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Background: Traditional risk scores for recurrent atrial fibrillation (AF) following catheter ablation utilize readily available clinical and echocardiographic variables and yet have limited discriminatory capacity. Use of data from cardiac imaging and deep learning may help improve accuracy and prediction of recurrent AF after ablation., Methods: We evaluated patients with symptomatic, drug-refractory AF undergoing catheter ablation. All patients underwent pre-ablation cardiac computed tomography (cCT). LAVi was computed using a deep-learning algorithm. In a two-step analysis, random survival forest (RSF) was used to generate prognostic models with variables of highest importance, followed by Cox proportional hazard regression analysis of the selected variables. Events of interest included early and late recurrence., Results: Among 653 patients undergoing AF ablation, the most important factors associated with late recurrence by RSF analysis at 24 (+/-18) months follow-up included LAVi and early recurrence. In total, 5 covariates were identified as independent predictors of late recurrence: LAVi (HR per mL/m 2 1.01 [1.01-1.02]; p  < .001), early recurrence (HR 2.42 [1.90-3.09]; p  < .001), statin use (HR 1.38 [1.09-1.75]; p  = .007), beta-blocker use (HR 1.29 [1.01-1.65]; p  = .043), and adjunctive cavotricuspid isthmus ablation [HR 0.74 (0.57-0.96); p  = .02]. Survival analysis demonstrated that patients with both LAVi >66.7 mL/m 2 and early recurrence had the highest risk of late recurrence risk compared with those with LAVi <66.7 mL/m 2 and no early recurrence (HR 4.52 [3.36-6.08], p  < .001)., Conclusions: Machine learning-derived, full volumetric LAVi from cCT is the most important pre-procedural risk factor for late AF recurrence following catheter ablation. The combination of increased LAVi and early recurrence confers more than a four-fold increased risk of late recurrence., Competing Interests: Authors declare no conflict of interests for this article., (© 2023 The Authors. Journal of Arrhythmia published by John Wiley & Sons Australia, Ltd on behalf of Japanese Heart Rhythm Society.)

We describe the curation, annotation methodology, and characteristics of the dataset used in an artificial intelligence challenge for detection and localization of COVID-19 on chest radiographs. The chest radiographs were annotated by an international group of radiologists into four mutually exclusive categories, including "typical," "indeterminate," and "atypical appearance" for COVID-19, or "negative for pneumonia," adapted from previously published guidelines, and bounding boxes were placed on airspace opacities. This dataset and respective annotations are available to researchers for academic and noncommercial use., (© 2022. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.)

Objectives: Interstitial pneumonia with autoimmune features (IPAF) includes patients with interstitial lung disease with autoimmune features who do not meet criteria for a connective tissue disease (CTD). Previous studies showed a wide variation in the radiologic pattern, pulmonary function and prognosis but there is still limited data on longitudinal outcomes. We aim to describe the long-term pulmonary function, radiological patterns, and survival of IPAF patients and explore a classification based on CTD-like subgroups by using clinical/serologic data., Methods: Retrospective analysis of IPAF patients who were sub-classified into six CTD-(like) subgroups: systemic lupus erythematosus-like, rheumatoid arthritis-like, Sjögren's syndrome-like, scleroderma, myositis-like, and unclassifiable. Linear mixed-effect models were used to compare the change in percent-predicted forced vital capacity (FVC%), percent-predicted diffusion capacity (DLCO%), and six-minute walk distance (SMWD) over time; and survival in the entire cohort and according to CTD-like subgroups and radiological patterns., Results: Fifty-nine patients fulfilled IPAF criteria. FVC%, DLCO%, and SMWD remained stable over time. There was no difference between usual interstitial pneumonia (UIP) versus non-UIP radiologic patterns. Thirty-five patients were sub-classified into CTD-like subgroups. Survival decreased from 79% at 60 months to 53% at 120 months in the entire cohort but was similar among CTD-like subgroups and radiological patterns., Conclusions: Long-term pulmonary function and six-minute walk test remained stable over 36 months in our IPAF cohort. Prognosis and pulmonary function in UIP had similar outcomes compared to non-UIP. Although 40% of IPAF patients could not be sub-classified, our exploratory subclassification stratified 60% of patients into a CTD-like subgroup.

