new CMR diagnosis was the strongest independent predictor of adverse outcome (hazard ratio: 1.92; 95% CI: 1.07 to 3.45; p = 0.03). CONCLUSIONS:CMR diagnosed new significant pathology in 27% of patients with HFpEF. These patients were at increased risk of death and heart failure hospitalization. TRIAL REGISTRATION:ClinicalTrials.gov Identifier: NCT03050593 . Retrospectively registered; Date of registration: February 06, 2017. 10.1186/s12968-017-0424-9
Heart Failure With Preserved Ejection Fraction: Will Cardiac Magnetic Imaging Impact on Diagnosis, Treatment, and Outcomes?: Explaining the Need for Advanced Imaging to Clinical Stakeholders. Cardiology in review Clinicians frequently equate symptoms of volume overload to heart failure (HF) but such generalization may preclude diagnostic or etiologic precision essential to optimizing outcomes. HF itself must be specified as the disparate types of cardiac pathology have been traditionally surmised by examination of left ventricular (LV) ejection fraction (EF) as either HF with preserved LVEF (HFpEF-LVEF >50%) or reduced LVEF of (HFrEF-LVEF <40%). More recent data support a third, potentially transitional HF subtype, but therapy, assessment, and prognosis have been historically dictated within the corresponding LV metrics determined by echocardiography. The present effort asks whether this historically dominant role of echocardiography is now shifting slightly, becoming instead a shared if not complimentary test. Will there be a gradual increasing profile for cardiac magnetic resonance as the attempt to further refine our understanding, diagnostic accuracy, and outcomes for HFpEF is attempted? 10.1097/CRD.0000000000000494
The role of cardiovascular magnetic resonance in heart failure. Rajappan K,Bellenger N G,Anderson L,Pennell D J European journal of heart failure Cardiovascular Magnetic Resonance (CMR) is an accepted gold standard for non-invasive, accurate, and reproducible assessment of cardiac mass and function. The interest in its use for viability, myocardial perfusion and coronary artery imaging is also widespread and growing rapidly as the hardware and expertise becomes available in more centres, and the scans themselves become more cost effective. In patients with heart failure, accurate and reproducible serial assessment of remodelling is of prognostic importance and the lack of exposure to ionizing radiation is helpful. The concept of an integrated approach to heart failure and its complications using CMR is fast becoming a reality, and this will be tested widely in the coming few years, with the new generation of dedicated CMR scanners. 10.1016/s1388-9842(00)00096-9
Editorial commentary: Cardiac MRI imaging in heart failure with preserved ejection fraction. Trends in cardiovascular medicine 10.1016/j.tcm.2021.12.015
Imaging and mechanisms of heart failure with preserved ejection fraction: a state-of-the-art review. European heart journal. Cardiovascular Imaging Understanding of the pathophysiology of heart failure with preserved ejection fraction (HFpEF) has advanced rapidly over the past two decades. Currently, HFpEF is recognized as a heterogeneous syndrome, and there is a growing movement towards developing personalized treatments based on phenotype-guided strategies. Left ventricular dysfunction is a fundamental pathophysiological abnormality in HFpEF; however, recent evidence also highlights significant roles for the atria, right ventricle, pericardium, and extracardiac contributors. Imaging plays a central role in characterizing these complex and highly integrated domains of pathophysiology. This review focuses on established evidence, recent insights, and the challenges that need to be addressed concerning the pathophysiology of HFpEF, with a focus on imaging-based evaluations and opportunities for further research. 10.1093/ehjci/jeae152
