Prior evidence observed no predictive utility of coronary CT angiography (CCTA) over the coronary artery calcium score (CACS) and the Framingham risk score (FRS), among asymptomatic individuals. ...Whether the prognostic value of CCTA differs for asymptomatic patients, when stratified by CACS severity, remains unknown.
From a 12-centre, 6-country observational registry, 3217 asymptomatic individuals without known coronary artery disease (CAD) underwent CACS and CCTA. Individuals were categorized by CACS as: 0-10, 11-100, 101-400, 401-1000, >1000. For CCTA analysis, the number of obstructive vessels-as defined by the per-patient presence of a ≥50% luminal stenosis-was used to grade the extent and severity of CAD. The incremental prognostic value of CCTA over and above FRS was measured by the likelihood ratio (LR) χ(2), C-statistic, and continuous net reclassification improvement (NRI) for prediction, discrimination, and reclassification of all-cause mortality and non-fatal myocardial infarction. During a median follow-up of 24 months (25th-75th percentile, 17-30 months), there were 58 composite end-points. The incremental value of CCTA over FRS was demonstrated in individuals with CACS >100 (LRχ(2), 25.34; increment in C-statistic, 0.24; NRI, 0.62, all P < 0.001), but not among those with CACS ≤100 (all P > 0.05). For subgroups with CACS >100, the utility of CCTA for predicting the study end-point was evident among individuals whose CACS ranged from 101 to 400; the observed predictive benefit attenuated with increasing CACS.
Coronary CT angiography provides incremental prognostic utility for prediction of mortality and non-fatal myocardial infarction for asymptomatic individuals with moderately high CACS, but not for lower or higher CACS.
Pathologic hypertrophy of the cardiac muscle is a commonly encountered phenotype in clinical practice, associated with a variety of structural and non-structural diseases. Coronary microvascular ...disease is considered to play an important role in the natural history of this pathological phenotype. Non-invasive imaging modalities, most prominently positron emission tomography and cardiac magnetic resonance, have provided insights into the pathophysiological mechanisms of the interplay between hypertrophy and the coronary microvasculature. This article summarizes the current knowledge on coronary microvascular dysfunction in the most frequently encountered forms of pathologic hypertrophy.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Explainable artificial intelligence (AI) can be integrated within standard clinical software to facilitate the acceptance of the diagnostic findings during clinical interpretation.
This study sought ...to develop and evaluate a novel, general purpose, explainable deep learning model (coronary artery disease–deep learning CAD-DL) for the detection of obstructive CAD following single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI).
A total of 3,578 patients with suspected CAD undergoing SPECT MPI and invasive coronary angiography within a 6-month interval from 9 centers were studied. CAD-DL computes the probability of obstructive CAD from stress myocardial perfusion, wall motion, and wall thickening maps, as well as left ventricular volumes, age, and sex. Myocardial regions contributing to the CAD-DL prediction are highlighted to explain the findings to the physician. A clinical prototype was integrated using a standard clinical workstation. Diagnostic performance by CAD-DL was compared to automated quantitative total perfusion deficit (TPD) and reader diagnosis.
In total, 2,247 patients (63%) had obstructive CAD. In 10-fold repeated testing, the area under the receiver-operating characteristic curve (AUC) (95% CI) was higher according to CAD-DL (AUC: 0.83 95% CI: 0.82-0.85) than stress TPD (AUC: 0.78 95% CI: 0.77-0.80) or reader diagnosis (AUC: 0.71 95% CI: 0.69-0.72; P < 0.0001 for both). In external testing, the AUC in 555 patients was higher according to CAD-DL (AUC: 0.80 95% CI: 0.76-0.84) than stress TPD (AUC: 0.73 95% CI: 0.69-0.77) or reader diagnosis (AUC: 0.65 95% CI: 0.61-0.69; P < 0.001 for all). The present model can be integrated within standard clinical software and generates results rapidly (<12 seconds on a standard clinical workstation) and therefore could readily be incorporated into a typical clinical workflow.
