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Background: Detecting cardiac thrombus in patients with acute ischemic stroke is crucial in determine stroke etiology and predict prognosis. However, the prevalence of cardiac thrombus in patients with acute ischemic stroke is unclear. Object: This study aimed to evaluate the prevalence of cardiac thrombus detected by cardiac computed tomography angiography (CCTA) in patients with acute ischemic stroke through a meta-analysis. Methods: Embase, Web of Science, MEDLINE, and CENTRAL were searched from January 1, 2000, to May 1, 2024. We included observational studies enrolling patients who underwent CCTA within 1 month following acute ischemic stroke, and reporting the incidence of cardiac thrombi on CCTA. Meta-analysis was performed using random effects models. Results: Twenty-six studies involving 4,516 patients were identified. The pooled prevalence of cardiac thrombus detected on CCTA in patients with acute ischemic stroke was 0.08 (95% confidence interval [CI]: 0.06-0.11). Interstudy heterogeneity was high (I2 = 88%). Among stroke type, the prevalence of atrial fibrillation, timing of CCTA and CCTA technology, the prevalence of atrial fibrillation was the only factor associated with cardiac thrombi prevalence detected by CCTA. However, atrial fibrillation was not documented in 41.5% of the patients with cardiac thrombi. Conclusion: CCTA is a useful non-invasive imaging approach for detecting cardiac thrombus in patients with acute ischemic stroke, which might be helpful to determine the stroke etiology. [ABSTRACT FROM AUTHOR]

Coronary CT angiography (CCTA) is now endorsed by all major cardiology guidelines for the investigation of chest pain and assessment for coronary artery disease (CAD) in appropriately selected patients. CAD is a leading cause of morbidity and mortality. There is extensive literature to support CCTA diagnostic and prognostic value both for stable and acute symptoms. It enables rapid and cost-effective rule-out of CAD, and permits quantification and characterization of coronary plaque and associated significance. In this comprehensive review, we detail the road traveled as CCTA evolved to include quantitative assessment of plaque stenosis and extent, characterization of plaque characteristics including high-risk features, functional assessment including fractional flow reserve-CT (FFR-CT), and CT perfusion techniques. The state of current guideline recommendations and clinical applications are reviewed, as well as future directions in the rapidly advancing field of CT technology, including photon counting and applications of artificial intelligence (AI). [ABSTRACT FROM AUTHOR]

Two letters to the editor are presented in response to the article "International Early Lung Cancer Action Program Investigators. Definition of a positive test result in computed tomography screening for lung cancer: a cohort study" in a 2013 issue, along with a response from the article's authors.

Background: Negative CCTA can effectively exclude significant CAD, eliminating the need for further noninvasive or invasive testing. However, in the presence of severe CAD, the accuracy declines, thus necessitating additional testing. The aim of our study was to evaluate the diagnostic performance of noninvasive cFFR derived from CCTA, compared to ICA in detecting hemodynamically significant stenoses in participants with high CAC scores (>400). Methods: This study included 37 participants suspected of having CAD who underwent CCTA and ICA. CAC was calculated and cFFR analyses were performed using an on-site machine learning-based algorithm. Diagnostic accuracy parameters of CCTA and cFFR were calculated on a per-vessel level. Results: The median total CAC score was 870, with an IQR of 642–1370. Regarding CCTA, sensitivity and specificity for RCA were 60% and 67% with an AUC of 0.639; a LAD of 87% and 50% with an AUC of 0.688; an LCX of 33% and 90% with an AUC of 0.617, respectively. Regarding cFFR, sensitivity and specificity for RCA were 60% and 61% with an AUC of 0.606; a LAD of 75% and 54% with an AUC of 0.647; an LCX of 50% and 77% with an AUC of 0.647. No significant differences between AUCs of coronary CTA and cFFR for each vessel were found. Conclusions: Our results showed poor diagnostic accuracy of CCTA and cFFR in determining significant ischemia-related lesions in participants with high CAC scores when compared to ICA. Based on our results and study limitations we cannot exclude cFFR as a method for determining significant stenoses in people with high CAC. A key issue is accurate and detailed lumen segmentation based on good-quality CCTA images. [ABSTRACT FROM AUTHOR]

CT angiography has become, in recent years, a main evaluating modality for patients with coronary artery disease (CAD). Recent advancements in the field have allowed us to identity not only the presence of obstructive disease but also the characteristics of identified lesions. High-risk coronary atherosclerotic plaques are identified in CT angiographies via a number of specific characteristics and may provide prognostic and therapeutic implications, aiming to prevent future ischemic events via optimizing medical treatment or providing coronary interventions. In light of new evidence evaluating the safety and efficacy of intervening in high-risk plaques, even in non-flow-limiting disease, we aim to provide a comprehensive review of the diagnostic algorithms and implications of plaque vulnerability in CT angiography, identify any differences with invasive imaging, analyze prognostic factors and potential future therapeutic options in such patients, as well as discuss new frontiers, including intervening in non-flow-limiting stenoses and the role of CT angiography in patient stratification. [ABSTRACT FROM AUTHOR]

Historically, cardiovascular prevention has been predominantly focused on stress-induced ischemia, but recent trials have challenged this paradigm, highlighting the emerging role of vulnerable, non-flow-limiting coronary plaques, leading to a shift towards integrating plaque morphology with functional data into risk prediction models. Coronary computed tomography angiography (CCTA) represents a high-resolution, low-risk, and largely available non-invasive modality for the precise delineation of plaque composition, morphology, and inflammatory activity, further enhancing our ability to stratify high-risk plaque and predict adverse cardiovascular outcomes. Coronary artery calcium (CAC) scoring, derived from CCTA, has emerged as a promising tool for predicting future cardiovascular events in asymptomatic individuals, demonstrating incremental prognostic value beyond traditional cardiovascular risk factors in terms of myocardial infarction, stroke, and all-cause mortality. Additionally, CCTA-derived information on adverse plaque characteristics, geometric characteristics, and hemodynamic forces provides valuable insights into plaque vulnerability and seems promising in guiding revascularization strategies. Additionally, non-invasive assessments of epicardial and pericoronary adipose tissue (PCAT) further refine risk stratification, adding prognostic significance to coronary artery disease (CAD), correlating with plaque development, vulnerability, and rupture. Moreover, CT imaging not only aids in risk stratification but is now emerging as a screening tool able to monitor CAD progression and treatment efficacy over time. Thus, the integration of CAC scoring and PCAT evaluation into risk stratification algorithms, as well as the identification of high-risk plaque morphology and adverse geometric and hemodynamic characteristics, holds promising results for guiding personalized preventive interventions, helping physicians in identifying high-risk individuals earlier, tailoring lifestyle and pharmacological interventions, and improving clinical outcomes in their patients. [ABSTRACT FROM AUTHOR]

In this narrative review, we investigate the essential role played by the computed tomography Aortic Valve Calcium Score (AVCS) in the cardiovascular diagnostic landscape, with a special focus on its implications for clinical practice and scientific research. Calcific aortic valve stenosis is the most prevalent type of aortic stenosis (AS) in industrialized countries, and due to the aging population, its prevalence is increasing. While transthoracic echocardiography (TTE) remains the gold standard, AVCS stands out as an essential complementary tool in evaluating patients with AS. The advantage of AVCS is its independence from flow; this allows for a more precise evaluation of patients with discordant findings in TTE. Further clinical applications of AVCS include in the assessment of patients before transcatheter aortic valve replacement (TAVR), as it helps in predicting outcomes and provides prognostic information post-TAVR. Additionally, we describe different AVCS thresholds regarding gender and the anatomical variations of the aortic valve. Finally, we discuss various scientific studies where AVCS was applied. As AVCS has some limitations, due to the pathophysiologies of AS extending beyond calcification and gender differences, scientists strive to validate contrast-enhanced AVCS. Furthermore, research on developing radiation-free methods of measuring calcium content is ongoing. [ABSTRACT FROM AUTHOR]

Infective endocarditis is a rare disease with an increasing incidence and an unaltered high mortality rate, despite medical development. Imaging plays an integrative part in the diagnosis of infective endocarditis, with echocardiography as the initial diagnostic test. Research data in the utility of cardiac computed tomography (CCT) in the diagnostic algorithm of IE are rising, which indicates its importance in detection of IE-related lesion along with the exclusion of coronary artery disease. The latest 2023 European Society of Cardiology Guidelines in the management of IE classified CCT as class of recommendation I and level of evidence B in detection of both valvular and paravalvular lesions in native and prosthetic valve endocarditis. This review article provides a comprehensive and contemporary review of the role of CCT in the diagnosis of IE, the optimization of acquisition protocols, the morphology characteristics of IE-related lesions, the published data of the diagnostic performance of CCT in comparison to echocardiography as the state-of-art method, as well as the limitations and future possibilities. [ABSTRACT FROM AUTHOR]

Chronic coronary syndrome (CCS) is one of the leading cardiovascular causes of morbidity, mortality, and use of medical resources. After the introduction by international guidelines of the same level of recommendation to non-invasive imaging techniques in CCS evaluation, a large debate arose about the dilemma of choosing anatomical (with coronary computed tomography angiography (CCTA)) or functional imaging (with stress echocardiography (SE), cardiovascular magnetic resonance (CMR), or nuclear imaging techniques) as a first diagnostic evaluation. The determinant role of the atherosclerotic burden in defining cardiovascular risk and prognosis more than myocardial inducible ischemia has progressively increased the use of a first anatomical evaluation with CCTA in a wide range of pre-test probability in CCS patients. Functional testing holds importance, both because the role of revascularization in symptomatic patients with proven ischemia is well defined and because functional imaging, particularly with stress cardiac magnetic resonance (s-CMR), gives further prognostic information regarding LV function, detection of myocardial viability, and tissue characterization. Emerging techniques such as stress computed tomography perfusion (s-CTP) and fractional flow reserve derived from CT (FFRCT), combining anatomical and functional evaluation, appear capable of addressing the need for a single non-invasive examination, especially in patients with high risk or previous revascularization. Furthermore, CCTA in peri-procedural planning is promising to acquire greater importance in the non-invasive planning and guiding of complex coronary revascularization procedures, both by defining the correct strategy of interventional procedure and by improving patient selection. This review explores the different roles of non-invasive imaging techniques in managing CCS patients, also providing insights into preoperative planning for percutaneous or surgical myocardial revascularization. [ABSTRACT FROM AUTHOR]

(1) Background: Computed tomography (CT) plays a paramount role in the characterization and follow-up of COVID-19. Several score systems have been implemented to properly assess the lung parenchyma involved in patients suffering from SARS-CoV-2 infection, such as the visual quantitative assessment score (VQAS) and software-based quantitative assessment score (SBQAS) to help in managing patients with SARS-CoV-2 infection. This study aims to investigate and compare the diagnostic accuracy of the VQAS and SBQAS with two different types of software based on artificial intelligence (AI) in patients affected by SARS-CoV-2. (2) Methods: This is a retrospective study; a total of 90 patients were enrolled with the following criteria: patients' age more than 18 years old, positive test for COVID-19 and unenhanced chest CT scan obtained between March and June 2021. The VQAS was independently assessed, and the SBQAS was performed with two different artificial intelligence-driven software programs (Icolung and CT-COPD). The Intraclass Correlation Coefficient (ICC) statistical index and Bland–Altman Plot were employed. (3) Results: The agreement scores between radiologists (R1 and R2) for the VQAS of the lung parenchyma involved in the CT images were good (ICC = 0.871). The agreement score between the two software types for the SBQAS was moderate (ICC = 0.584). The accordance between Icolung and the median of the visual evaluations (Median R1–R2) was good (ICC = 0.885). The correspondence between CT-COPD and the median of the VQAS (Median R1–R2) was moderate (ICC = 0.622). (4) Conclusions: This study showed moderate and good agreement upon the VQAS and the SBQAS; enhancing this approach as a valuable tool to manage COVID-19 patients and the combination of AI tools with physician expertise can lead to the most accurate diagnosis and treatment plans for patients. [ABSTRACT FROM AUTHOR]

Simple Summary: This is a contemporary overview of how cardiology issues arise in the breast cancer population from anti-cancer agents (e.g., chemotherapy, targeted therapy, endocrine therapy, and radiotherapy). The overview demonstrates how we identify these cardiac issues and what we can do about them. Breast cancer is amongst the most common invasive cancers in adults. There are established relationships between anti-cancer treatments for breast cancer and cardiovascular side effects. In recent years, novel anti-cancer treatments have been established, as well as the availability of multi-modal cardiac imaging and the sophistication of treatment for cardiac disease. This review provides an in-depth overview regarding the interface of breast cancer and cancer therapy-related cardiovascular toxicity. Specifically, it reviews the pathophysiology of breast cancer, the method of action in therapy-related cardiovascular toxicity from anti-cancer treatment, the use of echocardiography, cardiac CT, MRI, or nuclear medicine as diagnostics, and the current evidence-based treatments available. It is intended to be an all-encompassing review for clinicians caring for patients in this situation. [ABSTRACT FROM AUTHOR]

Objectives: No clear recommendations are endorsed by the different scientific societies on the clinical use of repeat coronary computed tomography angiography (CCTA) in patients with non-obstructive coronary artery disease (CAD). This study aimed to develop and validate a practical CCTA risk score to predict medium-term disease progression in patients at a low-to-intermediate probability of CAD. Methods: Patients were part of the Progression of AtheRosclerotic PlAque Determined by Computed Tomographic Angiography Imaging (PARADIGM) registry. Specifically, 370 (derivation cohort) and 219 (validation cohort) patients with two repeat, clinically indicated CCTA scans, non-obstructive CAD, and absence of high-risk plaque (≥ 2 high-risk features) at baseline CCTA were included. Disease progression was defined as the new occurrence of ≥ 50% stenosis and/or high-risk plaque at follow-up CCTA. Results: In the derivation cohort, 104 (28%) patients experienced disease progression. The median time interval between the two CCTAs was 3.3 years (2.7–4.8). Odds ratios for disease progression derived from multivariable logistic regression were as follows: 4.59 (95% confidence interval: 1.69–12.48) for the number of plaques with spotty calcification, 3.73 (1.46–9.52) for the number of plaques with low attenuation component, 2.71 (1.62–4.50) for 25–49% stenosis severity, 1.47 (1.17–1.84) for the number of bifurcation plaques, and 1.21 (1.02–1.42) for the time between the two CCTAs. The C-statistics of the model were 0.732 (0.676–0.788) and 0.668 (0.583–0.752) in the derivation and validation cohorts, respectively. Conclusions: The new CCTA-based risk score is a simple and practical tool that can predict mid-term CAD progression in patients with known non-obstructive CAD. Clinical relevance statement: The clinical implementation of this new CCTA-based risk score can help promote the management of patients with non-obstructive coronary disease in terms of timing of imaging follow-up and therapeutic strategies. Key Points: • No recommendations are available on the use of repeat CCTA in patients with non-obstructive CAD. • This new CCTA score predicts mid-term CAD progression in patients with non-obstructive stenosis at baseline. • This new CCTA score can help guide the clinical management of patients with non-obstructive CAD. [ABSTRACT FROM AUTHOR]

Objectives: Evaluation of in-stent restenosis (ISR), especially for small stents, remains challenging during computed tomography (CT) angiography. We used deep learning reconstruction to quantify stent strut thickness and lumen vessel diameter at the stent and compared it with values obtained using conventional reconstruction strategies. Methods: We examined 166 stents in 85 consecutive patients who underwent CT and invasive coronary angiography (ICA) within 3 months of each other from 2019–2021 after percutaneous coronary intervention with coronary stent placement. The presence of ISR was defined as percent diameter stenosis ≥ 50% on ICA. We compared a super-resolution deep learning reconstruction, Precise IQ Engine (PIQE), and a model-based iterative reconstruction, Forward projected model-based Iterative Reconstruction SoluTion (FIRST). All images were reconstructed using PIQE and FIRST and assessed by two blinded cardiovascular radiographers. Results: PIQE had a larger full width at half maximum of the lumen and smaller strut than FIRST. The image quality score in PIQE was higher than that in FIRST (4.2 ± 1.1 versus 2.7 ± 1.2, p < 0.05). In addition, the specificity and accuracy of ISR detection were better in PIQE than in FIRST (p < 0.05 for both), with particularly pronounced differences for stent diameters < 3.0 mm. Conclusion: PIQE provides superior image quality and diagnostic accuracy for ISR, even with stents measuring < 3.0 mm in diameter. Clinical relevance statement: With improvements in the diagnostic accuracy of in-stent stenosis, CT angiography could become a gatekeeper for ICA in post-stenting cases, obviating ICA in many patients after recent stenting with infrequent ISR and allowing non-invasive ISR detection in the late phase. Key Points: • Despite CT technology advancements, evaluating in-stent stenosis severity, especially in small-diameter stents, remains challenging. • Compared with conventional methods, the Precise IQ Engine uses deep learning to improve spatial resolution. • Improved diagnostic accuracy of CT angiography helps avoid invasive coronary angiography after coronary artery stenting. [ABSTRACT FROM AUTHOR]

Background Advanced coronary plaque analysis by cardiac computed tomography (CT) has recently emerged as a promising technique for better prognostic stratification. However, this evaluation application in clinical practice is still uncertain. Case summary In the present case, we described the clinical picture of a 44-year-old tennis player with ectopic ventricular beats in which cardiac CT enabled the identification of a non-obstructive but high-risk plaque on proximal left anterior descendent artery. The application of artificial intelligence (AI)-enhanced software enabled to better stratify the patients' risk. The present case describes how early identification of non-obstructive but high-risk coronary plaque evaluated by cardiac CT using AI-enhanced software enabled accurate and personalized risk assessment. Discussion The main clinical message of this case report is that advanced plaque analysis by cardiac CT, especially when performed with AI-based software, may provide important prognostic information leading to a personalized preventive approach. Moreover, AI-based software may contribute to promote a routine evaluation of these important data already included in traditional cardiac CT. [ABSTRACT FROM AUTHOR]

Background: Noninvasive imaging methods, either anatomical or functional tests, serve as essential instruments for the appropriate management of patients with established or suspected coronary artery disease (CAD). We sought to evaluate the safety and efficacy of a coronary computed tomography angiography (CCTA) plus stress cardiac magnetic resonance imaging (S-CMR) strategy in patients with chronic coronary syndrome (CCS). Methods: Patients with suspected CCS showing intermediate coronary plaques (stenosis 30–70%) at CCTA underwent S-CMR. Patients with a positive S-CMR were referred to invasive coronary angiography (ICA) plus instantaneous wave-free ratio (iFR), and myocardial revascularization if recommended. All patients received guideline-directed medical therapy (GDMT), including high-dose statins, regardless of myocardial revascularization. The primary endpoint was a composite of death from cardiovascular causes, non-fatal myocardial infarction, and unplanned revascularization. Results: According to the results of CCTA, 62 patients showing intermediate coronary plaques underwent S-CMR, which was positive for a myocardial perfusion deficit in n = 17 (27%) and negative in n = 45 (73%) patients. According to the results of ICA plus iFR, revascularization was performed in 13 patients. No differences in the primary endpoint between the positive and negative S-CMR groups were observed at 1 year (1 [5.9%] vs. 1 [2.2%], p = 0.485) and after a median of 33.4 months (2 [11.8%] vs. 3 [6.7%]; p = 0.605). Conclusions: Our study suggests that a CCTA plus S-CMR strategy is effective for the evaluation of patients with suspicion of CCS at low–intermediate risk, and it may help to refine the selection of patients with intermediate coronary plaques at CCTA needing coronary revascularization. [ABSTRACT FROM AUTHOR]

