Validation of machine learning models for estimation of left ventricular ejection fraction on point-of-care ultrasound: insights on features that impact performance.

BACKGROUND: Machine learning (ML) algorithms can accurately estimate left ventricular ejection fraction (LVEF) from echocardiography, but their performance on cardiac point-of-care ultrasound (POCUS) is not well understood. OBJECTIVES: We evaluate the performance of an ML model for estimation of LVEF on cardiac POCUS compared with Level III echocardiographers' interpretation and formal echo reported LVEF. METHODS: Clinicians at a tertiary care heart failure clinic prospectively scanned 138 participants using hand-carried devices. Video data were analyzed offline by an ML model for LVEF. We compared the ML model's performance with Level III echocardiographers' interpretation and echo reported LVEF. RESULTS: There were 138 participants scanned, yielding 1257 videos. The ML model generated LVEF predictions on 341 videos. We observed a good intraclass correlation (ICC) between the ML model's predictions and the reference standards (ICC = 0.77-0.84). When comparing LVEF estimates for randomized single POCUS videos, the ICC between the ML model and Level III echocardiographers' estimates was 0.772, and it was 0.778 for videos where quantitative LVEF was feasible. When the Level III echocardiographer reviewed all POCUS videos for a participant, the ICC improved to 0.794 and 0.843 when only accounting for studies that could be segmented. The ML model's LVEF estimates also correlated well with LVEF derived from formal echocardiogram reports (ICC = 0.798). CONCLUSION: Our results suggest that clinician-driven cardiac POCUS produces ML model LVEF estimates that correlate well with expert interpretation and echo reported LVEF.

Automated Atrial Fibrillation Diagnosis by Echocardiography without ECG: Accuracy and Applications of a New Deep Learning Approach.

BACKGROUND: Automated rhythm detection on echocardiography through artificial intelligence (AI) has yet to be fully realized. We propose an AI model trained to identify atrial fibrillation (AF) using apical 4-chamber (AP4) cines without requiring electrocardiogram (ECG) data. METHODS: Transthoracic echocardiography studies of consecutive patients ≥ 18 years old at our tertiary care centre were retrospectively reviewed for AF and sinus rhythm. The study was first interpreted by level III-trained echocardiography cardiologists as the gold standard for rhythm diagnosis based on ECG rhythm strip and imaging assessment, which was also verified with a 12-lead ECG around the time of the study. AP4 cines with three cardiac cycles were then extracted from these studies with the rhythm strip and Doppler information removed and introduced to the deep learning model ResNet(2+1)D with an 80:10:10 training-validation-test split ratio. RESULTS: 634 patient studies (1205 cines) were included. After training, the AI model achieved high accuracy on validation for detection of both AF and sinus rhythm (mean F1-score = 0.92; AUROC = 0.95). Performance was consistent on the test dataset (mean F1-score = 0.94, AUROC = 0.98) when using the cardiologist's assessment of the ECG rhythm strip as the gold standard, who had access to the full study and external ECG data, while the AI model did not. CONCLUSIONS: AF detection by AI on echocardiography without ECG appears accurate when compared to an echocardiography cardiologist's assessment of the ECG rhythm strip as the gold standard. This has potential clinical implications in point-of-care ultrasound and stroke risk stratification.

Cardiac Phenotyping of SARS-CoV-2 in British Columbia: A Prospective Echo Study With Strain Imaging.

BACKGROUND: There is limited data on the residual echocardiographic findings including strain analysis among post-coronavirus disease (COVID) patients. The aim of our study is to prospectively phenotype post-COVID patients. METHODS: All patients discharged following acute COVID infection were systematically followed in the post-COVID-19 Recovery Clinic at Vancouver General Hospital and St. Paul's Hospital. At 4-18 weeks post diagnosis, patients underwent comprehensive echocardiographic assessment. Left ventricular ejection fraction (LVEF) was assessed by 3D, 2D Biplane Simpson's, or visual estimate. LV global longitudinal strain (GLS) was measured using a vendor-independent 2D speckle-tracking software (TomTec). RESULTS: A total of 127 patients (53% female, mean age 58 years) were included in our analyses. At baseline, cardiac conditions were present in 58% of the patients (15% coronary artery disease, 4% heart failure, 44% hypertension, 10% atrial fibrillation) while the remainder were free of cardiac conditions. COVID-19 serious complications were present in 79% of the patients (76% pneumonia, 37% intensive care unit admission, 21% intubation, 1% myocarditis). Normal LVEF was seen in 96% of the cohort and 97% had normal right ventricular systolic function. A high proportion (53%) had abnormal LV GLS defined as < 18%. Average LV GLS of septal and inferior segments were lower compared to that of other segments. Among patients without pre-existing cardiac conditions, LVEF was abnormal in only 1.9%, but LV GLS was abnormal in 46% of the patients. CONCLUSIONS: Most post-COVID patients had normal LVEF at 4-18 weeks post diagnosis, but over half had abnormal LV GLS.