Myocardial deformation assessed among heart failure entities by cardiovascular magnetic resonance imaging. Hashemi Djawid,Motzkus Laura,Blum Moritz,Kraft Robin,Tanacli Radu,Tahirovic Elvis,Doeblin Patrick,Zieschang Victoria,Zamani S Mahsa,Kelm Marcus,Kuehne Titus,Pieske Burkert,Alogna Alessio,Edelmann Frank,Duengen Hans-Dirk,Kelle Sebastian ESC heart failure AIMS:Although heart failure (HF) is a leading cause for hospitalization and mortality, normalized and comparable non-invasive assessment of haemodynamics and myocardial action remains limited. Moreover, myocardial deformation has not been compared between the guideline-defined HF entities. The distribution of affected and impaired segments within the contracting left ventricular (LV) myocardium have also not been compared. Therefore, we assessed myocardial function impairment by strain in patients with HF and control subjects by magnetic resonance imaging after clinically phenotyping these patients. METHODS AND RESULTS:This prospective study conducted at two centres in Germany between 2017 and 2018 enrolled stable outpatient subjects with HF [n = 56, including HF with reduced ejection fraction (HFrEF), HF with mid-range ejection fraction (HFmrEF), and HF with preserved ejection fraction (HFpEF)] and a control cohort (n = 12). Parameters assessed included measures for external myocardial function, for example, cardiac index and myocardial deformation measurements by cardiovascular magnetic resonance imaging, left ventricular global longitudinal strain (GLS), the global circumferential strain (GCS) and the regional distribution of segment deformation within the LV myocardium, as well as basic phenotypical characteristics. Comparison of the cardiac indices at rest showed no differences neither between the HF groups nor between the control group and HF patients (one-way ANOVA P = 0.70). The analysis of the strain data revealed differences between all groups in both LV GLS (One-way ANOVA: P < 0.01. Controls vs. HFpEF: -20.48 ± 1.62 vs. -19.27 ± 1.25. HFpEF vs. HFmrEF: -19.27 ± 1.25 vs. -15.72 ± 2.76. HFmrEF vs. HFrEF: -15.72 ± 2.76 vs. -11.51 ± 3.97.) and LV GCS (One-way ANOVA: P < 0.01. Controls vs. HFpEF: -19.74 ± 2.18 vs. -17.47 ± 2.10. HFpEF vs. HFmrEF: -17.47 ± 2.10 vs. -12.78 ± 3.47. HFrEF: -11.41 ± 3.27). Comparing the segment deformation distribution patterns highlighted the discriminating effect between the groups was much more prominent between the groups (one-way ANOVA P < 0.01) when compared by a score combining regional effects and a global view on the LV. Further analyses of the patterns among the segments affected showed that while the LVEF is preserved in HFpEF, the segments impaired in their contractility are located in the ventricular septum. The worse the LVEF is, the more segments are affected, but the septum remains an outstanding location with the most severe contractility impairment throughout the HF entities. CONCLUSIONS:While cardiac index at rest did not differ significantly between controls and stable HF patients suffering from HFrEF, HFmrEF, or HFpEF, the groups did differ significantly in LV GLS and LV GCS values. Regional strain analysis revealed that the LV septum is the location affected most, with reduced values already visible in HFpEF and further reductions in HFmrEF and HFrEF. 10.1002/ehf2.13193
Cardiovascular magnetic resonance for the diagnosis and management of heart failure with preserved ejection fraction. Barison Andrea,Aimo Alberto,Todiere Giancarlo,Grigoratos Chrysanthos,Aquaro Giovanni Donato,Emdin Michele Heart failure reviews Heart failure with preserved ejection fraction (HFpEF) is characterized by an impaired ventricular filling resulting in the development of dyspnea and other HF symptoms. Even though echocardiography is the cornerstone to demonstrate structural and/or functional alterations of the heart as the underlying cause for the clinical presentation, cardiovascular magnetic resonance (CMR) represents the noninvasive gold standard to assess cardiac morphology, function, and tissue changes. Indeed, CMR allows quantification of biventricular volumes, mass, wall thickness, systolic function, and intra- and extracardiac flows; diastolic functional indices include transmitral and pulmonary venous velocities, left ventricular and left atrial filling velocities from volumetric changes, strain analysis from myocardial tagging, tissue phase contrast, and feature tracking. Moreover, CMR allows superior tissue characterization of the myocardium and the pericardium, which are crucial for a noninvasive etiological and histopathological assessment of HFpEF: conventional T1-weighted, T2-weighted, and post-contrast sequences are now complemented by quantitative mapping sequences, including T1 and T2 mapping as well as extracellular volume quantification. Further experimental sequences comprise diffusion tensor analysis, blood oxygenation-dependent sequences, hyperpolarized contrast agents, spectroscopy, and elastography. Finally, artificial intelligence is beginning to help clinicians deal with an increasing amount of information from CMR exams. 10.1007/s10741-020-09998-w
