BACKGROUND: Coronary plaque features are imaging biomarkers of cardiovascular risk, but less is known about sex-specific patterns in their prognostic value. This study aimed to define sex differences in the coronary atherosclerotic phenotypes assessed by artificial intelligence–based quantitative computed tomography (AI-QCT) and the associated risk of major adverse cardiovascular events (MACEs). METHODS: Global multicenter registry including symptomatic patients with suspicion of coronary artery disease referred for coronary computed tomography angiography. AI-QCT analyzed 16 coronary artery disease features. The primary end point was MACE defined as death, myocardial infarction, late revascularization, cerebrovascular events, unstable angina, and congestive heart failure. RESULTS: Among 3551 patients (mean age, 59±12 years; 49.5% women), MACE occurred in 3.2% of women and 6.1% of men during an average follow-up of 4.8±2.2 years. The AI-QCT features total plaque volume, noncalcified plaque, calcified plaque, and percentage atheroma volume were significantly higher in men ( P <0.001), and high-risk plaques were more prevalent (9.2% versus 2.5%; P <0.0001). Independent of age and cardiovascular risk factors, the AI-QCT-derived features of total plaque volume, noncalcified plaque, calcified plaque, and percentage atheroma volume conferred a higher relative risk of MACE in women than men. For every 50-mm ³ increase in total plaque volume, relative risk increased by 17.7% (95% CI, 1.12–1.24) in women versus 5.3% (95% CI, 1.03–1.07) in men ( P _interaction <0.001); for noncalcified plaque, relative risk increased by 27.1% (95% CI, 1.17–1.38) versus 11.6% (95% CI, 1.08–1.15; P _interaction =0.0015); and for calcified plaque, relative risk increased by 22.9% (95% CI, 1.14–1.33) versus 5.4% (95% CI, 1.01–1.10; P _interaction =0.0012), respectively. Similarly, for percentage atheroma volume, the risk was higher in women. The findings remained unchanged when restricted to a secondary composite end point (death and myocardial infarction). CONCLUSIONS: The AI-QCT plaque features, total plaque volume, noncalcified plaque, calcified plaque, and percentage atheroma volume, conferred a higher relative MACE risk in women and may prompt more aggressive antiatherosclerotic therapy and reinforced preventive interventions. REGISTRATION: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT04279496.

The purpose of this study was to evaluate the capabilities of photon-counting (PC) CT combined with artificial intelligence-derived coronary computed tomography angiography (PC-CCTA) stenosis quantification and fractional flow reserve prediction (FFRai) for the assessment of coronary artery disease (CAD) in transcatheter aortic valve replacement (TAVR) work-up. Consecutive patients with severe symptomatic aortic valve stenosis referred for pre-TAVR work-up between October 2021 and June 2023 were included in this retrospective tertiary single-center study. All patients underwent both PC-CCTA and ICA within three months for reference standard diagnosis. PC-CCTA stenosis quantification (at 50% level) and FFRai (at 0.8 level) were predicted using two deep learning models (CorEx, Spimed-AI). Diagnostic performance for global CAD evaluation (at least one significant stenosis ≥ 50% or FFRai ≤ 0.8) was assessed. A total of 260 patients (138 men, 122 women) with a mean age of 78.7 ± 8.1 (standard deviation) years (age range: 51-93 years) were evaluated. Significant CAD on ICA was present in 126/260 patients (48.5%). Per-patient sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy were 96.0% (95% confidence interval [CI]: 91.0-98.7), 68.7% (95% CI: 60.1-76.4), 74.3 % (95% CI: 69.1-78.8), 94.8% (95% CI: 88.5-97.8), and 81.9% (95% CI: 76.7-86.4) for PC-CCTA, and 96.8% (95% CI: 92.1-99.1), 87.3% (95% CI: 80.5-92.4), 87.8% (95% CI: 82.2-91.8), 96.7% (95% CI: 91.7-98.7), and 91.9% (95% CI: 87.9-94.9) for FFRai. Area under the curve of FFRai was 0.92 (95% CI: 0.88-0.95) compared to 0.82 for PC-CCTA (95% CI: 0.77-0.87) (P < 0.001). FFRai-guidance could have prevented the need for ICA in 121 out of 260 patients (46.5%) vs. 97 out of 260 (37.3%) using PC-CCTA alone (P < 0.001). Deep learning-based photon-counting FFRai evaluation improves the accuracy of PC-CCTA ≥ 50% stenosis detection, reduces the need for ICA, and may be incorporated into the clinical TAVR work-up for the assessment of CAD.