Machine learning derived echocardiographic image quality in patients with left ventricular systolic dysfunction: insights on the echo views of greatest image quality.

We sought to determine the cardiac ultrasound view of greatest quality using a machine learning (ML) approach on a cohort of transthoracic echocardiograms (TTE) with abnormal left ventricular (LV) systolic function. We utilize an ML model to determine the TTE view of highest quality when scanned by sonographers. A random sample of TTEs with reported LV dysfunction from 09/25/2017-01/15/2019 were downloaded from the regional database. Component video files were analyzed using ML models that jointly classified view and image quality. The model consisted of convolutional layers for extracting spatial features and Long Short-term Memory units to temporally aggregate the frame-wise spatial embeddings. We report the view-specific quality scores for each TTE. Pair-wise comparisons amongst views were performed with Wilcoxon signed-rank test. Of 1,145 TTEs analyzed by the ML model, 74.5% were from males and mean LV ejection fraction was 43.1 ± 9.9%. Maximum quality score was best for the apical 4 chamber (AP4) view (70.6 ± 13.9%, p<0.001 compared to all other views) and worst for the apical 2 chamber (AP2) view (60.4 ± 15.4%, p<0.001 for all views except parasternal short-axis view at mitral/papillary muscle level, PSAX M/PM). In TTEs scanned by professional sonographers, the view with greatest ML-derived quality was the AP4 view.

Quantification of pleural effusions by two-dimensional transthoracic echocardiography.

PURPOSE: There is lack of validated methods for quantifying the size of pleural effusion from standard transthoracic (TTE) windows. The purpose of this study is to determine whether pleural effusion (Peff) measured from routine two-dimensional (2D) TTE views correlate with chest radiograph (CXR). MATERIALS AND METHODS: We retrospectively identified all inpatients who underwent a TTE and CXR within 2 days in a large tertiary care center. Peff was measured on TTE from parasternal long axis (PLAX), apical four-chamber (A4C), and subcostal views and on CXR. Logistic regression models were used determine optimal cut points to predict moderate or greater Peff. RESULTS: In 200 patients (mean age 69.3 ± 14.3 years, 49.5% female), we found statistically significant associations between Peff size assessed by all TTE views and CXR, with weak to moderate correlation (PLAX length: 0.21 (95% CI [0.05, 0.35]); PLAX depth: 0.21 (95% CI [0.05, 0.35]); A4C left: 0.31 (95% CI [0.13, 0.46]); A4C right: 0.39 (95% CI [0.17, 0.57]); subcostal: 0.38 (95% CI [0.07, 0.61]). The best TTE thresholds for predicting moderate or greater left-sided Peff on CXR was PLAX length left > = 8.6 cm (sensitivity 78%, specificity 54%, PPV 26%, and NPV 92%). The best TTE thresholds for predicting moderate or greater right-sided Peff on CXR was A4C right > = 2.6 cm (sensitivity 87%, specificity 60%, PPV 37%, and NPV 94%). CONCLUSIONS: We identified statistically significant associations with Peff size measured on TTE and CXR. The predictive ability of TTE to identify moderate or large pleural effusion is limited.

Incidentally identified left ventricular apical aneurysm in a patient with Fabry disease.

Fabry disease is a rare X-linked lysosomal storage disorder caused by a deficiency in the lysosomal enzyme, galactosidase A, that can result in a progressive increase in the left ventricle (LV) wall thickness from glycosphingolipid deposition leading to myocardial fibrosis, conduction abnormalities, arrhythmias, and heart failure. We present a case of a patient with advanced Fabry cardiomyopathy, in whom a small LV apical aneurysm was incidentally discovered on abdominal imaging, which could have easily evaded detection on standard transthoracic echocardiography. The LV apex should be thoroughly interrogated in patients with Fabry cardiomyopathy, as the finding of LV aneurysm could have important management implications with respect to the prevention of stroke and sudden cardiac death.