Background: Recent studies have shown that epicardial adipose tissue (EAT) is an independent atrial fibrillation (AF) prognostic marker and has influence on the myocardial function. In computed tomography (CT), EAT volume (EATv) and density (EATd) are parameters that are often used to quantify EAT. While increased EATv has been found to correlate with the prevalence and the recurrence of AF after ablation therapy, higher EATd correlates with inflammation due to arrest of lipid maturation and with high risk of plaque presence and plaque progression. Automation of the quantification task diminishes the variability in readings introduced by different observers in manual quantification and results in high reproducibility of studies and less time-consuming analysis. Our objective is to develop a fully automated quantification of EATv and EATd using a deep learning (DL) framework., Methods: We proposed a framework that consists of image classification and segmentation DL models and performs the task of selecting images with EAT from all the CT images acquired for a patient, and the task of segmenting the EAT from the output images of the preceding task. EATv and EATd are estimated using the segmentation masks to define the region of interest. For our experiments, a 300-patient dataset was divided into two subsets, each consisting of 150 patients: Dataset 1 (41,979 CT slices) for training the DL models, and Dataset 2 (36,428 CT slices) for evaluating the quantification of EATv and EATd., Results: The classification model achieved accuracies of 98% for precision, recall and F 1 scores, and the segmentation model achieved accuracies in terms of mean ( ± std.) and median dice similarity coefficient scores of 0.844 ( ± 0.19) and 0.84, respectively. Using the evaluation set (Dataset 2), our approach resulted in a Pearson correlation coefficient of 0.971 ( R 2 = 0.943) between the label and predicted EATv, and the correlation coefficient of 0.972 ( R 2 = 0.945) between the label and predicted EATd., Conclusions: We proposed a framework that provides a fast and robust strategy for accurate EAT segmentation, and volume (EATv) and attenuation (EATd) quantification tasks. The framework will be useful to clinicians and other practitioners for carrying out reproducible EAT quantification at patient level or for large cohorts and high-throughput projects., Competing Interests: The authors declare no conflict of interest., (Copyright: © 2022 The Author(s). Published by IMR Press.)

Sotorasib is a KRAS G12C inhibitor that recently received approval for use in locally advanced or metastatic KRAS G12C -mutated NSCLC. CodeBreaK100, the phase 2 clinical trial leading to the approval of sotorasib, excluded patients with untreated brain metastases; there have been no reports describing efficacy of sotorasib on untreated brain metastases. We present a case of a patient with active untreated brain metastases with resulting disorientation and weakness who has radiographic response and complete resolution of neurologic symptoms with sotorasib. Our case illustrates the intracranial activity of sotorasib, but additional studies are needed to characterize the intracranial response rate and duration of response in these patients., (© 2022 The Authors.)

Objectives: Cardiac computed tomography (CCT) is a common pre-operative imaging modality to evaluate pulmonary vein anatomy and left atrial appendage thrombus in patients undergoing catheter ablation (CA) for atrial fibrillation (AF). These images also allow for full volumetric left atrium (LA) measurement for recurrence risk stratification, as larger LA volume (LAV) is associated with higher recurrence rates. Our objective is to apply deep learning (DL) techniques to fully automate the computation of LAV and assess the quality of the computed LAV values., Methods: Using a dataset of 85,477 CCT images from 337 patients, we proposed a framework that consists of several processes that perform a combination of tasks including the selection of images with LA from all other images using a ResNet50 classification model, the segmentation of images with LA using a UNet image segmentation model, the assessment of the quality of the image segmentation task, the estimation of LAV, and quality control (QC) assessment., Results: Overall, the proposed LAV estimation framework achieved accuracies of 98% (precision, recall, and F1 score metrics) in the image classification task, 88.5% (mean dice score) in the image segmentation task, 82% (mean dice score) in the segmentation quality prediction task, and R 2 (the coefficient of determination) value of 0.968 in the volume estimation task. It correctly identified 9 out of 10 poor LAV estimations from a total of 337 patients as poor-quality estimates., Conclusions: We proposed a generalizable framework that consists of DL models and computational methods for LAV estimation. The framework provides an efficient and robust strategy for QC assessment of the accuracy for DL-based image segmentation and volume estimation tasks, allowing high-throughput extraction of reproducible LAV measurements to be possible., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Abdulkareem, Brahier, Zou, Taylor, Thomaides, Bergquist, Srichai, Lee, Vargas and Petersen.)

