Non-contrast computed tomography findings for identification of chronically occluded coronary artery bypass grafts.

BACKGROUND: Detecting occlusions of coronary artery bypass grafts using non-contrast computed tomography (CT) series is understudied and underestimated. PURPOSE: To evaluate morphological findings for the diagnosis of chronic coronary artery bypass graft occlusion on non-contrast CT and investigate performance statistics for potential use cases. MATERIAL AND METHODS: Seventy-three patients with coronary artery bypass grafts who had CT angiography of the chest (non-contrast and arterial phases) were retrospectively included. Two readers applied pre-set morphologic findings to assess the patency of a bypass graft on non-contrast series. These findings included vessel shape (linear-band like), collapsed lumen and surgical graft marker without a visible vessel. Performance was tested using the simultaneously acquired arterial phase series as the ground truth. RESULTS: The per-patient diagnostic accuracy for occlusion was 0.890 (95% confidence interval = 0.795-0.951). Venous grafts overall had an 88% accuracy. None of the left internal mammary artery to left anterior descending artery arterial graft occlusions were detected. The negative likelihood ratio for an occluded graft that is truly patent was 0.121, demonstrating a true post-test probability of 97% for identifying a patent graft as truly patent given a prevalence of 20% occlusion at a median 8.4 years post-surgery. Neither years post-surgery, nor number of vessels was associated with a significant decrease in reader accuracy. CONCLUSION: Evaluation of coronary bypass grafts for chronic occlusion on non-contrast CT based off vessel morphology is feasible and accurate for venous grafts. Potential use cases include low-intermediate risk patients with chest pain or shortness of breath for whom non-contrast CT was ordered, or administration of iodine-based contrast is contraindicated.

Quantitative myocardial T2 mapping adds value to Japanese circulation society diagnostic criteria for active cardiac sarcoidosis.

Noninvasive identification of active myocardial inflammation in patients with cardiac sarcoidosis plays a key role in management but remains elusive. T2 mapping is a proposed solution, but the added value of quantitative myocardial T2 mapping for active cardiac sarcoidosis is unknown. Retrospective cohort analysis of 56 sequential patients with biopsy-confirmed extracardiac sarcoidosis who underwent cardiac MRI for myocardial T2 mapping. The presence or absence of active myocardial inflammation in patients with CS was defined using a modified Japanese circulation society criteria within one month of MRI. Myocardial T2 values were obtained for the 16 standard American Heart Association left ventricular segments. The best model was selected using logistic regression. Receiver operating characteristic curves and dominance analysis were used to evaluate the diagnostic performance and variable importance. Of the 56 sarcoidosis patients included, 14 met criteria for active myocardial inflammation. Mean basal T2 value was the best performing model for the diagnosis of active myocardial inflammation in CS patients (pR2 = 0.493, AUC = 0.918, 95% CI 0.835-1). Mean basal T2 value > 50.8 ms was the most accurate threshold (accuracy = 0.911). Mean basal T2 value + JCS criteria was significantly more accurate than JCS criteria alone (AUC = 0.981 vs. 0.887, p = 0.017). Quantitative regional T2 values are independent predictors of active myocardial inflammation in CS and may add additional discriminatory capability to JCS criteria for active disease.

Tumor burden of lung metastases at initial staging in breast cancer patients detected by artificial intelligence as a prognostic tool for precision medicine.