Background: Coronary CT angiography (CCTA) may detect coronary artery disease (CAD) in transcatheter aortic valve implantation (TAVI) patients and may obviate invasive coronary angiography (ICA) in selected patients. We assessed the diagnostic accuracy of CCTA for detecting CAD in TAVI patients based on published data. Methods: Meta-analysis and meta-regression were performed based on a comprehensive electronic search, including relevant studies assessing the diagnostic accuracy of CCTA in the setting of TAVI patients compared to ICA. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), were calculated on a patient and per segment level. Results: Overall, 27 studies (total of 7458 patients) were included. On the patient level, the CCTA's pooled sensitivity and NPV were 95% (95% CI: 93–97%) and 97% (95% CI: 95–98%), respectively, while the specificity and PPV were at 73% (95% CI: 62–82%) and 64% (95% CI: 57–71%), respectively. On the segmental coronary vessel level, the sensitivity and NPV were 90% (95% CI: 79–96%) and 98% (95% CI: 97–99%). Conclusions: This meta-analysis highlights CCTA's potential as a first-line diagnostic tool although its limited PPV and specificity may pose challenges when interpreting heavily calcified arteries. This study underscores the need for further research and protocol standardization in this area. [ABSTRACT FROM AUTHOR]

Background: The substantial heterogeneity of clinical presentations in patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pneumonia still requires robust chest computed tomography analysis to identify high-risk patients. While extension of ground-glass opacity and consolidation from peripheral to central lung fields on chest computed tomography (CT) might be associated with severely ill conditions, quantification of the central-peripheral distribution of ground glass opacity and consolidation in assessments of SARS-CoV-2 pneumonia remains unestablished. This study aimed to examine whether the central-peripheral distributions of ground glass opacity and consolidation were associated with severe outcomes in patients with SARS-CoV-2 pneumonia independent of the whole-lung extents of these abnormal shadows. Methods: This multicenter retrospective cohort included hospitalized patients with SARS-CoV-2 pneumonia between January 2020 and August 2021. An artificial intelligence-based image analysis technology was used to segment abnormal shadows, including ground glass opacity and consolidation. The area ratio of ground glass opacity and consolidation to the whole lung (GGO%, CON%) and the ratio of ground glass opacity and consolidation areas in the central lungs to those in the peripheral lungs (GGO(C/P)) and (CON(C/P)) were automatically calculated. Severe outcome was defined as in-hospital death or requirement for endotracheal intubation. Results: Of 512 enrolled patients, the severe outcome was observed in 77 patients. GGO% and CON% were higher in patients with severe outcomes than in those without. Multivariable logistic models showed that GGO(C/P), but not CON(C/P), was associated with the severe outcome independent of age, sex, comorbidities, GGO%, and CON%. Conclusion: In addition to GGO% and CON% in the whole lung, the higher the ratio of ground glass opacity in the central regions to that in the peripheral regions was, the more severe the outcomes in patients with SARS-CoV-2 pneumonia were. The proposed method might be useful to reproducibly quantify the extension of ground glass opacity from peripheral to central lungs and to estimate prognosis. [ABSTRACT FROM AUTHOR]

Coarctation of the aorta (CoA) is a congenital abnormality characterized by a narrowing of the aortic lumen, which can lead to significant morbidity and mortality if left untreated. Even after repair and despite significant advances in therapeutic management, these patients have overall reduced long-term survival due to the consequences of chronic afterload increase. Cardiovascular imaging is key from the first diagnosis to serial follow-up. In recent years, novel imaging techniques have emerged, increasing accessibility to advanced imaging modalities and enabling early and non-invasive identification of complications after repair. The aim of this paper is to provide a comprehensive review of the role of different imaging techniques in the evaluation and management of patients with native or repaired CoA, highlighting their unique strengths and limitations. [ABSTRACT FROM AUTHOR]

Background: Anomalous aortic origin of a coronary artery (AAOCA) is the most prevalent form of coronary anomaly. One variant of AAOCA is the anomalous origin of the left circumflex artery from the right sinus of Valsalva, which can be detected using cardiac computed tomography (CT). However, limited data are available regarding the natural history of this anomaly, its impact on myocardial function, and associated symptoms. Methods: We conducted a retrospective analysis of 16,680 CT exams (cardiac and chest) performed between 2015 and 2022 at our Heart Imaging Department, utilising a dual-source 128-slice CT scanner (SOMATOM Definition Flash, Siemens Healthineers, Forchheim, Germany). A registry of patients with anomalous origin of the circumflex artery from the right sinus of Valsalva (RCx) was established. The study included 56 cases of RCx (0.33%). Clinical information was obtained from medical records. RCx was defined as a circumflex artery originating from the right sinus of Valsalva (type I or II) or the right coronary artery (type III). Two researchers independently reevaluated each CT exam in our study group to ensure accurate radiologic descriptions and provide additional precise radiologic information regarding the anomaly, including high-risk features. Results: Our study comprised 56 patients, with approximately equal distribution between males (n = 30, 54%) and females (n = 26, 46%), and with a median age of 59 years. Coronary heart disease (CAD) was known in 23% of patients (n = 13), while 11% (n = 6) were obese (defined as a BMI > 30 kg/m2), and 13% (n = 7) were diagnosed with type 2 diabetes. Only 9% of patients (n = 5) were smokers. Dyslipidemia was the most prevalent atherosclerotic risk factor, affecting approximately one third of patients (n = 17, 30%). In 14% (n = 8) of patients, heart failure was observed, while 13% (n = 7) were diagnosed with atrial fibrillation. Type I RCx was the most common subtype, identified in 48% of patients (n = 27) with this anomaly. Type II and Type III were found in 25% (n = 14) and 27% (n = 15) of patients, respectively. Conclusions: Our findings suggest that RCx is frequently encountered as an incidental finding, and we did not identify a consistent clinical characteristic in all patients with this type of anomaly. Furthermore, no gender predominance was associated with RCx. The natural history of this anomaly and its clinical implications seem benign. Further research is warranted to better understand this anomaly's natural course and clinical implications. [ABSTRACT FROM AUTHOR]

The use of coronary computed tomography angiography (CCTA) in the setting of stable coronary artery disease is highly recommended for low-risk patients. High-risk patients, such as symptomatic subjects with prior revascularization, are suggested to be investigated with noninvasive functional tests or invasive coronary angiography. CCTA is not considered for these patients because of some well-known CCTA artifacts, such as blooming and motion artifacts. However, new technology has allowed us to obtain images with high spatial resolution, overcoming these well-known limitations of CCTA. Furthermore, the introduction of CT-derived fractional flow reserve and stress CT perfusion has made CCTA a comprehensive examination, including anatomical and functional assessments of coronary plaques. Additionally, CCTA allows for plaque characterization, which has become a cornerstone for the optimization of medical therapy, which is not possible with functional tests. Recent evidence has suggested that CCTA could be used with the aim of monitoring revascularization, both after coronary bypass grafts and percutaneous coronary intervention. With this background information, CCTA can also be considered the exam of choice in subjects with a history of revascularization. The availability of a noninvasive anatomic test for patients with previous coronary revascularization and its possible association with functional assessments in a single exam could play a key role in the follow-up management of these subjects, especially considering the rate of false-positive and negative results of noninvasive functional tests. The present review summarizes the main evidence about CCTA and coronary artery bypass grafts, complex percutaneous coronary intervention, and bioresorbable stent implantation. [ABSTRACT FROM AUTHOR]