The deep-learning model significantly surpasses the diagnostic accuracy of standard quantitative analysis and clinical visual reading for MPI. Explainable artificial intelligence can be integrated within standard clinical software to facilitate acceptance of artificial intelligence diagnosis of CAD following MPI.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Objectives
Deep-learning image reconstruction (DLIR) offers unique opportunities for reducing image noise without degrading image quality or diagnostic accuracy in coronary CT angiography (CCTA). The ...present study aimed at exploiting the capabilities of DLIR to reduce radiation dose and assess its impact on stenosis severity, plaque composition analysis, and plaque volume quantification.
Methods
This prospective study includes 50 patients who underwent two sequential CCTA scans at normal-dose (ND) and lower-dose (LD). ND scans were reconstructed with Adaptive Statistical Iterative Reconstruction-Veo (ASiR-V) 100%, and LD scans with DLIR. Image noise (in Hounsfield units, HU) and quantitative plaque volumes (in mm
3
) were assessed quantitatively. Stenosis severity was visually categorized into no stenosis (0%), stenosis (< 20%, 20–50%, 51–70%, 71–90%, 91–99%), and occlusion (100%). Plaque composition was classified as calcified, non-calcified, or mixed.
Results
Reduction of radiation dose from ND scans with ASiR-V 100% to LD scans with DLIR at the highest level (DLIR-H; 1.4 mSv vs. 0.8 mSv,
p
< 0.001) had no impact on image noise (28 vs. 27 HU,
p
= 0.598). Reliability of stenosis severity and plaque composition was excellent between ND scans with ASiR-V 100% and LD scans with DLIR-H (intraclass correlation coefficients of 0.995 and 0.974, respectively). Comparison of plaque volumes using Bland–Altman analysis revealed a mean difference of − 0.8 mm
3
(± 2.5 mm
3
) and limits of agreement between − 5.8 and + 4.1 mm
3
.
Conclusion
DLIR enables a reduction in radiation dose from CCTA by 43% without significant impact on image noise, stenosis severity, plaque composition, and quantitative plaque volume.
Key Points
•
Deep-learning image reconstruction (DLIR) enables radiation dose reduction by over 40% for coronary computed tomography angiography (CCTA)
.
•
Image noise remains unchanged between a normal-dose CCTA reconstructed by ASiR-V and a lower-dose CCTA reconstructed by DLIR
.
•
There is no impact on the assessment of stenosis severity, plaque composition, and quantitative plaque volume between the two scans
.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
The aim of this study was to assess the diagnostic accuracy of dual-source computed tomography (DSCT) for evaluation of coronary artery disease (CAD) in a population with extensive coronary ...calcifications without heart rate control. Thirty patients (24 male, 6 female, mean age 63.1+/-11.3 years) with a high pre-test probability of CAD underwent DSCT coronary angiography and invasive coronary angiography (ICA) within 14+/-9 days. No beta-blockers were administered prior to the scan. Two readers independently assessed image quality of all coronary segments with a diameter > or =1.5 mm using a four-point score (1: excellent to 4: not assessable) and qualitatively assessed significant stenoses as narrowing of the luminal diameter >50%. Causes of false-positive (FP) and false-negative (FN) ratings were assigned to calcifications or motion artifacts. ICA was considered the standard of reference. Mean body mass index was 28.3+/-3.9 kg/m2 (range 22.4-36.3 kg/m2), mean heart rate during CT was 70.3+/-14.2 bpm (range 47-102 bpm), and mean Agatston score was 821+/-904 (range 0-3,110). Image quality was diagnostic (scores 1-3) in 98.6% (414/420) of segments (mean image quality score 1.68+/-0.75); six segments in three patients were considered not assessable (1.4%). DSCT correctly identified 54 of 56 significant coronary stenoses. Severe calcifications accounted for false ratings in nine segments (eight FP/one FN) and motion artifacts in two segments (one FP/one FN). Overall sensitivity, specificity, positive and negative predictive value for evaluating CAD were 96.4, 97.5, 85.7, and 99.4%, respectively. First experience indicates that DSCT coronary angiography provides high diagnostic accuracy for assessment of CAD in a high pre-test probability population with extensive coronary calcifications and without heart rate control.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
•Clinical standard PET images and images with thirtyfold reduced dose were assessed.•Neural networks had a high sensitivity (91.5%–95.9%) for lung cancer detection.•Machine learning may be helpful in ...the detection of lung cancer in low dose FDG-PET.