BACKGROUND: Determination of the total number and size of all pulmonary metastases on chest CT is time-consuming and as such has been understudied as an independent metric for disease assessment. A novel artificial intelligence (AI) model may allow for automated detection, size determination, and quantification of the number of pulmonary metastases on chest CT. OBJECTIVE: To investigate the utility of a novel AI program applied to initial staging chest CT in breast cancer patients in risk assessment of mortality and survival. METHODS: Retrospective imaging data from a cohort of 226 subjects with breast cancer was assessed by the novel AI program and the results validated by blinded readers. Mean clinical follow-up was 2.5 years for outcomes including cancer-related death and development of extrapulmonary metastatic disease. AI measurements including total number of pulmonary metastases and maximum nodule size were assessed by Cox-proportional hazard modeling and adjusted survival. RESULTS: 752 lung nodules were identified by the AI program, 689 of which were identified in 168 subjects having confirmed lung metastases (Lmet+) and 63 were identified in 58 subjects without confirmed lung metastases (Lmet-). When compared to the reader assessment, AI had a per-patient sensitivity, specificity, PPV and NPV of 0.952, 0.639, 0.878, and 0.830. Mortality in the Lmet + group was four times greater compared to the Lmet-group (p = 0.002). In a multivariate analysis, total lung nodule count by AI had a high correlation with overall mortality (OR 1.11 (range 1.07-1.15), p < 0.001) with an AUC of 0.811 (R2 = 0.226, p < 0.0001). When total lung nodule count and maximum nodule diameter were combined there was an AUC of 0.826 (R2 = 0.243, p < 0.001). CONCLUSION: Automated AI-based detection of lung metastases in breast cancer patients at initial staging chest CT performed well at identifying pulmonary metastases and demonstrated strong correlation between the total number and maximum size of lung metastases with future mortality. CLINICAL IMPACT: As a component of precision medicine, AI-based measurements at the time of initial staging may improve prediction of which breast cancer patients will have negative future outcomes.

Deep learning model to quantify left atrium volume on routine non-contrast chest CT and predict adverse outcomes.

BACKGROUND: Low-dose computed tomography (LDCT) are performed routinely for lung cancer screening. However, a large amount of nonpulmonary data from these scans remains unassessed. We aimed to validate a deep learning model to automatically segment and measure left atrial (LA) volumes from routine NCCT and evaluate prediction of cardiovascular outcomes. METHODS: We retrospectively evaluated 273 patients (median age 69 years, 55.5% male) who underwent LDCT for lung cancer screening. LA volumes were quantified by three expert cardiothoracic radiologists and a prototype AI algorithm. LA volumes were then indexed to the body surface area (BSA). Expert and AI LA volume index (LAVi) were compared and used to predict cardiovascular outcomes within five years. Logistic regression with appropriate univariate statistics were used for modelling outcomes. RESULTS: There was excellent correlation between AI and expert results with an LAV intraclass correlation of 0.950 (0.936-0.960). Bland-Altman plot demonstrated the AI underestimated LAVi by a mean 5.86 â€‹mL/m2. AI-LAVi was associated with new-onset atrial fibrillation (AUC 0.86; OR 1.12, 95% CI 1.08-1.18, p â€‹< â€‹0.001), HF hospitalization (AUC 0.90; OR 1.07, 95% CI 1.04-1.13, p â€‹< â€‹0.001), and MACCE (AUC 0.68; OR 1.04, 95% CI 1.01-1.07, p â€‹= â€‹0.01). CONCLUSION: This novel deep learning algorithm for automated measurement of LA volume on lung cancer screening scans had excellent agreement with manual quantification. AI-LAVi is significantly associated with increased risk of new-onset atrial fibrillation, HF hospitalization, and major adverse cardiac and cerebrovascular events within 5 years.

Diagnostic Accuracy and Performance of Artificial Intelligence in Detecting Lung Nodules in Patients With Complex Lung Disease: A Noninferiority Study.