Tetralogy of Fallot (TOF) is the most common complex congenital heart disease with long-term survivors, demanding serial monitoring of the possible complications that can be encountered from the diagnosis to long-term follow-up. Cardiovascular imaging is key in the diagnosis and serial assessment of TOF patients, guiding patients' management and providing prognostic information. Thorough knowledge of the pathophysiology and expected sequalae in TOF, as well as the advantages and limitations of different non-invasive imaging modalities that can be used for diagnosis and follow-up, is the key to ensuring optimal management of patients with TOF. The aim of this manuscript is to provide a comprehensive overview of the role of each modality and common protocols used in clinical practice in the assessment of TOF patients. [ABSTRACT FROM AUTHOR]

Purpose: Left atrial appendage occlusion (LAAO) is a structural interventional cardiology procedure that offers several possibilities for the application of additive manufacturing technologies. The literature shows a growing interest in the use of 3D-printed models for LAAO procedure planning and occlusion device choice. This study aims to describe a full workflow to create a 3D-printed LAA model for LAAO procedure planning. Design/methodology/approach: The workflow starts with the patient's computed tomography diagnostic image selection. Segmentation in a commercial software provides initial geometrical models in standard tessellation language (STL) format that are then preprocessed for print in dedicated software. Models are printed using a commercial stereolithography machine and postprocessing is performed. Findings: Models produced with the described workflow have been used at the Careggi Hospital of Florence as LAAO auxiliary planning tool in 10 cases of interest, demonstrating a good correlation with state-of-the-art software for device selection and improving the surgeon's understanding of patient anatomy and device positioning. Originality/value: 3D-printed models for the LAAO planning are already described in the literature. The novelty of the article lies in the detailed description of a robust workflow for the creation of these models. The robustness of the method is demonstrated by the coherent results obtained for the 10 different cases studied. [ABSTRACT FROM AUTHOR]

Thin-section computed tomography (CT) is widely employed not only for assessing morphology but also for evaluating respiratory function. Three-dimensional images obtained from thin-section CT provide precise measurements of lung, airway, and vessel volumes. These volumetric indices are correlated with traditional pulmonary function tests (PFT). CT also generates lung histograms. The volume ratio of areas with low and high attenuation correlates with PFT results. These quantitative image analyses have been utilized to investigate the early stages and disease progression of diffuse lung diseases, leading to the development of novel concepts such as pre-chronic obstructive pulmonary disease (pre-COPD) and interstitial lung abnormalities. Quantitative analysis proved particularly valuable during the COVID-19 pandemic when clinical evaluations were limited. In this review, we introduce CT analysis methods and explore their clinical applications in the context of various lung diseases. We also highlight technological advances, including images with matrices of 1024 × 1024 and slice thicknesses of 0.25 mm, which enhance the accuracy of these analyses. [ABSTRACT FROM AUTHOR]

Background: To present an approach that autonomously identifies and selects a self-selective optimal target for the purpose of enhancing learning efficiency to segment infected regions of the lung from chest computed tomography images. We designed a semi-supervised dual-branch framework for training, where the training set consisted of limited expert-annotated data and a large amount of coarsely annotated data that was automatically segmented based on Hu values, which were used to train both strong and weak branches. In addition, we employed the Lovasz scoring method to automatically switch the supervision target in the weak branch and select the optimal target as the supervision object for training. This method can use noisy labels for rapid localization during the early stages of training, and gradually use more accurate targets for supervised training as the training progresses. This approach can utilize a large number of samples that do not require manual annotation, and with the iterations of training, the supervised targets containing noise become closer and closer to the fine-annotated data, which significantly improves the accuracy of the final model. Results: The proposed dual-branch deep learning network based on semi-supervision together with cost-effective samples achieved 83.56 ± 12.10 and 82.67 ± 8.04 on our internal and external test benchmarks measured by the mean Dice similarity coefficient (DSC). Through experimental comparison, the DSC value of the proposed algorithm was improved by 13.54% and 2.02% on the internal benchmark and 13.37% and 2.13% on the external benchmark compared with U-Net without extra sample assistance and the mean-teacher frontier algorithm, respectively. Conclusion: The cost-effective pseudolabeled samples assisted the training of DL models and achieved much better results compared with traditional DL models with manually labeled samples only. Furthermore, our method also achieved the best performance compared with other up-to-date dual branch structures. [ABSTRACT FROM AUTHOR]

Aims: To evaluate a deep-learning model (DLM) for detecting coronary stenoses in emergency room patients with acute chest pain (ACP) explored with electrocardiogram-gated aortic computed tomography angiography (CTA) to rule out aortic dissection. Methods and results: This retrospective study included 217 emergency room patients (41% female, mean age 67.2 years) presenting with ACP and evaluated by aortic CTA at our institution. Computed tomography angiography was assessed by two readers, who rated the coronary arteries as 1 (no stenosis), 2 (<50% stenosis), or 3 (≥50% stenosis). Computed tomography angiography was categorized as high quality (HQ), if all three main coronary arteries were analysable and low quality (LQ) otherwise. Curvilinear coronary images were rated by a DLM using the same system. Per-patient and per-vessel analyses were conducted. One hundred and twenty-one patients had HQ and 96 LQ CTA. Sensitivity, specificity, positive predictive value, negative predictive value (NPV), and accuracy of the DLM in patients with high-quality image for detecting ≥50% stenoses were 100, 62, 59, 100, and 75% at the patient level and 98, 79, 57, 99, and 84% at the vessel level, respectively. Sensitivity was lower (79%) for detecting ≥50% stenoses at the vessel level in patients with low-quality image. Diagnostic accuracy was 84% in both groups. All 12 patients with acute coronary syndrome (ACS) and stenoses by invasive coronary angiography (ICA) were rated 3 by the DLM. Conclusion: A DLM demonstrated high NPV for significant coronary artery stenosis in patients with ACP. All patients with ACS and stenoses by ICA were identified by the DLM. Graphical Abstract Study highlights and presentation of the study workflow. Showcase of a proximal RCA occlusion opportunisticall identified by the AI model in a 54 year-old male patient with acute chest pain. ACS, acute coronary syndrome; AI, artificial intelligence; CTA, computed tomography angiography; CX, circumflex artery; ECG, electrocardiogram; ICA, invasive coronary angiography; LAD, left anterior descending artery; MPR, multiplanar reconstruction; RCA, right coronary artery. [ABSTRACT FROM AUTHOR]