A Novel Continuous Left Ventricular Diastolic Function Score Using Machine Learning.

BACKGROUND: Unlike left ventricular (LV) ejection fraction, which provides a precise, reliable, and prognostically valuable measure of systolic function, there is no single analogous measure of LV diastolic function. OBJECTIVES: We aimed to develop a continuous score to grade LV diastolic function using machine learning modeling of echocardiographic data. METHODS: Consecutive echo studies performed at a tertiary-care center between February 1, 2010, and March 31, 2016, were assessed, excluding studies containing features that would interfere with diastolic function assessment as well as studies in which 1 or more parameters within the contemporary diastolic function assessment algorithm were not reported. Diastolic function was graded based on 2016 American Society of Echocardiography (ASE)/European Association of Cardiovascular Imaging (EACVI) guidelines, excluding indeterminate studies. Machine learning models were trained (support vector machine [SVM], decision tree [DT], XGBoost [XGB], and dense neural network [DNN]) to classify studies within the training set by diastolic dysfunction severity, blinded to the ASE/EACVI classification. The DNN model was retrained to generate a regression model (R-DNN) to predict a continuous LV diastolic function score. RESULTS: A total of 28,986 studies were included; 23,188 studies were used to train the models, and 5,798 studies were used for validation. The models were able to reclassify studies with high agreement to the ASE/EACVI algorithm (SVM, 83%; DT, 100%; XGB, 100%; DNN, 98%). The continuous diastolic function score corresponded well with ASE/EACVI guidelines, with scores of 1.00 ± 0.01 for studies with normal function and 0.74 ± 0.05, 0.51 ± 0.06, and 0.27 ± 0.11 for mild, moderate, and severe diastolic dysfunction, respectively (mean ± 1 SD). A score of <0.91 predicted abnormal diastolic function (area under the receiver operator curve = 0.99), while a score of <0.65 predicted elevated filling pressure (area under the receiver operator curve = 0.99). CONCLUSIONS: Machine learning can assimilate echocardiographic data and generate an automated continuous diastolic function score that corresponds well with current diastolic function grading recommendations.

Relationship between enlarged cardiac silhouette on chest X-ray and left ventricular size on transthoracic echocardiography.

The diagnostic accuracy of the cardiothoracic ratio on chest X-ray to detect left ventricular (LV) enlargement has not been well defined despite its traditional association with cardiomegaly. We aimed to determine whether the cardiothoracic ratio can accurately predict LV enlargement based on indexed linear measurements of the LV on transthoracic echocardiography (TTE). We included consecutive patients who had a TTE and a posteroanterior chest X-ray performed within 90 days of each other at a tertiary care center. LV size was determined by measuring the LV end-diastolic dimension (LVEDD) and LV end-diastolic dimension indexed (LVEDDI) to body surface area. The cardiothoracic ratio was calculated by dividing the maximum transverse diameter of the cardiac silhouette by the maximum transverse diameter of the right and left lung boundaries. 173 patients were included in the study (mean age 68 ± 15 years, 49.1% female). Mean cardiothoracic ratio was 0.56 ± 0.09, and the mean LVEDD and indexed LVEDDI were of 47 ± 8.6 mm and dimension of 27 ± 4.5 mm/m2 respectively. There was no significant correlation between the cardiothoracic ratio measured on chest X-ray and either the LVEDD or LVEDDI measured on TTE (r = 0.011, p = 0.879; r = 0.122, p = 0.111). The ability of the cardiothoracic ratio to predict LV enlargement (defined as LVEDDI > 30 mm/m2) was not statistically significant. The cardiothoracic ratio on chest X-ray is not a predictor of LV enlargement based on indexed linear measurements of the LV by TTE.

Prevalence of left ventricular systolic dysfunction by single echocardiographic view: towards an evidence-based point of care cardiac ultrasound scanning protocol.

