Dataset Information

Left ventricular myocardial strain and tissue characterization by cardiac magnetic resonance imaging in immune checkpoint inhibitor associated cardiotoxicity.

ABSTRACT:

Background

Immune checkpoint inhibitors (ICIs) are highly effective in treating cancer; however, cardiotoxicity can occur, including myocarditis. Cardiac magnetic resonance (CMR) imaging is useful for evaluation of myocarditis, although it has not been well studied in ICI cardiotoxicity.

Methods

We identified patients referred for CMR evaluation of ICI cardiotoxicity from September 2015 through September 2019. We assessed structural and functional parameters, feature tracking (FT) left ventricular and atrial strain, T2- weighted ratios and quantitative late gadolinium enhancement (LGE). We also applied the Updated Lake Louise Criteria for diagnosis of myocarditis.

Results

Of the 20 patients referred, the median left ventricular ejection fraction (LVEF) was 52.5% ± 19.1 and 50% had a normal LVEF (?53%). FT strain analysis revealed an average abnormal global longitudinal strain (GLS) of -9.8%± 4.2%. In patients with a normal LVEF, the average GLS remained depressed at -12.3%± 2.4%. In all patients, GLS demonstrated a significant negative correlation with LVEF (rs = -0.64, p 0.002). Sixteen patients (80%) had presence of LGE (14 non-ischemic pattern and 2 ischemic). Percent LGE did not correlate with any CMR parameters and notably did not correlate with LVEF (rs = -0.29, p = 0.22) or GLS (rs = 0.10, p = 0.67), highlighting the value of tissue characterization beyond functional assessment. Nine patients (45%) met full Updated Lake Louise Criteria and 85% met at least one criterion, suggestive of myocarditis in the correct clinical context. Thirteen patients (65%) were treated for ICI-associated myocarditis and, of these, 54% (n = 7) had recovery of LVEF to normal. There was no correlation between LVEF (p = 0.47), GLS (0.89), or % LGE (0.15) and recovery of LVEF with treatment.

Conclusion

In patients with suspected ICI cardiotoxicity, CMR is an important diagnostic tool, even in the absence of overt left ventricular dysfunction, as abnormalities in left ventricular strain, T2 signal and LGE can identifying disease.

SUBMITTER: Higgins AY

PROVIDER: S-EPMC7895343 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Left ventricular myocardial strain and tissue characterization by cardiac magnetic resonance imaging in immune checkpoint inhibitor associated cardiotoxicity.

Higgins Angela Y AY Arbune Amit A Soufer Aaron A Ragheb Elio E Kwan Jennifer M JM Lamy Jerome J Henry Mariana M Cuomo Jason R JR Charifa Ahmad A Gallegos Cesia C Hull Sarah S Coviello Jessica Shank JS Bader Anna S AS Peters Dana C DC Huber Steffen S Mojibian Hamid R HR Sinusas Albert J AJ Kluger Harriet H Baldassarre Lauren A LA

PloS one 20210219 2

<h4>Background</h4>Immune checkpoint inhibitors (ICIs) are highly effective in treating cancer; however, cardiotoxicity can occur, including myocarditis. Cardiac magnetic resonance (CMR) imaging is useful for evaluation of myocarditis, although it has not been well studied in ICI cardiotoxicity.<h4>Methods</h4>We identified patients referred for CMR evaluation of ICI cardiotoxicity from September 2015 through September 2019. We assessed structural and functional parameters, feature tracking (FT) ...[more]

PMID: 33606757

Similar Datasets

Project description:BackgroundAlthough there are many studies on the prognostic factors of left ventricular myocardial noncompaction (LVNC), the determinants are varied and not entirely consistent. This study aimed to build predictive models using radiomics features and machine learning to predict major adverse cardiovascular events (MACEs) in patients with LVNC.MethodsIn total, 96 patients with LVNC were included and randomly divided into training and test cohorts. A total of 105 cine cardiac magnetic resonance (CMR)-derived radiomics features and 35 clinical characteristics were extracted. Five different oversampling algorithms were compared for selection of the optimal imbalanced processing. Feature importance was assessed with extreme gradient boosting (XGBoost). We compared the performance of 5 machine learning classification methods with different sample:feature ratios to determine the optimal hybrid classification strategy. Subsequently, radiomics, clinical, and combined radiomics-clinical models were developed and compared.ResultsThe machine learning pipeline included an adaptive synthetic (ADASYN) algorithm for imbalanced processing, XGBoost feature selection with a sample:feature ratio of 10, and support vector machine (SVM) modeling. The areas under the receiver operating characteristic curves (AUCs) of the radiomics model, clinical model, and combined model in the validation cohort were 0.87 (sensitivity 83.33%, specificity 64.29%), 0.65 (sensitivity 16.67%, specificity 78.57%), and 0.92 (specificity 33.33%, sensitivity 100.00%), respectively. The radiomics model performed similarly to the clinical and combined models (P=0.124 and P=0.621, respectively). The performance of the combined model was significantly better than that of the clinical model (P=0.003).ConclusionsThe machine learning-based cine CMR radiomics model performed well at predicting MACEs in patients with LVNC. Adding radiomics features offered incremental prognostic value over clinical factors alone.

Project description:Background Despite limitations as a stand-alone parameter, left ventricular (LV) ejection fraction is the preferred measure of myocardial function and marker for postinfarction risk stratification. LV myocardial uniformity alterations may provide superior prognostic information after acute myocardial infarction, which was the subject of this study. Methods and Results Consecutive patients with acute myocardial infarction (n=1082; median age: 63 years; 75% male) undergoing cardiac magnetic resonance at a median of 3 days after infarction were included in this multicenter observational study. Circumferential and radial uniformity ratio estimates were derived from cardiac magnetic resonance feature tracking as markers of mechanical uniformity alterations (values between 0 and 1 with 1 reflecting perfect uniformity). The clinical end point was the 12-month rate of major adverse cardiac events, consisting of all-cause death, reinfarction, and new congestive heart failure. Patients with major adverse cardiac events (n=73) had significantly impaired circumferential uniformity ratio estimates (0.76 [interquartile range: 0.67-0.86] versus 0.84 [interquartile range: 0.76-0.89]; P<0.001) and radial uniformity ratio estimates (0.69 [interquartile range: 0.60-0.79] versus 0.76 [interquartile range: 0.67-0.83]; P<0.001) compared with patients without events. Although uniformity estimates did not provide independent prognostic information in the overall cohort, a circumferential uniformity ratio estimate below the median of 0.84 emerged as an independent predictor of outcome in postinfarction patients with LV ejection fraction >35% (n=959), even after adjustment for established risk factors (hazard ratio: 1.99; 95% CI, 1.06-3.74; P=0.033 in multivariable Cox regression analysis). In contrast, LV ejection fraction was not associated with adverse events in this subgroup of patients with acute myocardial infarction. Conclusions Cardiac magnetic resonance-derived estimates of mechanical uniformity alterations are novel markers for risk assessment after acute myocardial infarction, and the circumferential uniformity ratio estimate provides independent prognostic information for patients with preserved or only moderately reduced LV ejection fraction.

Dataset Information

Left ventricular myocardial strain and tissue characterization by cardiac magnetic resonance imaging in immune checkpoint inhibitor associated cardiotoxicity.

Background

Methods

Results

Conclusion

Publications

Left ventricular myocardial strain and tissue characterization by cardiac magnetic resonance imaging in immune checkpoint inhibitor associated cardiotoxicity.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets