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Chest computed tomography is one of the most common diagnostic tests, with 15 million scans performed annually in the United States. Coronary calcium can be visualized on these scans, but other measures of cardiac risk such as atrial and ventricular volumes have classically required administration of contrast. Here we show that a fully automated pipeline, incorporating two artificial intelligence models, automatically quantifies coronary calcium, left atrial volume, left ventricular mass, and other cardiac chamber volumes in 29,687 patients from three cohorts. The model processes chamber volumes and coronary artery calcium with an end-to-end time of ~18 s, while failing to segment only 0.1% of cases. Coronary calcium, left atrial volume, and left ventricular mass index are independently associated with all-cause and cardiovascular mortality and significantly improve risk classification compared to identification of abnormalities by a radiologist. This automated approach can be integrated into clinical workflows to improve identification of abnormalities and risk stratification, allowing physicians to improve clinical decision-making. Chest computed tomography (CT) is one of the most common diagnostic tests. Here, the authors combine two AI models to measure from CT coronary artery calcium, left ventricular mass index, and left and right atrial and ventricular volumes, and show their association with cardiovascular mortality.

Background Relative myocardial perfusion imaging (MPI) is the standard imaging approach for the diagnosis and prognostic work-up of coronary artery disease (CAD). However, this technique may underestimate the extent of disease in patients with 3-vessel CAD. Positron emission tomography (PET) is also able to quantify myocardial blood flow. Rubidium-82 ( 82 Rb) is a valid PET tracer alternative in centers that lack a cyclotron. The aim of this study was to assess whether assessment of myocardial flow reserve (MFR) measured with 82 Rb PET is an independent predictor of severe obstructive 3-vessel CAD. Methods We enrolled a cohort of 120 consecutive patients referred to a dipyridamole 82 Rb PET MPI for evaluation of ischemia neither with prior coronary artery bypass graft nor with recent percutaneous coronary intervention that also underwent coronary angiogram within 6 months of the PET study. Patients with and without 3-vessel CAD were compared. Results Among patients with severe 3-vessel CAD, MFR was globally reduced (<2) in 88% (22/25). On the adjusted logistic Cox model, MFR was an independent predictor of 3-vessel CAD [.5 unit decrease, HR: 2.1, 95% CI (1.2-3.8); P  = .015]. The incremental value of 82 Rb MFR over the SSS was also shown by comparing the adjusted SSS models with and without 82 Rb MFR ( P  = .005). Conclusion 82 Rb MFR is an independent predictor of 3-vessel CAD and provided added value to relative MPI. Clinical integration of this approach should be considered to enhance detection and risk assessment of patients with known or suspected CAD.

BackgroundCardiac magnetic resonance perfusion studies with adenosine stress have shown that splenic response can identify patients with inadequate pharmacologic stress. We investigate the incremental prognostic impact of splenic response ratio (SRR) in patients with normal Rubidium (Rb)-82 PET myocardial perfusion imaging (MPI).MethodsConsecutive patients undergoing dipyridamole Rb-82 PET MPI for the evaluation of coronary artery disease were screened. Spleen and liver Rb-82 activity was measured and the SRR was calculated: SRR = (Spleen stress/Liver stress)/(Spleen rest/Liver rest). Major adverse cardiac events (MACE) were determined at 1 year of follow-up in patients with normal summed stress score and normal summed difference score.ResultsOf the 839 patients screened, the spleen was visualized in 703 (84%) of scans. There was significantly higher MACE observed in splenic non-responders vs splenic responders in both the normal SSS (7.8% vs 2.9%, P = .027) and the normal SDS groups (7.4% vs 2.2%, P = .014). In multivariate analysis in patients with normal SDS, splenic response was a significant, independent predictor of MACE (HR 2.97, 95% CI 1.10 to 8.04, P = .033).ConclusionsSRR is a novel imaging metric to identify patients with sub-maximal vasodilator stress and an incremental prognostic marker in patients with normal SDS and SSS (Clinical Trial Registration: https://clinicaltrials.gov/ct2/show/NCT01128023).