OBJECTIVES: The aim of the study is to investigate the performance of artificial intelligence (AI) convolutional neural networks (CNN) in detecting lung nodules on chest computed tomography of patients with complex lung disease, and demonstrate its noninferiority when compared against an experienced radiologist through clinically relevant assessments. METHODS: A CNN prototype was used to retrospectively evaluate 103 complex lung disease cases and 40 control cases without reported nodules. Computed tomography scans were blindly evaluated by an expert thoracic radiologist; a month after initial analyses, 20 positive cases were re-evaluated with the assistance of AI. For clinically relevant applications: (1) AI was asked to classify each patient into nodules present or absent and (2) AI results were compared against standard radiology reports. Standard statistics were performed to determine detection performance. RESULTS: AI was, on average, 27 seconds faster than the expert and detected 8.4% of nodules that would have been missed. AI had a sensitivity of 67.7%, similar to an accuracy reported for experienced radiologists. AI correctly classified each patient (nodules present/absent) with a sensitivity of 96.1%. When matched against radiology reports, AI performed with a sensitivity of 89.4%. Control group assessment demonstrated an overall specificity of 82.5%. When aided by AI, the expert decreased the average assessment time per case from 2:44 minutes to 35.7 seconds, while reporting an overall increase in confidence. CONCLUSION: In a group of patients with complex lung disease, the sensitivity of AI is similar to an experienced radiologist and the tool helps detect previously missed nodules. AI also helps experts analyze for lung nodules faster and more confidently, a feature that is beneficial to patients and favorable to hospitals due to increased patient load and need for shorter turnaround times.

Role of Gender on the Outcomes of ST-Elevation Myocardial Infarction Patients Following Primary Coronary Angioplasty.

Background There are considerable differences in the prevalence of coronary artery disease (CAD) and its cardiovascular risk factors between men and women. Due to the significance of gender as a factor that potentially affects cardiovascular disorders and patient outcomes, the present study aimed to assess the baseline characteristics and outcomes of CAD patients in terms of gender distribution. Methods All consecutive patients diagnosed with ST-elevation myocardial infarction (MI) who had undergone primary percutaneous coronary intervention (PCI) in the previous two years in a comprehensive cardiology center were included. Data were retrospectively collected from the hospital record files. Color Doppler echocardiography, valvular involvement, and the type of coronary vessel involvement were also evaluated. Results In total, 557 consecutive patients (437 men and 120 women) were included with a mean age of 59.37 ± 26.23 years and 64.07 ± 11.60 years for men and women, respectively (p = 0.004). The prevalence of mitral regurgitation (MR) and tricuspid regurgitation (TR) was significantly higher among women than men. Conclusion Female patients who suffered from CAD and underwent PCI were older than men. Also, ischemic mitral regurgitation (MR) and tricuspid regurgitation (TR) were more prevalent among women, while smoking was more prevalent among men.

Automated detection of lung nodules and coronary artery calcium using artificial intelligence on low-dose CT scans for lung cancer screening: accuracy and prognostic value.

BACKGROUND: Artificial intelligence (AI) in diagnostic radiology is undergoing rapid development. Its potential utility to improve diagnostic performance for cardiopulmonary events is widely recognized, but the accuracy and precision have yet to be demonstrated in the context of current screening modalities. Here, we present findings on the performance of an AI convolutional neural network (CNN) prototype (AI-RAD Companion, Siemens Healthineers) that automatically detects pulmonary nodules and quantifies coronary artery calcium volume (CACV) on low-dose chest CT (LDCT), and compare results to expert radiologists. We also correlate AI findings with adverse cardiopulmonary outcomes in a retrospective cohort of 117 patients who underwent LDCT. METHODS: A total of 117 patients were enrolled in this study. Two CNNs were used to identify lung nodules and CACV on LDCT scans. All subjects were used for lung nodule analysis, and 96 subjects met the criteria for coronary artery calcium volume analysis. Interobserver concordance was measured using ICC and Cohen's kappa. Multivariate logistic regression and partial least squares regression were used for outcomes analysis. RESULTS: Agreement of the AI findings with experts was excellent (CACV ICC = 0.904, lung nodules Cohen's kappa = 0.846) with high sensitivity and specificity (CACV: sensitivity = .929, specificity = .960; lung nodules: sensitivity = 1, specificity = 0.708). The AI findings improved the prediction of major cardiopulmonary outcomes at 1-year follow-up including major adverse cardiac events and lung cancer (AUCMACE = 0.911, AUCLung Cancer = 0.942). CONCLUSION: We conclude the AI prototype rapidly and accurately identifies significant risk factors for cardiopulmonary disease on standard screening low-dose chest CT. This information can be used to improve diagnostic ability, facilitate intervention, improve morbidity and mortality, and decrease healthcare costs. There is also potential application in countries with limited numbers of cardiothoracic radiologists.