Limited views are often obtained in the setting of cardiac ultrasound, however, the likelihood of missing left ventricular (LV) dysfunction based on a single view is not known. We sought to determine the echo views that were least likely to miss LV systolic dysfunction in consecutive transthoracic echocardiograms (TTEs). Structured data from TTEs performed at 2 hospitals from September 25, 2017, to January 15, 2019, were screened. Studies of interest were those with reported LV dysfunction. Views evaluated were the parasternal long-axis (PLAX), parasternal-short axis at mitral (PSAX M), papillary muscle (PSAX PM), and apical (PSAX A) levels, apical 2 (AP2), apical 3 (AP3), and apical 4 (AP4) chamber views. The probability that a view contained at least 1 abnormal segment was determined and analyzed with McNemar's test for 21 adjusted pair-wise comparisons. There were 4102 TTE studies included for analysis. TTEs on males comprised 72.7% of studies with a mean LV ejection fraction of 42.8 ± 9.7%. The echo view with the greatest likelihood of encompassing an abnormal segment was the AP2 view with a prevalence of 93.4% (p < 0.001, compared to all other views). The PLAX view performed the worst with a prevalence of 82.5% (p < 0.015, compared to all other views). The best parasternal view for the detection of abnormality was the PSAX PM view at 90.4%. In conclusions, a single echo view will contain abnormal segments > 82% of the time in the setting of LV systolic dysfunction, with a prevalence of up to 93.4% in the apical windows.

Point-of-care ultrasound in the COVID-19 era: A scoping review.

In the midst of the COVID-19 pandemic, unprecedented pressure has been added to healthcare systems around the globe. Imaging is a crucial component in the management of COVID-19 patients. Point-of-care ultrasound (POCUS) such as hand-carried ultrasound emerges in the COVID-19 era as a tool that can simplify the imaging process of COVID-19 patients, and potentially reduce the strain on healthcare providers and healthcare resources. The preliminary evidence available suggests an increasing role of POCUS in diagnosing, monitoring, and risk-stratifying COVID-19 patients. This scoping review aims to delineate the challenges in imaging COVID-19 patients, discuss the cardiopulmonary complications of COVID-19 and their respective sonographic findings, and summarize the current data and recommendations available. There is currently a critical gap in knowledge in the role of POCUS in the COVID-19 era. Nonetheless, it is crucial to summarize the current preliminary data available in order to help fill this gap in knowledge for future studies.

Impact of the updated diastolic function guidelines in the real world.

BACKGROUND: Echocardiographic assessment of diastolic function is complex but can aid in the diagnosis of heart failure, particularly in patients with preserved ejection fraction. In 2016, the American Society of Echocardiography (ASE) and European Association of Cardiovascular Imaging (EACVI) published an updated algorithm for the evaluation of diastolic function. The objective of our study was to assess its impact on diastolic function assessment in a real-world cohort of echo studies. METHODS: We retrospectively identified 71,727 consecutive transthoracic echo studies performed at a tertiary care center between February 2010 and March 2016 in which diastolic function was reported based on the 2009 ASE Guidelines. We then programmed a software algorithm to assess diastolic function in these echo studies according to the 2016 ASE/EACVI Guidelines. RESULTS: When diastolic function assessment based on the 2009 guidelines was compared to that using the 2016 guidelines, there were significant differences in proportion of studies classified as normal (23% vs. 32%) or indeterminate (43% vs. 36%) function, and mild (23% vs. 23%), moderate (10% vs. 8%), or severe (1% vs. 2%) diastolic dysfunction, with poor agreement between the two methods (Kappa 0.323, 95% CI 0.318-0.328). Furthermore, within the subgroup of studies with preserved ejection fraction and no evidence of myocardial disease, there was significant reclassification from mild diastolic dysfunction to normal diastolic function. CONCLUSION: The updated guidelines result in significant differences in diastolic function interpretation in the real world. Our findings have important implications for the identification of patients with or at risk for heart failure.

Automated estimation of echocardiogram image quality in hospitalized patients.