The purpose of this study was to evaluate the diagnostic performance of automated deep learning in the detection of coronary artery disease (CAD) on photon-counting coronary CT angiography (PC-CCTA). Consecutive patients with suspected CAD who underwent PC-CCTA between January 2022 and December 2023 were included in this retrospective, single-center study. Non-ultra-high resolution (UHR) PC-CCTA images were analyzed by artificial intelligence using two deep learning models (CorEx, Spimed-AI), and compared to human expert reader assessment using UHR PC-CCTA images. Diagnostic performance for global CAD assessment (at least one significant stenosis ≥ 50 %) was estimated at patient and vessel levels. A total of 140 patients (96 men, 44 women) with a median age of 60 years (first quartile, 51; third quartile, 68) were evaluated. Significant CAD on UHR PC-CCTA was present in 36/140 patients (25.7 %). The sensitivity, specificity, accuracy, positive predictive value), and negative predictive value of deep learning-based CAD were 97.2 %, 81.7 %, 85.7 %, 64.8 %, and 98.9 %, respectively, at the patient level and 96.6 %, 86.7 %, 88.1 %, 53.8 %, and 99.4 %, respectively, at the vessel level. The area under the receiver operating characteristic curve was 0.90 (95 % CI: 0.83-0.94) at the patient level and 0.92 (95 % CI: 0.89-0.94) at the vessel level. Automated deep learning shows remarkable performance for the diagnosis of significant CAD on non-UHR PC-CCTA images. AI pre-reading may be of supportive value to the human reader in daily clinical practice to target and validate coronary artery stenosis using UHR PC-CCTA.

To provide clinically relevant criteria for differentiation between the athlete’s heart and similar appearing hypertrophic (HCM), dilated (DCM), and arrhythmogenic right-ventricular cardiomyopathy (ARVC) in MRI. 40 top-level athletes were prospectively examined with cardiac MR (CMR) in two university centres and compared to retrospectively recruited patients diagnosed with HCM (n = 14), ARVC (n = 18), and DCM (n = 48). Analysed MR imaging parameters in the whole study cohort included morphology, functional parameters and late gadolinium enhancement (LGE). Mean left-ventricular enddiastolic volume index (LVEDVI) was high in athletes (105 ml/m²) but significantly lower compared to DCM (132 ml/m²; p = 0.001). Mean LV ejection fraction (EF) was 61% in athletes, below normal in 7 (18%) athletes vs. EF 29% in DCM, below normal in 46 (96%) patients (p < 0.0001). Mean RV-EF was 54% in athletes vs. 60% in HCM, 46% in ARVC, and 41% in DCM (p < 0.0001). Mean interventricular myocardial thickness was 10 mm in athletes vs. 12 mm in HCM (p = 0.0005), 9 mm in ARVC, and 9 mm in DCM. LGE was present in 1 (5%) athlete, 8 (57%) HCM, 10 (56%) ARVC, and 21 (44%) DCM patients (p < 0.0001). Healthy athletes’ hearts are characterized by both hypertrophy and dilation, low EF of both ventricles at rest, and increased interventricular septal thickness with a low prevalence of LGE. Differentiation of athlete’s heart from other non-ischemic cardiomyopathies in MRI can be challenging due to a significant overlap of characteristics also seen in HCM, ARVC, and DCM.