Predictive value of initial imaging and staging with long-term outcomes in young adults diagnosed with colorectal cancer.

PURPOSE: To evaluate how initial abdominopelvic CT findings and staging correlate with outcomes in a cohort of patients aged 18-40 years. METHODS: We evaluated all young adult patients at a single tertiary center diagnosed with histopathologically confirmed CRC who also had CT of the abdomen and pelvis at the time of initial diagnosis. Demographics, symptoms, CT findings, staging, treatments, and outcomes at 1 year and 5 years were recorded. RESULTS: Of 91 patients who met initial inclusion criteria, 81.8% had a mass present on CT, with an average size of 4.8 cm ± 2.9. A majority of patients were surgical stage III or IV (64.3%). Advanced AJCC stage was more common with rectal tumors and metastatic disease on initial CT (p < 0.0001). In a subgroup analysis, almost all patients initially staged 4A or higher had progression of disease. At the final follow-up visit, by RECIST 1.1 criteria, 58.8% had progressive disease, 35.3% complete response, and 3.9% stable disease. The overall 5-year survival rate in this subgroup was 40% with lower survival probability with increasing stage (p = 0.0001). CONCLUSION: Most young adult patients presented with large tumors on imaging, increasing the likelihood of identification on CT. Tumors initially presenting in the rectum with enlarged lymph nodes and/or with distant metastases on CT were more often associated with advanced surgical stage and poorer prognosis. A majority of patients presented at an advanced stage, most commonly stage 4A, and had progression of disease at follow-up.

The Future of Concurrent Automated Coronary Artery Calcium Scoring on Screening Low-Dose Computed Tomography.

Low-dose computed tomography (LDCT) has been extensively validated for lung cancer screening in selected patient populations. Additionally, the use of gated cardiac CT to assess coronary artery calcium (CAC) burden has been validated to determine a patient's risk for major cardiovascular adverse events. This is typically performed by calculating an Agatston score based on density and overall burden of calcified plaque within the coronary arteries. Patients that qualify for LDCT for lung cancer screening commonly share major risk factors for coronary artery disease and would frequently benefit from an additional gated cardiac CT for the assessment of CAC. Given the widespread use of LDCT for lung cancer screening, we evaluated current literature regarding the use of non-gated chest CT, specifically LDCT, for the detection and grading of coronary artery calcifications. Additionally, given the evolving and increasing use of artificial intelligence (AI) in the interpretation of radiologic studies, current literature for the use of AI in CAC assessment was reviewed.  We reviewed primary scientific literature dating up to April 2020 using Pubmed and Google Scholar, with the search terms low dose CT, lung cancer screening, coronary artery calcium, EKG/cardiac gated CT, deep learning, machine learning, and AI. These publications were then independently evaluated by each member of our team. Overall, there was a consensus within these papers that LDCT for lung cancer screening plays a role in the evaluation of CAC. Most studies note the inherent problems with the evaluation of the density of coronary calcifications on LDCT to give an accurate numeric calcium or Agatston score. The current method of evaluating CAC on LDCT involves using a qualitative categorical system (none, mild, moderate, or severe). When performed by cardiac imaging experts, this method broadly correlates with traditional CAC score groups (0, 1 to 100, 101 to 400, and > 400). Furthermore, given the high sensitivity of a properly protocolled LDCT for coronary calcium, a negative study for CAC precludes the need for a dedicated gated CT assessment. However, qualitative methods are not as accurate or reproducible when performed by general radiologists. The implementation of AI in the LDCT screening process has the potential to give a quantifiable and reproducible numeric value to the calcium score, based on whole heart volume scoring of calcium. This more closely aligns with the Agatston score and serves as a better guide for treatment and risk assessment using current guidelines. We conclude that CAC should be assessed on all LDCT performed for lung cancer screening and that a qualitative categorical scoring system should be provided in the impression for each patient. Early studies involving AI for the assessment of CAC are promising, but more extensive studies are needed before a final recommendation for its use can be given. The implementation of an accurate, automated AI CAC assessment tool would improve radiologist compliance and ease of overall workflow. Ultimately, the potential end result would be improved turnaround time, better patient outcomes, and reduced healthcare costs by maximizing preventative care in this high-risk population.