Purpose: This study aimed to evaluate interobserver reproducibility between cardiothoracic radiologists applying the Coronary Artery Disease Reporting and Data System (CAD-RADS) to describe atherosclerotic burden on coronary computed tomography angiography., Methods: Forty clinical computed tomography angiography cases were retrospectively and independently evaluated by 3 attending and 2 fellowship-trained cardiothoracic radiologists using the CAD-RADS lexicon. Radiologists were blinded to patient history and underwent initial training using a practice set of 10 subjects. Interobserver reproducibility was assessed using an intraclass correlation (ICC) on the basis of single-observer scores, absolute agreement, and a 2-way random-effects model. Nondiagnostic studies were excluded. ICC was also performed for CAD-RADS scores grouped by management recommendations for absent (0), nonobstructive (1 to 2), and potentially obstructive (3 to 5) CAD., Results: Interobserver reproducibility was moderate to good (ICC: 0.748, 95% confidence interval [CI]: 0.639-0.842, P<0.0001), with higher agreement among cardiothoracic radiology fellows (ICC: 0.853, 95% CI: 0.730-0.922, P<0.0001) than attending radiologists (ICC: 0.711, 95% CI: 0.568-0.824, P<0.0001). Interobserver reproducibility for clinical management categories was marginally decreased (ICC: 0.692, 95% CI: 0.570-0.802, P<0.0001). The average percent agreement between pairs of radiologists was 84.74%. Percent observer agreement was significantly reduced in the presence (M=62.22%, SD=15.17%) versus the absence (M=80.91%, SD=17.97%) of modifiers, t(37.95)=3.566, P=0.001., Conclusions: Interobserver reliability and agreement with the CAD-RADS terminology are moderate to good in clinical practice. However, further investigations are needed to characterize the causes of interobserver disagreement that may lead to differences in management recommendations., Competing Interests: The authors declare no conflicts of interest., (Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved.)

Intracardiac masses are classified as neoplastic or non-neoplastic. Prognosis varies based on the diagnosis of the mass since treatment options differ greatly. As novel imaging techniques emerge, a multimodality approach to the evaluation of intracardiac masses becomes an important part of non-invasive evaluation prior to potential surgical planning or oncological treatment. The purpose of this article is to compare the available imaging modalities-echocardiography, cardiovascular magnetic resonance, cardiac computed tomography, nuclear imaging, and emerging novel hybrid imaging techniques for future clinical applications-and to review the characteristic features seen on those modalities for the most common intracardiac masses.

Purpose: A few case reports of intracavitary coronary arteries (ICCA) have been reported and only a single case series on the coronary computed tomography angiography (CCTA) prevalence rate of ICCA of the right coronary artery (RCA). We describe several cases of ICCA that were noted incidentally and also determine the overall prevalence rate of anomalous ICCA., Materials and Methods: A retrospective analysis of ICCA was performed consisting of consecutive CCTA cases as well as a group of ICCA from teaching files. To establish a prevalence rate, we reviewed 464 consecutive CCTA referred to our center for transcatheter aortic valve replacement. The presence of ICCA and several imaging features were evaluated., Results: Our cohort comprises a total of 12 patients with ICCA, with 1 patient containing 2 anomalous ICCA. 83.3% of affected patients were adult males, with an average age of 69.8 years. The RCA was the most commonly affected vessel (53.8%). The mean length of the intracavitary segment was 33.4 mm for the RCA and 27 mm for the LAD. No cases involved the left circumflex coronary artery. Six of the cases were identified routinely as part of clinical practice and therefore not included in the prevalence analysis. On review of our transcatheter aortic valve replacement database, there was a 1.3% prevalence rate of ICCA. RCA had a prevalence of 0.4%, whereas LAD had a prevalence of 0.9%., Conclusions: Although rare, our study suggests that ICCA may be more common than previously described. Its presence is important to communicate to clinicians prior to invasive cardiac procedures to prevent potentially catastrophic outcomes.

Purpose of Review: The purpose of this study was to provide an overview of the clinical applications of PET-MR in the setting of cardiac imaging with emphasis on specific scenarios where both techniques together provided added information., Recent Findings: Synergy of cardiac PET and MR fusion may hold similar promise eliminating ionizing radiation and improving tissue contrast. Future development of new hybrid scanners, use of new imaging tracers, and clinical applications are significant factors which will influence its use. Both positron emission tomography (PET) and cardiac magnetic resonance imaging (CMR) provide important anatomic and physiologic information with regard to the heart. Being able to combine the data from these two examinations in a hybrid technique allows for a more complete evaluation of cardiac pathology. While hybrid PET-CT has already established the utility of a combined imaging approach, the use of CMR in lieu of CT allows for elimination of ionizing radiation and for improved tissue contrast.

This article reviews current and evolving concepts in the diagnosis of penetrating diaphragmatic injury with multidetector CT (MDCT). As criteria for nonoperative management in the setting of penetrating trauma become more inclusive, confident exclusion of penetrating diaphragmatic injury (PDI) has become imperative. Diagnostic performance of MDCT for PDI has improved substantially with the use of thin sections and multiplanar reformats. Evaluation of injury trajectory in nonstandard planes using 3D post-processing software can aid in the diagnosis. Contiguous injury and transdiaphragmatic trajectory are the best predictors of PDI. Careful appraisal of the diaphragm for defects should be undertaken in all patients with thoracoabdominal penetrating trauma.