Objectives: To report a case of ingested wire bristle embedded within the extrinsic musculature of the tongue requiring a transcervical approach for removal and to provide a revised algorithm for the management of ingested wire bristles. Methods: The clinical record of 1 patient who ingested a grill brush wire bristle was reviewed. A literature review was also conducted to refine a treatment algorithm for managing ingested wire bristles. Results: We present a case of a 53-year-old male who accidentally ingested a grill brush wire bristle. After multiple unsuccessful endoscopic attempts at removal, the wire bristle migrated deep into the extrinsic musculature of the tongue, necessitating a transcervical approach for adequate visualization and retrieval. Conclusions: This is the first case reported of a wire bristle migrating deep into the tongue musculature that was successfully removed via a transcervical approach. Our proposed algorithm provides a comprehensive approach to the management of ingested wire bristles, specifically in cases where endoscopic retrieval is not feasible. [ABSTRACT FROM AUTHOR]

Recently, the Covid-19 pandemic has affected several lives of people globally, and there is a need for a massive number of screening tests to diagnose the existence of coronavirus. For the medical specialist, detecting COVID-19 cases is a difficult task. There is a need for fast, cheap and accurate diagnostic tools. The chest X-ray and the computerized tomography (CT) play a significant role in the COVID-19 diagnosis. The advancement of deep learning (DL) approaches helps to introduce a COVID diagnosis system to achieve maximum detection rate with minimum time complexity. This research proposed a discrete wavelet optimized network model for COVID-19 diagnosis and feature extraction to overcome these problems. It consists of three stages pre-processing, feature extraction and classification. The raw images are filtered in the pre-processing phase to eliminate unnecessary noises and improve the image quality using the MMG hybrid filtering technique. The next phase is feature extraction, in this stage, the features are extracted, and the dimensionality of the features is diminished with the aid of a modified discrete wavelet based Mobile Net model. The third stage is the classification here, the convolutional Aquila COVID detection network model is developed to classify normal and COVID-19 positive cases from the collected images of the COVID-CT and chest X-ray dataset. Finally, the performance of the proposed model is compared with some of the existing models in terms of accuracy, specificity, sensitivity, precision, f-score, negative predictive value (NPV) and positive predictive value (PPV), respectively. The proposed model achieves the performance of 99%, 100%, 98.5%, and 99.5% for the CT dataset, and the accomplished accuracy, specificity, sensitivity, and precision values of the proposed model for the X-ray dataset are 98%, 99%, 98% and 97% respectively. In addition, the statistical and cross validation analysis is conducted to validate the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

Aims Icosapent ethyl (IPE) significantly reduced ischaemic events in statin-treated patients with atherosclerosis or diabetes and elevated triglycerides in REDUCE-IT, including large reductions in myocardial infarction and elective, urgent, and emergent coronary revascularization. However, the mechanisms driving this clinical benefit are not fully known. The EVAPORATE trial demonstrated that IPE significantly reduced plaque burden. No study to date has assessed the impact of IPE on coronary physiology. Fractional flow reserve (FFR) derived from coronary computed tomography angiography (CTA) data sets (FFRCT) applies computational fluid dynamics to calculate FFR values in epicardial coronary arteries. Our objective was to assess the impact of IPE on coronary physiology assessed by FFRCT using imaging data from EVAPORATE. Methods and results A total of 47 patients and of 507 coronary lesions at baseline, 9 months, and 18 months with coronary CTA and FFRCT were studied in a blinded core lab. The pre-specified primary endpoint was the FFRCT value in the distal coronary segment from baseline to follow-up in the most diseased vessel per patient using IPE compared with placebo. The pre-specified secondary endpoint was the change in translesional FFRCT (ΔFFRCT) across the most severe (minimum 30% diameter stenosis) coronary lesion per vessel. Baseline FFRCT was similar for IPE compared with placebo (0.83 ± 0.08 vs. 0.84 ± 0.08, P = 0.55). There was significant improvement in the primary endpoint, as IPE improved mean distal segment FFRCT at 9- and 18-month follow-up compared with placebo (0.01 ± 0.05 vs. −0.05 ± 0.09, P = 0.02, and −0.01 ± 0.09 vs. −0.09 ± 0.12, P = 0.03, respectively). ΔFFRCT in 140 coronary lesions was improved, although not statistically significant, with IPE compared with placebo (−0.06 ± 0.08 vs. −0.09 ± 0.1, P = 0.054). Conclusion Icosapent ethyl demonstrated significant benefits in coronary physiology compared with placebo. This early and sustained improvement in FFRCT at 9- and 18-month follow-up provides mechanistic insight into the clinical benefit observed in the REDUCE-IT trial. Furthermore, this is the first assessment of FFRCT to determine drug effect. [ABSTRACT FROM AUTHOR]

Objectives: To compare high-pitch spiral (HPS) and prospectively-gated step-and-shoot (SAS) coronary CT angiography (CCTA) using third generation dual-source CT regarding objective and subjective image quality parameters and radiation exposure. Methods: Eighty pairs of patients matched for gender, age, heart rate and BMI were enrolled in this retrospective study. High-pitch spiral and prospectively ECG-gated sequential CCTA were performed using third generation dual-source CT. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) in the left ventricle were calculated for each group. Image quality were also scored using four-point scale. Student t-test was used to compare SNR, CNR and mean effective dose values (ED) and Wilcoxon test was used to compare image quality scores. Interrater aggreement were evaluated using Cohen's kappa statistics. Results: Between-group differences in terms of age, gender, BMI, heart rate, and Agatston score were statistically not significant. Mean SNR and CNR was higher in prospective SAS protocol (16.5 ± 6.2 vs. 14.7 ± 4.9, p = 0.047 and 13.0 ± 5.2 vs. 11.2 ± 4.3, p = 0.02). Image quality scores showed no significant difference between two scan protocols (p > 0.05). Regarding radiation exposure, CT dose index (CTDIvol), dose length product (DLP) and ED was significantly lower for high-pitch group (p < 0.0001). Conclusions: HPS CCTA using DSCT enables > 70% dose reduction while maintaining the image quality compared to prospectively ECG-gated SAS protocol. Therefore, HPS CCTA protocol can be preferred in patients appropriate for prospective ECG-triggered protocol. [ABSTRACT FROM AUTHOR]

Background: Epicardial adipose tissue (EAT) thickness is an independent predictor for the recurrence of premature ventricular beats after ablation. However, it is unclear whether EAT volume is associated with the recurrence of idiopathic ventricular tachycardia (IVT) following ablation. This study sought to investigate the association between EAT volume and IVT recurrence following radiofrequency ablation for IVT patients. Methods: This retrospective study included 69 IVT patients undergoing computed tomography examination before ablation who underwent their first catheter ablation between 2017 and 2021. The predictive value of EAT volume for IVT recurrence following ablation was assessed. Results: During the follow-up period (median: 540 days; range: 253-929 days), 26.1% (18/69) of the patients experienced IVT recurrence. The cut-off point of EAT volume for predicting IVT recurrence was 160.30 mL, and the area under the curve (AUC) was 0.751 (95% confidence interval (CI): 0.615-0.887) by the receiver operating characteristic curve. Kaplan-Meier analysis showed that patients with larger EAT volumes had higher cumulative rates of IVT recurrence. Multivariable analysis also revealed that EAT volume (per 10 mL increase; hazard ratio (HR): 1.16, 95% CI: 1.03-1.32, p = 0.018) was independently associated with IVT recurrence. Furthermore, patients with an epicardial site of IVT had a significantly larger EAT volume than IVT patients with non-epicardial origins. Conclusions: A larger EAT volume may be associated with IVT recurrence after catheter ablation. EAT volume may be helpful for risk stratification in patients undergoing IVT ablation. [ABSTRACT FROM AUTHOR]

