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Coronary artery calcium is an accurate predictor of cardiovascular events. While it is visible on all computed tomography (CT) scans of the chest, this information is not routinely quantified as it requires expertise, time, and specialized equipment. Here, we show a robust and time-efficient deep learning system to automatically quantify coronary calcium on routine cardiac-gated and non-gated CT. As we evaluate in 20,084 individuals from distinct asymptomatic (Framingham Heart Study, NLST) and stable and acute chest pain (PROMISE, ROMICAT-II) cohorts, the automated score is a strong predictor of cardiovascular events, independent of risk factors (multivariable-adjusted hazard ratios up to 4.3), shows high correlation with manual quantification, and robust test-retest reliability. Our results demonstrate the clinical value of a deep learning system for the automated prediction of cardiovascular events. Implementation into clinical practice would address the unmet need of automating proven imaging biomarkers to guide management and improve population health. Coronary artery calcium is an accurate predictor of cardiovascular events but this information is not routinely quantified. Here the authors show a robust and time-efficient deep learning system to automatically quantify coronary calcium on CT scans and predict cardiovascular events in a large, multicentre study.

Introduction This study investigated the use of dual-energy spectral detector computed tomography (CT) and virtual monoenergetic imaging (VMI) reconstructions in pre-interventional transcatheter aortic valve replacement (TAVR) planning. We aimed to determine the minimum required contrast medium (CM) amount to maintain diagnostic CT imaging quality for TAVR planning. Methods In this prospective clinical trial, TAVR candidates received a standardized dual-layer spectral detector CT protocol. The CM amount (Iohexol 350 mg iodine/mL, standardized flow rate 3 mL/s) was reduced systematically after 15 patients by 10 mL, starting at 60 mL (institutional standard). We evaluated standard, and 40- and 60-keV VMI reconstructions. For image quality, we measured signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and diameters in multiple vessel sections (i.e., aortic annulus: diameter, perimeter, area; aorta/arteries: minimal diameter). Mixed regression models (MRM), including interaction terms and clinical characteristics, were used for comparison. Results Sixty consecutive patients (mean age, 79.4 ± 7.5 years; 28 females, 46.7%) were included. In pre-TAVR CT, the CM reduction to 40 mL is possible without affecting the image quality (MRM: SNR: –1.1, p  = 0.726; CNR: 0.0, p  = 0.999). VMI 40-keV reconstructions showed better results than standard reconstructions with significantly higher SNR (+ 6.04, p  < 0.001). Reduction to 30 mL CM resulted in a significant loss of quality (MRM: SNR: –12.9, p  < 0.001; CNR: –13.9, p  < 0.001), regardless of the reconstruction. Across the reconstructions, we observed no differences in the metric evaluation ( p  > 0.914). Conclusion Among TAVR candidates undergoing pre-interventional CT at a dual-layer spectral detector system, applying 40 mL CM is sufficient to maintain diagnostic image quality. VMI 40-keV reconstructions improve the vessel attenuation and are recommended for evaluation. Clinical relevance statement Contrast medium reduction to 40 mL in pre-interventional transcatheter aortic valve replacement CT using dual-energy CT maintains image quality, while 40-keV virtual monoenergetic imaging reconstructions enhance vessel attenuation. These results offer valuable recommendations for interventional transcatheter aortic valve replacement evaluation and potentially improve nephroprotection in patients with compromised renal function. Key Points • Patients undergoing transcatheter aortic valve replacement (TAVR), requiring pre-interventional CT, are often multimorbid with impaired renal function. • Using a spectral detector dual-layer CT, contrast medium reduction to 40 mL is feasible, maintaining diagnostic image quality. • The additional application of virtual monoenergetic image reconstructions with 40 keV improves vessel attenuation significantly in clinical practice.