Detecting ongoing inflammation in myocarditis patients has prognostic relevance, but there are limited data on the detection of chronic myocarditis and its differentiation from healed myocarditis. This study sought to assess the performance of cardiac magnetic resonance (CMR) for the detection of ongoing inflammation and the discrimination of chronic myocarditis from healed myocarditis. Consecutive patients with persistent symptoms (>30 days) suggestive of myocarditis were prospectively enrolled from a single tertiary center. All patients underwent a multiparametric 1.5-T CMR protocol including biventricular strain, T₁/T₂ mapping, and late gadolinium enhancement (LGE). Endomyocardial biopsy was chosen for the reference standard diagnosis. Among 452 consecutive patients, 103 (median age: 50 years; 66 men) had evaluable CMR and cardiopathologic reference diagnosis: 53 (51%) with chronic lymphocytic myocarditis and 50 (49%) with healed myocarditis. T₂ mapping as a single parameter showed the best accuracy in detecting chronic myocarditis, if abnormal in ≥3 segments (92%; 95% CI: 85-97), and provided the best discrimination from healed myocarditis, as defined by the area under the receiver-operating characteristic curve (0.87 [95% CI: 0.79-0.93]; P < 0.001), followed by radial peak systolic strain rate of the left ventricle (0.86) and the right ventricle (0.84); T₁ mapping (0.64), extracellular volume fraction (0.62), and LGE (0.57). Specificity increased when T₂ mapping was combined with elevation of either troponin or C-reactive protein. A multiparametric CMR protocol allows detection of ongoing myocardial inflammation and discrimination of chronic myocarditis from healed myocarditis, with segmental T₂ mapping and biventricular strain analysis showing higher diagnostic accuracy compared with T₁ mapping, extracellular volume fraction, and LGE. The use of biomarkers (troponin or C-reactive protein) may improve specificity.

The purpose of this study was to compare a conventional T1-weighted volumetric interpolated breath-hold examination (VIBE) sequence with a DL-reconstructed accelerated high-resolution VIBE sequence (HR-VIBE_DL) in terms of image quality, lesion conspicuity, and lesion detection. Consecutive patients referred for upper abdominal MRI between December 2023 and March 2024 at a single tertiary center were prospectively enrolled. Participants underwent 1.5 T upper abdominal MRI with acquisition of spectrally fat-saturated unenhanced and gadobutrol-enhanced conventional VIBE (fourfold acceleration, 3.0 mm slice thickness, 72 axial slices) and HR-VIBE_DL (sixfold acceleration, 2.0 mm, 108 slices). Both sequences had an identical acquisition time of 16 s. Image analysis was performed by three readers in a blinded and randomized fashion, with respect to image quality, lesion conspicuity, and lesion detection in liver, pancreas, spleen, lymph nodes and adrenal glands. Image quality parameters were compared using repeated measures analysis of variance. Lesion detection rates were compared using Fisher exact test. Inter-reader agreement was assessed using Fleiss κ test. Among 744 consecutive patients, 50 participants were evaluated. There were 30 men and 20 women, with a mean age of 60 ± 15 (standard deviation [SD]) years (age range: 18-88 years). HR-VIBE_DL images demonstrated superior signal-to-noise ration and edge sharpness by comparison with conventional VIBE images (P < 0.001 for both), with substantial interreader agreement (κ: 0.70-0.90). Lesion conspicuity was higher with for HR-VIBE_DL images (3.50 ± 0.83 [SD]) by comparison with conventional VIBE images (3.21 ± 0.98 [SD]) (P = 0.005). There were 171 upper abdominal lesions, yielding a total of 513 for all three readers. HR-VIBE_DL images yielded higher lesion detection rate (97.5 %; 500/513) compared to conventional VIBE images (93.2 %; 478/513) (P = 0.002). HR-VIBE_DL images of the upper abdomen result in superior image quality, better lesion conspicuity, and improved lesion detection without time penalty by comparsion with conventional VIBE images.