We developed a machine learning model for efficient analysis of echocardiographic image quality in hospitalized patients. This study applied a machine learning model for automated transthoracic echo (TTE) image quality scoring in three inpatient groups. Our objectives were: (1) Assess the feasibility of a machine learning model for echo image quality analysis, (2) Establish the comprehensiveness of real-world TTE reporting by clinical group, and (3) Determine the relationship between machine learning image quality and comprehensiveness of TTE reporting. A machine learning model was developed and applied to TTEs from three matched cohorts for image quality of nine standard views. Case TTEs were comprehensive studies in mechanically ventilated patients between 01/01/2010 and 12/31/2015. For each case TTE, there were two matched spontaneously breathing controls (Control 1: Inpatients scanned in the lab and Control 2: Portable studies). We report the overall mean maximum and view specific quality scores for each TTE. The comprehensiveness of an echo report was calculated as the documented proportion of 12 standard parameters. An inverse probability weighted regression model was fit to determine the relationship between machine learning quality score and the completeness of a TTE report. 175 mechanically ventilated TTEs were included with 350 non-intubated samples (175 Control 1: Lab and 175 Control 2: Portable). In total, the machine learning model analyzed 14,086 echo video clips for quality. The overall accuracy of the model with regard to the expert ground truth for the view classification was 87.0%. The overall mean maximum quality score was lower for mechanically ventilated TTEs (0.55 [95% CI 0.54, 0.56]) versus 0.61 (95% CI 0.59, 0.62) for Control 1: Lab and 0.64 (95% CI 0.63, 0.66) for Control 2: Portable; p = 0.002. Furthermore, mechanically ventilated TTE reports were the least comprehensive, with fewer reported parameters. The regression model demonstrated the correlation of echo image quality and completeness of TTE reporting regardless of the clinical group. Mechanically ventilated TTEs were of inferior quality and clinical utility compared to spontaneously breathing controls and machine learning derived image quality correlates with completeness of TTE reporting regardless of the clinical group.

Cardiac point-of-care to cart-based ultrasound translation using constrained CycleGAN.

PURPOSE:  The emerging market of cardiac handheld ultrasound (US) is on the rise. Despite the advantages in ease of access and the lower cost, a gap in image quality can still be observed between the echocardiography (echo) data captured by point-of-care ultrasound (POCUS) compared to conventional cart-based US, which limits the further adaptation of POCUS. In this work, we aim to present a machine learning solution based on recent advances in adversarial training to investigate the feasibility of translating POCUS echo images to the quality level of high-end cart-based US systems. METHODS:  We propose a constrained cycle-consistent generative adversarial architecture for unpaired translation of cardiac POCUS to cart-based US data. We impose a structured shape-wise regularization via a critic segmentation network to preserve the underlying shape of the heart during quality translation. The proposed deep transfer model is constrained to the anatomy of the left ventricle (LV) in apical two-chamber (AP2) echo views. RESULTS:  A total of 1089 echo studies from 841 patients are used in this study. The AP2 frames are captured by POCUS (Philips Lumify and Clarius) and cart-based (Philips iE33 and Vivid E9) US machines. The dataset of quality translation comprises a total of 441 echo studies from 395 patients. Data from both POCUS and cart-based systems of the same patient were available in 122 cases. The deep-quality transfer model is integrated into a pipeline for an automated cardiac evaluation task, namely segmentation of LV in AP2 view. By transferring the low-quality POCUS data to the cart-based US, a significant average improvement of 30% and 34 mm is obtained in the LV segmentation Dice score and Hausdorff distance metrics, respectively. CONCLUSION:  This paper presents the feasibility of a machine learning solution to transform the image quality of POCUS data to that of high-quality high-end cart-based systems. The experiments show that by leveraging the quality translation through the proposed constrained adversarial training, the accuracy of automatic segmentation with POCUS data could be improved.

The Vancouver 3M (Multidisciplinary, Multimodality, But Minimalist) Clinical Pathway Facilitates Safe Next-Day Discharge Home at Low-, Medium-, and High-Volume Transfemoral Transcatheter Aortic Valve Replacement Centers: The 3M TAVR Study.

OBJECTIVES: The authors sought to prospectively determine the safety and efficacy of next-day discharge using the Vancouver 3M (Multidisciplinary, Multimodality, but Minimalist) Clinical Pathway. BACKGROUND: Transfemoral transcatheter aortic valve replacement (TAVR) is an alternative to surgery in high- and intermediate-risk patients; however, hospital stays average at least 6 days in most trials. The Vancouver 3M Clinical Pathway is focused on next-day discharge, made possible by the use of objective screening criteria as well as streamlined peri- and post-procedural management guidelines. METHODS: Patients were enrolled from 6 low-volume (<100 TAVR/year), 4 medium-volume, and 3 high-volume (>200 TAVR/year) centers in Canada and the United States. The primary outcomes were a composite of all-cause death or stroke by 30 days and the proportion of patients successfully discharged home the day following TAVR. RESULTS: Of 1,400 screened patients, 411 were enrolled at 13 centers and received a SAPIEN XT (58.2%) or SAPIEN 3 (41.8%) valve (Edwards Lifesciences, Irvine, California). In centers enrolling exclusively in the study, 55% of screened patients were enrolled. The median age was 84 years (interquartile range: 78 to 87 years) with a median STS score of 4.9% (interquartile range: 3.3% to 6.8%). Next-day discharge home was achieved in 80.1% of patients, and within 48 h in 89.5%. The composite of all-cause mortality or stroke by 30 days occurred in 2.9% (95% confidence interval: 1.7% to 5.1%), with neither component of the primary outcome affected by hospital TAVR volume (p = 0.51). Secondary outcomes at 30 days included major vascular complication 2.4% (n = 10), readmission 9.2% (n = 36), cardiac readmission 5.7% (n = 22), new permanent pacemaker 5.7% (n = 23), and >mild paravalvular regurgitation 3.8% (n = 15). CONCLUSIONS: Adherence to the Vancouver 3M Clinical Pathway at low-, medium-, and high-volume TAVR centers allows next-day discharge home with excellent safety and efficacy outcomes.