Fat-Finding Mission: Primary Pleomorphic Liposarcoma of the Heart and Pericardium.

Primary cardiac liposarcomas are rare tumors with a poor prognosis and no well-defined imaging characteristics or treatment guidelines. Here, we present a case of primary pleomorphic liposarcoma of the heart and pericardium with multimodality imaging findings and our institution's treatment approach. (Level of Difficulty: Intermediate.).

Anomalous Coronary Anatomy with Fistula Diagnosed on Coronary Computed Tomography Angiography.

Anomalous coronary vasculature is a rare finding among the general population. Identifying such cases is important for preventing adverse outcomes such as sudden cardiac death. We present two rare cases of aberrant coronary anatomy. In Case 1, a 4-year-old male who presented with non-exertional chest pain was found to have anomalous coronary architecture on echocardiogram. Coronary computed tomography angiogram (CCTA) confirmed an anomalous origin of the left coronary artery from the right coronary sinus with a malignant interarterial course and myocardial bridging of the left anterior descending (LAD) artery. The patient underwent a successful surgical correction of the defects. In Case 2, a full-term infant female was born with a hypoplastic right ventricle and pulmonary atresia. CCTA showed a large fistula originating from the coronary sinus on the left that drained into the superior aspect of the mid right ventricular cavity, an anomalous bridge between the left and right atrial appendages, and five fistulous connections between various vessels. The patient was transferred to another facility for cardiac transplant.

Left-sided Inferior Vena Cava with Hemiazygos Continuation to Left Superior Vena Cava.

Congenital anomalies of superior (SVC) and inferior vena cava (IVC) are not uncommon and usually incidentally recognized. The normal embryogenesis is a complex process involving the formation of several anastomoses. Failure of certain vessels to develop or regress results in numerous caval variations and anomalies. Although these are usually without significant clinical implications, awareness of these anomalies is necessary to avoid diagnostic pitfalls and suggest the presence of other abnormalities and for the planning of vascular intervention or surgery. We present a very rare, caval anomaly, a left-sided IVC with hemiazygos continuation to left SVC in the absence of right SVC.

Deep learning beyond cats and dogs: recent advances in diagnosing breast cancer with deep neural networks.

Deep learning has demonstrated tremendous revolutionary changes in the computing industry and its effects in radiology and imaging sciences have begun to dramatically change screening paradigms. Specifically, these advances have influenced the development of computer-aided detection and diagnosis (CAD) systems. These technologies have long been thought of as "second-opinion" tools for radiologists and clinicians. However, with significant improvements in deep neural networks, the diagnostic capabilities of learning algorithms are approaching levels of human expertise (radiologists, clinicians etc.), shifting the CAD paradigm from a "second opinion" tool to a more collaborative utility. This paper reviews recently developed CAD systems based on deep learning technologies for breast cancer diagnosis, explains their superiorities with respect to previously established systems, defines the methodologies behind the improved achievements including algorithmic developments, and describes remaining challenges in breast cancer screening and diagnosis. We also discuss possible future directions for new CAD models that continue to change as artificial intelligence algorithms evolve.

Funny Lumps, Flaming Pheo, and a Broken Heart: A Rare Case of Pheochromocytoma.