Epicardial adipose tissue (EAT) volume and attenuation are associated with cardiovascular risk, but manual annotation is time-consuming. We evaluated whether automated deep learning-based EAT measurements from ungated computed tomography (CT) are associated with death or myocardial infarction (MI). We included 8781 patients from 4 sites without known coronary artery disease who underwent hybrid myocardial perfusion imaging. Of those, 500 patients from one site were used for model training and validation, with the remaining patients held out for testing ( n  = 3511 internal testing, n  = 4770 external testing). We modified an existing deep learning model to first identify the cardiac silhouette, then automatically segment EAT based on attenuation thresholds. Deep learning EAT measurements were obtained in <2 s compared to 15 min for expert annotations. There was excellent agreement between EAT attenuation (Spearman correlation 0.90 internal, 0.82 external) and volume (Spearman correlation 0.90 internal, 0.91 external) by deep learning and expert segmentation in all 3 sites (Spearman correlation 0.90–0.98). During median follow-up of 2.7 years (IQR 1.6–4.9), 565 patients experienced death or MI. Elevated EAT volume and attenuation were independently associated with an increased risk of death or MI after adjustment for relevant confounders. Deep learning can automatically measure EAT volume and attenuation from low-dose, ungated CT with excellent correlation with expert annotations, but in a fraction of the time. EAT measurements offer additional prognostic insights within the context of hybrid perfusion imaging.

Standard clinical interpretation of myocardial perfusion imaging (MPI) has proven prognostic value for predicting major adverse cardiovascular events (MACE). However, personalizing predictions to a specific event type and time interval is more challenging. We demonstrate an explainable deep learning model that predicts the time-specific risk separately for all-cause death, acute coronary syndrome (ACS), and revascularization directly from MPI and 15 clinical features. We train and test the model internally using 10-fold hold-out cross-validation (n = 20,418) and externally validate it in three separate sites (n = 13,988) with MACE follow-ups for a median of 3.1 years (interquartile range [IQR]: 1.6, 3.6). We evaluate the model using the cumulative dynamic area under receiver operating curve (cAUC). The best model performance in the external cohort is observed for short-term prediction – in the first six months after the scan, mean cAUC for ACS and all-cause death reaches 0.76 (95% confidence interval [CI]: 0.75, 0.77) and 0.78 (95% CI: 0.78, 0.79), respectively. The model outperforms conventional perfusion abnormality measures at all time points for the prediction of death in both internal and external validations, with improvement increasing gradually over time. Individualized patient explanations are visualized using waterfall plots, which highlight the contribution degree and direction for each feature. This approach allows the derivation of individual event probability as a function of time as well as patient- and event-specific risk explanations that may help draw attention to modifiable risk factors. Such a method could help present post-scan risk assessments to the patient and foster shared decision-making.

Nuclear cardiology has experienced exponential growth within the past four decades with converging capacity to diagnose and influence management of a variety of cardiovascular diseases. Single photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) with technetium-99m radiotracers or thallium-201 has dominated the field; however new hardware and software designs that optimize image quality with reduced radiation exposure are fuelling a resurgence of interest at the preclinical and clinical levels to expand beyond MPI. Other imaging modalities including positron emission tomography (PET) and magnetic resonance imaging (MRI) continue to emerge as powerful players with an expanded capacity to diagnose a variety of cardiac conditions. At the forefront of this resurgence is the development of novel target vectors based on an enhanced understanding of the underlying pathophysiological process in the subcellular domain. Molecular imaging with novel radiopharmaceuticals engineered to target a specific subcellular process has the capacity to improve diagnostic accuracy and deliver enhanced prognostic information to alter management. This paper, while not comprehensive, will review the recent advancements in radiotracer development for SPECT and PET MPI, autonomic dysfunction, apoptosis, atherosclerotic plaques, metabolism, and viability. The relevant radiochemistry and preclinical and clinical development in addition to molecular imaging with emerging modalities such as cardiac MRI and PET-MR will be discussed.

Low-dose computed tomography attenuation correction (CTAC) scans are used in hybrid myocardial perfusion imaging (MPI) for attenuation correction and coronary calcium scoring, and contain additional anatomic and pathologic information not utilized in clinical assessment. We seek to uncover the full potential of these scans utilizing a holistic artificial intelligence (AI) approach. A multi-structure model segmented 33 structures and quantified 15 radiomics features in each organ in 10,480 patients from 4 sites. Coronary calcium and epicardial fat measures were obtained from separate AI models. The area under the receiver-operating characteristic curves (AUC) for all-cause mortality prediction of the model utilizing MPI, CT, stress test, and clinical features was 0.80 (95% confidence interval [0.74–0.87]), which was higher than for coronary calcium (0.64 [0.57–0.71]) or perfusion (0.62 [0.55–0.70]), with p  < 0.001 for both. A comprehensive multimodality approach can significantly improve mortality prediction compared to MPI information alone in patients undergoing hybrid MPI.