In recent decades, cardiac computed tomography (CT) has emerged as a powerful non-invasive tool for risk stratification, as well as the detection and characterization of coronary artery disease (CAD), which remains the main cause of morbidity and mortality in the world. Advances in technology have favored the increasing use of cardiac CT by allowing better performance with lower radiation doses. Coronary artery calcium, as assessed by non-contrast CT, is considered to be the best marker of subclinical atherosclerosis, and its use is recommended for the refinement of risk assessment in low-to-intermediate risk individuals. In addition, coronary CT angiography (CCTA) has become a gate-keeper to invasive coronary angiography (ICA) and revascularization in patients with acute chest pain by allowing the assessment not only of the extent of lumen stenosis, but also of its hemodynamic significance if combined with the measurement of fractional flow reserve or perfusion imaging. Moreover, CCTA provides a unique incremental value over functional testing and ICA by imaging the vessel wall, thus allowing the assessment of plaque burden, composition, and instability features, in addition to perivascular adipose tissue attenuation, which is a marker of vascular inflammation. There exists the potential to identify the non-obstructive lesions at high risk of progression to plaque rupture by combining all of these measures. [ABSTRACT FROM AUTHOR]

Background: In coronary computed tomography angiography (CCTA), the main issue of image quality is noise in obese patients, blooming artifacts due to calcium and stents, high-risk coronary plaques, and radiation exposure to patients. Objective: To compare the CCTA image quality of deep learning-based reconstruction (DLR) with that of filtered back projection (FBP) and iterative reconstruction (IR). Methods: This was a phantom study of 90 patients who underwent CCTA. CCTA images were acquired using FBP, IR, and DLR. In the phantom study, the aortic root and the left main coronary artery in the chest phantom were simulated using a needleless syringe. The patients were classified into three groups according to their body mass index. Noise, the signal-to-noise ratio (SNR), and the contrast-to-noise ratio (CNR) were measured for image quantification. A subjective analysis was also performed for FBP, IR, and DLR. Results: According to the phantom study, DLR reduced noise by 59.8% compared to FBP and increased SNR and CNR by 121.4% and 123.6%, respectively. In a patient study, DLR reduced noise compared to FBP and IR. Furthermore, DLR increased the SNR and CNR more than FBP and IR. In terms of subjective scores, DLR was higher than FBP and IR. Conclusion: In both phantom and patient studies, DLR effectively reduced image noise and improved SNR and CNR. Therefore, the DLR may be useful for CCTA examinations. [ABSTRACT FROM AUTHOR]

X-linked agammaglobulinemia (XLA) is a primary immunodeficiency characterized by marked reduction in serum immunoglobulins and early-onset infections. Coronavirus Disease-2019 (COVID-19) pneumonia in immunocompromised patients presents clinical and radiological peculiarities which have not yet been completely understood. Very few cases of agammaglobulinemic patients with COVID-19 have been reported since the beginning of the pandemic in February 2020. We report two cases of migrant COVID-19 pneumonia in XLA patients. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the correlation between multislice computed tomography (MSCT)-derived parameters characterizing atrial enlargement and the frequency of emergency hospitalizations after catheter ablation for atrial fibrillation (AF). The study included 52 patients with paroxysmal or persistent AF, who presented criteria for interventional rhythm control strategies and underwent MSCT evaluation prior to ablation. The majority of emergency hospital admissions were due to heart failure caused by high-frequency arrhythmia (90.33%), or by cardioembolic complications, causing acute stroke (9.67%). The number of emergency referrals was significantly increased in cases of moderately enlarged left atrial volume (69.23%), and re-admission was necessary for over three quarters of the patients with highly enlarged left atrial volume (76.92%, p = 0.02). The average recurrence rate of AF following ablation therapy was 28.84% during the one-year follow-up, being 0% for volumes <71.33 mL, 32% for volumes between 71.33 mL and 109.5 mL, and 53.84% for volumes >109.5 mL (p = 0.01). A large volume of the left atrium, determined by MSCT, is associated with a higher risk of emergency rehospitalizations following catheter ablation of AF. [ABSTRACT FROM AUTHOR]

Background: Deep learning image reconstructions (DLIR) have been recently introduced as an alternative to filtered back projection (FBP) and iterative reconstruction (IR) algorithms for computed tomography (CT) image reconstruction. The aim of this study was to evaluate the effect of DLIR on image quality and quantification of coronary artery calcium (CAC) in comparison to FBP. Methods: One hundred patients were consecutively enrolled. Image quality–associated variables (noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR)) as well as CAC-derived parameters (Agatston score, mass, and volume) were calculated from images reconstructed by using FBP and three different strengths of DLIR (low (DLIR_L), medium (DLIR_M), and high (DLIR_H)). Patients were stratified into 4 risk categories according to the Coronary Artery Calcium - Data and Reporting System (CAC-DRS) classification: 0 Agatston score (very low risk), 1–99 Agatston score (mildly increased risk), Agatston 100–299 (moderately increased risk), and ≥ 300 Agatston score (moderately-to-severely increased risk). Results: In comparison to standard FBP, increasing strength of DLIR was associated with a significant and progressive decrease of image noise (p < 0.001) alongside a significant and progressive increase of both SNR and CNR (p < 0.001). The use of incremental levels of DLIR was associated with a significant decrease of Agatston CAC score and CAC volume (p < 0.001), while mass score remained unchanged when compared to FBP (p = 0.232). The underestimation of Agatston CAC led to a CAC-DRS misclassification rate of 8%. Conclusion: DLIR systematically underestimates Agatston CAC score. Therefore, DLIR should be used cautiously for cardiovascular risk assessment. Key Points: • In coronary artery calcium imaging, the implementation of deep learning image reconstructions improves image quality, by decreasing the level of image noise. • Deep learning image reconstructions systematically underestimate Agatston coronary artery calcium score. • Deep learning image reconstructions should be used cautiously in clinical routine to measure Agatston coronary artery calcium score for cardiovascular risk assessment. [ABSTRACT FROM AUTHOR]