ObjectivesTo associate coronary wall volume and composition, derived from coronary computed tomography angiography (CTA), with cardiac allograft vasculopathy (CAV) detected on invasive coronary angiography (ICA) in heart-transplanted (HTX) patients.MethodsWe included consecutive adults who received ICA and coronary CTA for evaluation of CAV ≥ 10 months after HTX. In all coronary segments, we assessed lumen and wall volumes and segmental length, calculated volume-length ratio (VLR) (volumes indexed by segmental length; mm3/mm), wall burden (WB) (wall/wall + lumen volumes; %), and assessed proportions of calcified, fibrotic, fibro-fatty, and low-attenuation tissue (%) in coronary wall. We rendered independent CTA measures associated with CAV by ICA, tested their discriminatory capacity, and assessed concordance between CTA and ICA.ResultsAmong 50 patients (84% men; 53.6 ± 11.9 years), we analyzed 632 coronary segments. Mean interval between HTX and CTA was 6.7 ± 4.7 years and between ICA and CTA 1 (0–1) day. Segmental VLR, WB, and proportion of fibrotic tissue were independently associated with CAV (OR = 1.06–1.27; p ≤ 0.002), reaching a high discriminatory capacity (combination of all three: AUC = 0.84; 95%CI, 0.75–0.90). Concordance between CTA and ICA was higher in advanced CAV (88%) compared with that in none (37%) and mild (19%) CAV. Discordance was primarily driven by a large number of segments with coronary wall changes on CTA but without luminal stenoses on ICA (177/591; 25%).ConclusionCTA-derived coronary wall VLR, WB, and the proportion of fibrotic tissue are independent markers of CAV. Combination of these three parameters may aid the detection of early CAV not detected by ICA, the current standard of care.Key Points• Coronary CTA detects CAV in HTX patients.• Coronary wall volume-length ratio, wall burden, and proportion of fibrotic tissue are independently associated with CAV.• In contrast to ICA, coronary CTA may identify the early stages of CAV.

Neurological impairment is high after preterm birth. This study evaluates the impact and interplay of cMRI-detected brain injuries (BI) and clinical risk factors on neurodevelopmental outcomes and extracts the most important key factors. A retrospective analysis was conducted on risk factors (perinatal/neonatal, cMRI-detected BI) for adverse motor (MO) and cognitive (CO) outcomes (Bayley Scales of Infant Development, 24 months corrected age) in a tertiary center cohort (2009–2018) of very preterm infants (< 32 weeks of gestation) using uni-/multivariable regression models. We included 342 infants (mean gestational age:28.0 ± 2.3 weeks; male:49%). Significant clinical predictors for MO/CO included GA, birthweight, APGAR score, catecholamine treatment, ventilation, retinopathy of prematurity, transfusion of red blood cells (RBCs), bronchopulmonary dysplasia, surgery, and patent ductus arteriosus interventions (all p  < 0.01/ p  < 0.01), surfactant (MO: p  = 0.037), and sepsis ( p  < 0.001/ p  = 0.016). (Severe) cMRI-detected BIs (> 1, all p  < 0.05) and not only severe intraventricular hemorrhage (IVH) III°/III°+PVHI and ventricular dilatation (VD) (all p  < 0.05), but also mild/moderate injuries like IVH II° ( p  < 0.001/ p  < 0.024), cerebellar hemorrhage (CO: p  = 0.028), and moderate VD (MO: p  = 0.005) significantly impacted outcomes. Independent key factors were > 1 severe cMRI-detected BI (MO/CO:-11.27/-10.3 score points (sp), p  = 0.021/0.043), APGAR score (10 min, MO/CO:+5.3/+4.45 sp/point, p  < 0.001/ p  < 0.001), surfactant administration (MO:+4.88 sp, p  = 0.031), and transfusion of RBCs (MO/CO:-1.69/-1.96 sp/transfusion, p  = 0.006/ p  < 0.001). In conclusion, combining imaging and clinical (key) risk factors is important for risk stratification of preterm infants. Even mild BI, like IVH II°, significantly contributes to adverse outcomes, underlining the importance of cMRI.

BackgroundPatients with lung cancer face a heightened risk of atherosclerosis-related cardiovascular events. Despite the strong scientific rationale, there is currently a lack of clinical evidence examining the impact of immune checkpoint inhibitors (ICIs) on the advancement of atherosclerosis in patients with lung cancer. The objective of our study was to investigate whether there is a correlation between ICIs and the accelerated progression of atherosclerosis among individuals with lung cancer.MethodsIn this case–control (2:1 matched by age and gender) study, total, non-calcified, and calcified plaque volumes were measured in the thoracic aorta using sequential contrast-enhanced chest CT scans. Univariate and multivariate rank-based estimation regression models were developed to estimate the effect of ICI therapy on plaque progression in 40 cases (ICI) and 20 controls (non-ICI).ResultsThe patients had a median age of 66 years (IQR: 58–69), with 50% of them being women. At baseline, there were no significant differences in plaque volumes between the groups, and their cardiovascular risk profiles were similar. However, the annual progression rate for non-calcified plaque volume was 7 times higher in the ICI group compared with the controls (11.2% vs 1.6% per year, p=0.001). Conversely, the controls showed a greater progression in calcified plaque volume compared with the ICI group (25% vs 2% per year, p=0.017). In a multivariate model that considered cardiovascular risk factors, the use of an ICI was associated with a more substantial progression of non-calcified plaque volume. Additionally, individuals treated with combination ICI therapy exhibited greater plaque progression.ConclusionsICI therapy was associated with more non-calcified plaque progression. These findings underscore the importance of conducting studies aimed at identifying the underlying mechanisms responsible for plaque advancement in patients undergoing ICI treatment.Trial registration numberNCT04430712.

ObjectivesTo investigate the association between directly measured density and morphology of coronary artery calcium (CAC) with cardiovascular disease (CVD) events, using computed tomography (CT).MethodsFramingham Heart Study (FHS) participants with CAC in noncontrast cardiac CT (2002–2005) were included and followed until 2016. Participants with known CVD or uninterpretable CT scans were excluded. We assessed and correlated (Spearman) CAC density, CAC volume, and the number of calcified segments. Moreover, we counted morphology features including shape (cylindrical, spherical, semi-tubular, and spotty), location (bifurcation, facing pericardium, or facing myocardium), and boundary regularity. In multivariate Cox regression analyses, we associated all CAC characteristics with CVD events (CVD-death, myocardial infarction, stroke).ResultsAmong 1330 included participants (57.8 ± 11.7 years; 63% male), 73 (5.5%) experienced CVD events in a median follow-up of 9.1 (7.8–10.1) years. CAC density correlated strongly with CAC volume (Spearman’s ρ = 0.75; p < 0.001) and lower number of calcified segments (ρ = − 0.86; p < 0.001; controlled for CAC volume). In the survival analysis, CAC density was associated with CVD events independent of Framingham risk score (HR (per SD) = 2.09; 95%CI, 1.30–3.34; p = 0.002) but not after adjustment for CAC volume (p = 0.648). The extent of spherically shaped and pericardially sided calcifications was associated with fewer CVD events accounting for the number of calcified segments (HR (per count) = 0.55; 95%CI, 0.31–0.98; p = 0.042 and HR = 0.66; 95%CI, 0.45–0.98; p = 0.039, respectively).ConclusionsDirectly measured CAC density does not predict CVD events due to the strong correlation with CAC volume. The spherical shape and pericardial-sided location of CAC are associated with fewer CVD events and may represent morphological features related to stable coronary plaques.Key Points• Coronary calcium density may not be independently associated with cardiovascular events.• Coronary calcium density correlates strongly with calcium volume.• Spherical shape and pericardial-sided location of CAC are associated with fewer CVD events.

Background Heavy smokers are at increased risk for cardiovascular disease and may benefit from individualized risk quantification using routine lung cancer screening chest computed tomography. We investigated the prognostic value of deep learning-based automated epicardial adipose tissue quantification and compared it to established cardiovascular risk factors and coronary artery calcium. Methods We investigated the prognostic value of automated epicardial adipose tissue quantification in heavy smokers enrolled in the National Lung Screening Trial and followed for 12.3 (11.9–12.8) years. The epicardial adipose tissue was segmented and quantified on non-ECG-synchronized, non-contrast low-dose chest computed tomography scans using a validated deep-learning algorithm. Multivariable survival regression analyses were then utilized to determine the associations of epicardial adipose tissue volume and density with all-cause and cardiovascular mortality (myocardial infarction and stroke). Results Here we show in 24,090 adult heavy smokers (59% men; 61 ± 5 years) that epicardial adipose tissue volume and density are independently associated with all-cause (adjusted hazard ratios: 1.10 and 1.38; P  < 0.001) and cardiovascular mortality (adjusted hazard ratios: 1.14 and 1.78; P  < 0.001) beyond demographics, clinical risk factors, body habitus, level of education, and coronary artery calcium score. Conclusions Our findings suggest that automated assessment of epicardial adipose tissue from low-dose lung cancer screening images offers prognostic value in heavy smokers, with potential implications for cardiovascular risk stratification in this high-risk population. Plain Language Summary Heavy smokers are at increased risk of poor health outcomes, particularly outcomes related to cardiovascular disease. We explore how fat surrounding the heart, known as epicardial adipose tissue, may be an indicator of the health of heavy smokers. We use an artificial intelligence system to measure the heart fat on chest scans of heavy smokers taken during a lung cancer screening trial and following their health for 12 years. We find that higher amounts and denser epicardial adipose tissue are linked to an increased risk of death from any cause, specifically from heart-related issues, even when considering other health factors. This suggests that measuring epicardial adipose tissue during lung cancer screenings could be a valuable tool for identifying heavy smokers at greater risk of heart problems and death, possibly helping to guide their medical management and improve their cardiovascular health. Foldyna et al. demonstrate that deep learning-based quantification of epicardial adipose tissue from low-dose chest CT scans independently predicts all-cause and cardiovascular mortality in heavy smokers. This can enhance cardiovascular risk stratification beyond traditional measures.

Objectives The size of the heart may predict major cardiovascular events (MACE) in patients with stable chest pain. We aimed to evaluate the prognostic value of 3D whole heart volume (WHV) derived from non-contrast cardiac computed tomography (CT). Methods Among participants randomized to the CT arm of the Prospective Multicenter Imaging Study for Evaluation of Chest Pain (PROMISE), we used deep learning to extract WHV, defined as the volume of the pericardial sac. We compared the WHV across categories of cardiovascular risk factors and coronary artery disease (CAD) characteristics and determined the association of WHV with MACE (all-cause death, myocardial infarction, unstable angina; median follow-up: 26 months). Results In the 3798 included patients (60.5 ± 8.2 years; 51.5% women), the WHV was 351.9 ± 57.6 cm 3 /m 2 . We found smaller WHV in no- or non-obstructive CAD, women, people with diabetes, sedentary lifestyle, and metabolic syndrome. Larger WHV was found in obstructive CAD, men, and increased atherosclerosis cardiovascular disease (ASCVD) risk score ( p < 0.05). In a time-to-event analysis, small WHV was associated with over 4.4-fold risk of MACE (HR (per one standard deviation) = 0.221; 95% CI: 0.068–0.721; p = 0.012) independent of ASCVD risk score and CT-derived CAD characteristics. In patients with non-obstructive CAD, but not in those with no- or obstructive CAD, WHV increased the discriminatory capacity of ASCVD and CT-derived CAD characteristics significantly. Conclusions Small WHV may represent a novel imaging marker of MACE in stable chest pain. In particular, WHV may improve risk stratification in patients with non-obstructive CAD, a cohort with an unmet need for better risk stratification. Key Points • Heart volume is easily assessable from non-contrast cardiac computed tomography. • Small heart volume may be an imaging marker of major adverse cardiac events independent and incremental to traditional cardiovascular risk factors and established CT measures of CAD. • Heart volume may improve cardiovascular risk stratification in patients with non-obstructive CAD.