Pheochromocytoma is the underlying etiology in 0.1% of hypertensive cases. However, it may be present in up to 5.7% of patients with neurofibromatosis I (NF1). The burst of catecholamines inherent in pheochromocytoma has significant effects on the mechanical and electrical activity of the myocardium. Different theories have been postulated for myocardial stunning in patients with pheochromocytoma that include microvascular spasm, impaired fatty acid metabolism, increased production of oxygen-derived free radicals and dynamic left ventricular mid-cavity obstruction. QT interval prolongation is seen in 16% to 35% of patients with pheochromocytoma. Takotsubo cardiomyopathy (TS) is now being increasingly identified and it may be responsible for up to 40% of cases of acute catecholamine cardiomyopathy. These manifestations may sometimes precede or cloud the typical triad of a headache, sweating, and tachycardia. We herein present a case of a 42-year-old female with a unique combination of QT prolongation, torsades de pointes, and TS caused by pheochromocytoma in the background of NF1. All these complications are potentially reversible with the removal of the underlying adrenal tumor, underscoring the importance of a high suspicion for pheochromocytoma in patients with NF1.

Invasive Cardiac Lipoma: a case report and review of literature.

BACKGROUND: Cardiac lipomas are rare benign tumors of the heart. They are usually asymptomatic and are thus most often diagnosed on autopsies. Symptoms, when present, depend upon the location within the heart. Typical locations are the endocardium of the right atrium and the left ventricle. Diagnostic modality of choice is cardiac MRI. Treatment guidelines have not yet been established due to the very low prevalence of these tumors and are thus guided by the patient's symptomatology. CASE PRESENTATION: We describe a case of an invasive cardiac lipoma, wherein the initial symptom of the patient was shortness of breath. Although the echocardiogram visualized the tumor in the right atrium, a cardiac MRI was performed for better tissue characterization. The MRI revealed a large, fat containing, septated mass in the right atrium with invasion into the inter-atrial septum and inferior cavoatrial junction. There was also invasion of the coronary sinus along the inferior and left lateral aspect of the posterior atrioventricular groove. Although the mass appeared to represent a lipoma by imaging characteristics, the unusual extension into the coronary sinus led to consideration of a low-grade liposarcoma in the differential. Thus a pre-operative biopsy was performed along with MDM2 gene amplification to rule out a liposarcoma preceding surgical excision. CONCLUSION: Cardiac lipomas are well-characterized on cardiac MRI, which is the diagnostic modality of choice. Typical locations are the right atrium and the left ventricle. However, in those with atypical features such as invasion of the coronary sinus, pre-operative biopsy for histopathologic confirmation is imperative to exclude well-differentiated liposarcoma. Our patient with a simple lipoma underwent partial resection to relieve symptoms. We discuss prognosis and treatment of symptomatic cardiac lipomas.

CT-guided Native Medical Renal Biopsy: Cortical Tangential versus Non-Tangential Approaches-A Comparison of Efficacy and Safety.

Purpose To review a single-center experience with the cortical tangential approach during computed tomography (CT)-guided native medical renal biopsy and to evaluate its efficacy and safety compared with those of a non-cortical tangential approach. Materials and Methods This retrospective study received institutional review board approval, with a waiver of the HIPAA requirement for informed consent. The number of cores, glomeruli, and complications were reviewed in 431 CT-guided medical renal biopsies performed between July 2007 and September 2015. A biopsy followed a cortical tangential approach if the needle path was parallel to the renal cortical surface, at a depth closer to the renal capsule than the renal pelvic fat. A sample was considered adequate if the biopsy yielded at least 10 glomeruli at light microscopy, one glomerulus at immunofluorescence microscopy, and one glomerulus at electron microscopy. The χ2 test, the t test, the Mann-Whitney test, and logistic regression modeling of sample adequacy were performed. Results One hundred fifty-six (36%) of 431 biopsies were performed with the cortical tangential approach. More cores were obtained for the cortical tangential group (2.6 vs 2.4, P = .001); biopsy needle gauge was not significantly different (P = .076). More adequate samples were obtained in the cortical tangential group (66.7% vs 49.8%, P = .001), with more glomeruli (23 vs 16, P = .014). Results were significant after controlling for needle gauge and number of cores (P = .008). The cortical tangential group had fewer complications (1.9% vs 7.3%, P = .018). Conclusion The cortical tangential approach, when applied to CT-guided native medical renal biopsies, results in higher rates of sample adequacy and lower rates of postprocedural complications. © RSNA, 2016.