Echocardiographic Assessment of Patients with Fabry Disease.

Fabry disease is an X-linked lysosomal storage disorder that results from a deficiency of α-galactosidase A. Increased left ventricular wall thickness has been the most commonly described cardiovascular manifestation of the disease. However, a variety of other structural and functional abnormalities have also been reported. Echocardiography is an effective noninvasive method of assessing the cardiac involvement of Fabry disease. A more precise and comprehensive characterization of Fabry cardiomyopathy using conventional and novel echocardiographic techniques may lead to earlier diagnosis, more accurate prognostication, and timely treatment. The aim of this review is to provide a comprehensive overview of the structural and functional abnormalities on echocardiography that have thus far been described in patients with Fabry disease and to highlight potential areas that would benefit from further research.

Clinical effectiveness of a systematic "pill-in-the-pocket" approach for the management of paroxysmal atrial fibrillation.

BACKGROUND: For patients with symptomatic, sustained atrial fibrillation (AF), a "pill-in-the-pocket" antiarrhythmic drug (PIP-AAD) strategy has been proposed to reduce emergency department (ED) use. OBJECTIVE: To assess the clinical utility of a protocolled PIP-AAD approach within contemporary practice. METHODS: Consecutive patients who hemodynamically tolerated symptomatic, sustained AF were prospectively managed with the PIP-AAD strategy. All patients were given an atrioventricular nodal blocker 30 minutes prior to a single oral dose of a class Ic antiarrhythmic drug. If the initial PIP-AAD in the ED was efficacious and tolerated, PIP-AADs were given out of hospital for subsequent sustained AF episodes. Usage and complications were systematically recorded. RESULTS: During a median follow-up period of 565 days, 43 of 80 patients presented to the ED for initial PIP-AAD. Sinus rhythm was restored without complication in 30 of 43 patients. The reasons for initial PIP-AAD failure were inefficacy (6 patients), significant hypotension (4 patients), conversion to flutter necessitating cardioversion (2 patients), and syncopal conversion pause (1 patient). For the 30 patients with successful initial PIP-AAD, 159 out-of-hospital PIP-AAD treatments occurred (mean 5.3 ± SD 1.3 per patient). Compared with ED visits in the period prior to PIP-AAD initiation, there was a significant reduction in visits (2.6 ± 3.0 vs. 0.4±0.9 ED visits per patient, P < .001) and the need for cardioversion (2.3 ± 3.1 vs. 0.0 ± 0.2 treatments per patient, P < .001). Adverse events associated with out-of-hospital PIP-AAD include presyncope (3 of 30 patients), syncope necessitating pacemaker implantation (1 patient), and conversion to flutter (1 patient). CONCLUSION: Out-of-hospital PIP-AAD can be an effective for highly selected patients; however, the rates of treatment failure and adverse events are clinically relevant, which limits the widespread application of a PIP-AAD approach.

Multimodality imaging of a pulmonary artery sarcoma.

Pulmonary artery sarcoma is a rare malignant neoplasm. Here, we describe a patient with a pulmonary artery sarcoma, which was only subtly visible and therefore not fully appreciated on initial transthoracic echocardiogram. Characterization of the tumor was aided by the use of multimodality imaging that included computed tomography, magnetic resonance imaging, and positron emission tomography. Familiarity with its appearance on multiple imaging modalities including echocardiography is important to ensure timely diagnosis, although the optimal treatment strategy is still unknown, and the prognosis remains poor.