Cardiovascular magnetic resonance in heart failure. Karamitsos Theodoros D,Neubauer Stefan Current cardiology reports Imaging has a central role in the evaluation of patients with heart failure (HF). Cardiovascular magnetic resonance (CMR) is rapidly evolving as a versatile imaging modality that often provides additional information to echocardiography in patients with suspected or known HF. CMR is the only imaging modality that has the ability to assess, without exposure to ionizing radiation, cardiac function, structure (tissue characterization), perfusion, and viability. Moreover, magnetic resonance spectroscopy techniques can assess the pathophysiologic role of deranged cardiac energetics in HF. In this review we discuss the role of CMR in the evaluation of patients with HF giving particular emphasis to recent developments and the additional information that can be obtained with this imaging modality, over and above standard echocardiography. 10.1007/s11886-011-0177-2
Heart failure with preserved ejection fraction assessed by cardiac magnetic resonance: From clinical uses to emerging techniques. Trends in cardiovascular medicine Patients with heart failure with preserved ejection fraction (HFpEF) account for approximately 50% of those with heart failure (HF) and have increased morbidity and mortality when compared to those with HF with reduced ejection fraction. Currently, the pathophysiology and diagnostic criteria for HFpEF remain unclear, contributing significantly to delays in creating a beneficial and tailored treatment that can improve the prognosis of HFpEF. A multitude of studies have exclusively tested and illustrated the diagnostic value of echocardiography imaging in HFpEF; however, a widely-accepted criterion to identify HFpEF using cardiovascular magnetic resonance (CMR) imaging has not been established. As the gold standard for cardiac structural, functional measurement, and tissue characterization, CMR holds great potential for the early discovery of the pathophysiology, diagnosis, and risk stratification of HFpEF. This review aims to comprehensively discuss the diagnostic and prognostic role of CMR parameters in the setting of HFpEF through validated routine and prospective emerging techniques, and provide clinical perspectives for CMR imaging application in HFpEF. 10.1016/j.tcm.2021.12.006
Review of cardiovascular magnetic resonance (CMR) in heart failure. Preface. Kim Raymond J,Pennell Dudley J Heart failure clinics 10.1016/j.hfc.2009.03.002
Cardiovascular Magnetic Resonance in Valvular Heart Disease-Related Heart Failure. Uretsky Seth,Wolff Steven D Heart failure clinics Patients with valvular heart disease-related heart failure are unable to pump enough blood to meet the body's needs. Magnetic resonance imaging (MRI) can play an important role by identifying these patients and distinguishing them from patients whose valvular disease is not the cause of their heart failure. Heart failure is a major public health problem, with a prevalence of 5.8 million people in the United States and more than 223 million people worldwide. This article focuses on the diagnostic and prognostic value of MRI patients with valvular causes of heart failure. 10.1016/j.hfc.2020.09.002
Magnetic resonance imaging in heart failure, including coronary imaging: numbers, facts, and challenges. Adams Lisa,Noutsias Michel,Bigalke Boris,Makowski Marcus R ESC heart failure Coronary artery disease (CAD) is a major risk factor for the incidence and progression of heart failure (HF). HF is characterized by a substantial morbidity and mortality and its lifetime risk is estimated at approximately 20% for men and women. As patients are in most cases identified only after developing overt clinical symptoms, detecting early stages of CAD and HF is of paramount importance. Due to its non-invasiveness, excellent soft-tissue contrast, high spatial resolution, and multiparametric nature, cardiovascular magnetic resonance (CMR) imaging has emerged as a promising radiation-free technique to assess a wide range of cardiovascular diseases such as CAD or HF, enabling a comprehensive evaluation of myocardial anatomy, regional and global function, and viability with the additional benefit of in vivo tissue characterization. CMR has the potential to enhance our understanding of coronary atherosclerosis and the aetiology of HF on functional and biological levels, to identify patients at risk for CAD or HF, and to enable individualized patient management and improved outcomes. Even though larger-scale studies on the different applications of CMR for the assessment of heart failure are scarce, recent research highlighted new possible clinical applications for CMR in the evaluation of CAD and HF. 10.1002/ehf2.12236
Role of Cardiac Magnetic Resonance Imaging in Heart Failure. Contaldi Carla,Dellegrottaglie Santo,Mauro Ciro,Ferrara Francesco,Romano Luigia,Marra Alberto M,Ranieri Brigida,Salzano Andrea,Rega Salvatore,Scatteia Alessandra,Cittadini Antonio,Cademartiri Filippo,Bossone Eduardo Heart failure clinics This review describes the current role and potential future applications of cardiac magnetic resonance (CMR) for the management of heart failure (HF). CMR allows noninvasive morphologic and functional assessment, tissue characterization, blood flow, and perfusion evaluation. CMR overcomes echocardiography limitations (geometric assumptions, interobserver variability and poor acoustic window) and provides incremental information in relation to cause, prognosis, and treatment monitoring of patients with HF. 10.1016/j.hfc.2021.01.001
Cardiovascular magnetic resonance imaging in heart failure. Yoneyama Kihei,Kitanaka Yuki,Tanaka Osamu,Akashi Yoshihiro J Expert review of cardiovascular therapy INTRODUCTION:Heart failure is a complex clinical syndrome resulting from heart structural remodeling and impaired function in ejecting blood; its incidence is increasing markedly worldwide. The observed variations in the structure and function of the heart are attributable to differences in etiology of heart failure. Cardiac magnetic resonance imaging (CMR) can characterize myocardial tissue, assess myocardial viability, and help diagnose specific cardiomyopathies. The emergence of T1 mapping techniques further improves our knowledge and the clinical assessment of myocardial diffuse fibrosis. Physicians, therefore, must identify the variations using CMR to improve patient's symptoms, survival, and quality of life. Area covered: Current reports regarding CMR and the evidence for heart failure diagnosis and therapy as a potential marker of therapeutic response, including low- and high-risk patients, were reviewed. Literature search was performed using PubMed and Google Scholar for literature relevant to CMR, late gadolinium enhancement, T1 mapping, assessment of fibrosis and remodeling, coronary artery, myocardial infarction, heart failure, and its outcomes. Expert commentary: The authors review current evidence and discuss the potential ability of CMR to guide, diagnose, plan risk strategies, and treat patients with heart failure. 10.1080/14779072.2018.1445525
Cardiovascular Magnetic Resonance Imaging and Heart Failure. Liu Chuanfen,Ferrari Victor A,Han Yuchi Current cardiology reports PURPOSE OF REVIEW:The purpose of this review is to summarize the application of cardiac magnetic resonance (CMR) in the diagnostic and prognostic evaluation of patients with heart failure (HF). RECENT FINDINGS:CMR is an important non-invasive imaging modality in the assessment of ventricular volumes and function and in the analysis of myocardial tissue characteristics. The information derived from CMR provides a comprehensive evaluation of HF. Its unique ability of tissue characterization not only helps to reveal the underlying etiologies of HF but also offers incremental prognostic information. CMR is a useful non-invasive tool for the diagnosis and assessment of prognosis in patients suffering from heart failure. 10.1007/s11886-021-01464-9