Cardiac MR findings and potential diagnostic pitfalls in patients evaluated for arrhythmogenic right ventricular cardiomyopathy.

Arrhythmogenic right ventricular cardiomyopathy (ARVC) is a familial cardiomyopathy characterized by fibrofatty replacement of the myocardium, ventricular tachycardia, and ventricular dysfunction that affects primarily the right ventricle (RV). This disease is not common but can be seen more frequently in young adults, and clinical manifestations range from no symptoms to lethal arrhythmia and sudden death. The diagnosis of ARVC is challenging and is based on the recently revised international task force criteria. Given the strengths of cardiac magnetic resonance (MR) imaging for depicting the RV, this modality plays an important role in the diagnosis of ARVC. Functional and structural abnormalities of the RV depicted with cardiac MR imaging constitute major and minor criteria in the revised task force criteria. Since the ARVC program was established at our center in 1998, there has been an increased awareness of a number of normal variants that are commonly misinterpreted as showing evidence for ARVC. On the basis of our clinical experience, the overdiagnosis of ARVC appears to reflect two fundamental problems: (a) a lack of awareness of diagnostic criteria that identify major and minor variables to be used for the diagnosis of ARVC, and (b) a lack of familiarity with the normal variants and mimics that may be misinterpreted as showing evidence of ARVC. The purpose of this article is to review the typical patterns of ventricular involvement in ARVC at cardiac MR imaging and to compare those with the patterns of normal variants and other diseases that can mimic ARVC. Online supplemental material is available for this article.

Incremental value of cardiac magnetic resonance imaging in arrhythmic risk stratification of arrhythmogenic right ventricular dysplasia/cardiomyopathy-associated desmosomal mutation carriers.

OBJECTIVES: The aim of this study was to identify the incremental value and optimal role of cardiac magnetic resonance (CMR) imaging in arrhythmic risk stratification of arrhythmogenic right ventricular dysplasia/cardiomyopathy (ARVD/C)-associated desmosomal mutation carriers without histories of sustained ventricular arrhythmia. BACKGROUND: Risk stratification of ARVD/C mutation carriers is challenging. METHODS: Sixty-nine patients (mean age 27.0 ± 15.3 years, 42% men) harboring ARVD/C-associated pathogenic mutations (83% plakophilin 2) without prior sustained ventricular arrhythmias were included. Electrocardiographic and 24-h Holter monitoring findings closest to presentation were analyzed for electrical abnormalities per revised task force criteria. CMR studies were done to identify abnormal cardiac structure and function according to the revised task force criteria. RESULTS: Overall, 42 patients (61%) presented with electrical abnormalities on the basis of electrocardiography and Holter monitoring, of whom 20 (48%) had abnormal results on CMR. Only 1 of 27 patients (4%) without electrical abnormalities at initial evaluation had abnormal CMR results. Over a mean follow-up period of 5.8 ± 4.4 years, 11 patients (16%) experienced sustained ventricular arrhythmias, exclusively in patients with both electrical abnormalities (electrocardiography and/or Holter monitoring) and abnormal CMR results. CONCLUSIONS: These results suggest that electrical abnormalities on electrocardiography and Holter monitoring precede detectable structural abnormalities in ARVD/C mutation carriers. Therefore, evaluation of cardiac structure and function using CMR is probably not necessary in the absence of baseline electrical abnormalities. Among ARVD/C mutation carriers, the presence of both electrical and CMR abnormalities identifies patients at high risk for events and thus patients who might benefit from prophylactic implantable cardioverter-defibrillator placement.