Transcatheter aortic valve implantation (TAVI) is a minimally invasive intervention for the treatment of severe aortic valve stenosis. The main cause of failure is the structural deterioration of the implanted prosthetic leaflets, possibly inducing a valvular re‐stenosis 5–10 years after the implantation. Based solely on pre‐implantation data, the aim of this work is to identify fluid‐dynamics and structural indices that may predict the possible valvular deterioration, in order to assist the clinicians in the decision‐making phase and in the intervention design. Patient‐specific, pre‐implantation geometries of the aortic root, the ascending aorta, and the native valvular calcifications were reconstructed from computed tomography images. The stent of the prosthesis was modeled as a hollow cylinder and virtually implanted in the reconstructed domain. The fluid‐structure interaction between the blood flow, the stent, and the residual native tissue surrounding the prosthesis was simulated by a computational solver with suitable boundary conditions. Hemodynamical and structural indicators were analyzed for five different patients that underwent TAVI – three with prosthetic valve degeneration and two without degeneration – and the comparison of the results showed a correlation between the leaflets' structural degeneration and the wall shear stress distribution on the proximal aortic wall. This investigation represents a first step towards computational predictive analysis of TAVI degeneration, based on pre‐implantation data and without requiring additional peri‐operative or follow‐up information. Indeed, being able to identify patients more likely to experience degeneration after TAVI may help to schedule a patient‐specific timing of follow‐up. [ABSTRACT FROM AUTHOR]

Performance of cardiovascular magnetic resonance (CMR) in the planning phase of percutaneous pulmonary valve implantation (PPVI) is needed for the accurate delineation of the right ventricular outflow tract (RVOT), coronary anatomy and the quantification of right ventricular (RV) volume overload in patients with significant pulmonary regurgitation (PR). This helps to find the correct timings for the intervention and prevention of PPVI-related complications such as coronary artery compression, device embolization and stent fractures. A defined CMR study protocol should be set for all PPVI candidates to reduce acquisition times and acquire essential sequences that are determinants for PPVI success. For correct RVOT sizing, contrast-free whole-heart sequences, preferably at end-systole, should be adopted in the pediatric population thanks to their high reproducibility and concordance with invasive angiographic data. When CMR is not feasible or contraindicated, cardiac computed tomography (CCT) may be performed for high-resolution cardiac imaging and eventually the acquisition of complementary functional data. The aim of this review is to underline the role of CMR and advanced multimodality imaging in the context of pre-procedural planning of PPVI concerning its current and potential future applications. [ABSTRACT FROM AUTHOR]

Nowadays, coronary computed tomography angiography (CCTA) has a role of paramount importance in the diagnostic algorithm of ischemic heart disease (IHD), both in stable coronary artery disease (CAD) and acute chest pain. Alongside the quantification of obstructive coronary artery disease, the recent technologic developments in CCTA provide additional relevant information that can be considered as "novel markers" for risk stratification in different settings, including ischemic heart disease, atrial fibrillation, and myocardial inflammation. These markers include: (i) epicardial adipose tissue (EAT), associated with plaque development and the occurrence of arrhythmias; (ii) late iodine enhancement (LIE), which allows the identification of myocardial fibrosis; and (iii) plaque characterization, which provides data about plaque vulnerability. In the precision medicine era, these emerging markers should be integrated into CCTA evaluation to allow for the bespoke interventional and pharmacological management of each patient. [ABSTRACT FROM AUTHOR]

Computed tomography angiography (CTA) of the aorta and the iliofemoral arteries is crucial for preprocedural planning of transcatheter aortic valve implantation (TAVI) in patients with severe aortic stenosis (AS), because it provides details on a variety of aspects required for heart team decision-making. In addition to providing relevant diagnostic information on the degree of aortic valve calcification, CTA allows for a customized choice of the transcatheter heart valve system and the TAVI access route. Furthermore, current guidelines recommend the exclusion of relevant coronary artery disease (CAD) prior to TAVI. The feasibility of coronary artery assessment with CTA in patients scheduled for TAVI has been established previously, and accumulating data support its value. In addition, fractional flow reserve determined from CTA (CT–FFR) and machine learning-based CT–FFR were recently shown to improve its diagnostic yield for this purpose. However, the utilization of CTA for coronary artery evaluation remains limited in this specific population of patients due to the relatively high risk of CAD coexistence with severe AS. Therefore, the current diagnostic work-up prior to TAVI routinely includes invasive catheter coronary angiography at most centers. In this article, the authors address technological prerequisites and CT protocol considerations, discuss pitfalls, review the current literature regarding combined CTA coronary artery assessment and preprocedural TAVI evaluation, and provide an overview of unanswered questions and future research goals within the field. [ABSTRACT FROM AUTHOR]

Context Widespread prevalence of Coronavirus Disease 2019 (COVID-19) infection with high rates of morbidity and mortality necessitates early case definition and severity grading to predict prognosis and improve disease outcome parameters. Purpose To correlate computed tomography severity score (CTSS) in COVID-19-infected patients with their clinical, laboratory, method of ventilation, and disease outcome parameters. Patients and methods A retrospective analysis was performed on 139 COVID-19-infected patients with typical or indeterminate COVID radiological patterns with a correlation between CTSS and their clinical, laboratory, ventilation, hospital stay, and survival data. Results A total of 139 cases were included, with a mean age of 59.81±12.29 years, 63.3% males, 28.77% were noncomorbid, and oxygen saturation of 82.53±6.58 SD. They were subgrouped based on CTSS. A significant correlation was found between high CTSS and oxygen saturation, C-reactive protein, ferritin, D-dimer, lymphopenia, diabetes comorbid patients, need for high-flow oxygen therapy, need for noninvasive or invasive ventilation, mortality, and number of hospital stay in days. Conclusion Different parameters are correlated with high CTSS, especially low oxygen saturation, high C-reactive protein or ferritin values, diabetes, and long hospital stay [ABSTRACT FROM AUTHOR]

Background: Diagnosis of the stage of COVID-19 patients using the chest computed tomography (CT) can help the physician in making decisions on the length of time required for hospitalization and adequate selection of patient care. This diagnosis requires very expert radiologists who are not available everywhere and is also tedious and subjective. The aim of this study is to propose an advanced machine learning system to diagnose the stages of COVID-19 patients including normal, early, progressive, peak, and absorption stages based on lung CT images, using an automatic deep transfer learning ensemble. Methods: Different strategies of deep transfer learning were used which were based on pretrained convolutional neural networks (CNNs). Pretrained CNNs were fine-tuned on the chest CT images, and then, the extracted features were classified by a softmax layer. Finally, we built an ensemble method based on majority voting of the best deep transfer learning outputs to further improve the recognition performance. Results: The experimental results from 689 cases indicate that the ensemble of three deep transfer learning outputs based on EfficientNetB4, InceptionResV3, and NasNetlarge has the highest results in diagnosing the stage of COVID-19 with an accuracy of 91.66%. Conclusion: The proposed method can be used for the classification of the stage of COVID-19 disease with good accuracy to help the physician in making decisions on patient care. [ABSTRACT FROM AUTHOR]