We evaluated whether machine learning may be helpful for the detection of lung cancer in FDG-PET imaging in the setting of ultralow dose PET scans.
We studied the performance of an artificial neural network discriminating lung cancer patients (n = 50) from controls (n = 50) without pulmonary malignancies. A total of 3936 PET slices including images in which the lung tumor is visually present and image slices of patients with no lung cancer were exported. The diagnostic performance of the artificial neural network based on clinical standard dose PET images (PET100%) as well as with a tenfold (PET10%) and thirtyfold (PET3.3%) reduced radiation dose (∼0.11 mSv) was assessed.
The area under the curve of the deep learning algorithm for lung cancer detection was 0.989, 0.983 and 0.970 for standard dose images (PET100%), and reduced dose PET10%, and PET3.3% reconstruction, respectively. The artificial neural network achieved a sensitivity of 95.9% and 91.5% and a specificity of 98.1% and 94.2%, at standard dose and ultralow dose PET3.3%, respectively.
Our results suggest that machine learning algorithms may aid fully automated lung cancer detection even at very low effective radiation doses of 0.11 mSv. Further improvement of this technology might improve the specificity of lung cancer screening efforts and could lead to new applications of FDG-PET.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
Abstract Objectives The goal of this study was to determine the long-term prognostic value of coronary computed tomography angiography (CTA) among patients with diabetes mellitus (DM) compared with ...nondiabetic subjects. Background The long-term prognostic value of coronary CTA in patients with DM is not well established. Methods Patients enrolled in the CONFIRM (Coronary CT Angiography Evaluation for Clinical Outcomes: An International Multicenter) registry with 5-year follow-up data were identified. The extent and severity of coronary artery disease (CAD) were analyzed at baseline coronary CTA and in relation to outcomes between diabetic and nondiabetic patients. CAD according to coronary CTA was defined as none (0% stenosis), nonobstructive (1% to 49% stenosis), or obstructive (≥50% stenosis). Time to death (and in a subgroup, time to major adverse cardiovascular event) was estimated by using multivariable Cox proportional hazards models. Results A total of 1,823 patients were identified as having DM with 5-year clinical follow-up and were propensity-matched to 1,823 patients without DM (mean age 61.8 ± 10.9 years; 54.4% male). Patients with DM did not exhibit a heightened risk of death compared with the propensity-matched nondiabetic subjects in the absence of CAD on coronary CTA (risk-adjusted hazard ratio HR of DM: 1.32; 95% confidence interval CI: 0.78 to 2.24; p = 0.296). Patients with DM were at increased risk of dying compared with nondiabetic subjects in the setting of nonobstructive CAD (in the propensity-matched cohort: HR, 2.10; 95% CI: 1.43 to 3.09; p < 0.001) with a mortality risk greater than nondiabetic subjects with obstructive disease (p < 0.001). In a risk-adjusted hazard analysis among patients with DM, both per-patient obstructive CAD and nonobstructive CAD conferred an increase in all-cause mortality risk compared with patients without atherosclerosis on coronary CTA (nonobstructive disease—HR: 2.07; 95% CI: 1.33 to 3.24; p = 0.001; obstructive disease—HR: 2.22; 95% CI: 1.47 to 3.36; p < 0.001). Conclusions Among patients with DM, nonobstructive and obstructive CAD according to coronary CTA were associated with higher rates of all-cause mortality and major adverse cardiovascular events at 5 years, and this risk was significantly higher than in nondiabetic subjects. Importantly, patients with DM without CAD according to coronary CTA were at a risk comparable to that of nondiabetic subjects.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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