AimsTo examine the functional and hemodynamic mid-term outcome at 5 months of mitral regurgitation (MR) reduction using the PASCAL repair system.Methods and resultsBetween July 2019 and February 2020 31 consecutive patients with MR 3 +/4 + (mean age 77.5 years, all in New York Heart Association (NYHA) class III–IV, STS score 9.1 ± 7.4) underwent MR reduction in our institute using the PASCAL device. 61.3% had a functional, 29.0% a degenerative, and 9.7% a mixed etiology. Successful implantation was achieved in 30/31 (96.8%) patients. 27/31 patients (87.1%) completed 5-month follow-up with clinical, echocardiographic, laboratory and hemodynamic assessment. At 5 months, 70.4% of the patients had MR grade ≤ 1 (p < 0.001). 85.2% were in NYHA class I or II (p < 0.001). Six-minute walk distance improved by 145 m (p = 0.010), Kansas City cardiomyopathy questionnaire and European quality of life 5 dimensions questionnaire (EQ5D) improved by 31 (p < 0.001) and 9 points (p = 0.001), respectively. Mean pulmonary capillary wedge pressure decreased significantly from 22.1 ± 9 mmHg to 17.3 ± 8 mmHg (p = 0.041) and right atrial pressure from 10.3 ± 6 mmHg to 8.0 ± 6 mmHg (p = 0.013) from baseline to 5 months. In addition, propensity score matching showed that PASCAL and MitraClip procedures resulted in equally hemodynamic and functional improvement.ConclusionMR reduction of severe MR with the PASCAL device is feasible and safe regardless of etiologies. Mid-term follow-up at 5 months showed a sustained MR reduction, improvement of exercise capacity, quality of life, proBNP levels and hemodynamics regarding pulmonary capillary wedge pressure and right atrial pressure.Graphic abstract

Although artificial intelligence algorithms are often developed and applied for narrow tasks, their implementation in other medical settings could help to improve patient care. Here we assess whether a deep-learning system for volumetric heart segmentation on computed tomography (CT) scans developed in cardiovascular radiology can optimize treatment planning in radiation oncology. The system was trained using multi-center data ( n  = 858) with manual heart segmentations provided by cardiovascular radiologists. Validation of the system was performed in an independent real-world dataset of 5677 breast cancer patients treated with radiation therapy at the Dana-Farber/Brigham and Women’s Cancer Center between 2008–2018. In a subset of 20 patients, the performance of the system was compared to eight radiation oncology experts by assessing segmentation time, agreement between experts, and accuracy with and without deep-learning assistance. To compare the performance to segmentations used in the clinic, concordance and failures (defined as Dice < 0.85) of the system were evaluated in the entire dataset. The system was successfully applied without retraining. With deep-learning assistance, segmentation time significantly decreased (4.0 min [IQR 3.1–5.0] vs. 2.0 min [IQR 1.3–3.5]; p  < 0.001), and agreement increased (Dice 0.95 [IQR = 0.02]; vs. 0.97 [IQR = 0.02], p  < 0.001). Expert accuracy was similar with and without deep-learning assistance (Dice 0.92 [IQR = 0.02] vs. 0.92 [IQR = 0.02]; p  = 0.48), and not significantly different from deep-learning-only segmentations (Dice 0.92 [IQR = 0.02]; p  ≥ 0.1). In comparison to real-world data, the system showed high concordance (Dice 0.89 [IQR = 0.06]) across 5677 patients and a significantly lower failure rate ( p  < 0.001). These results suggest that deep-learning algorithms can successfully be applied across medical specialties and improve clinical care beyond the original field of interest.