Deep Learning of Coronary Calcium Scores From PET/CT Attenuation Maps Accurately Predicts Adverse Cardiovascular Events

Pieszko, Konrad; Shanbhag, Aakash; Killekar, Aditya; Miller, Robert J.H.; Lemley, Mark; Otaki, Yuka; Singh, Ananya; Kwieciński, Jacek; Gransar, Heidi; Kriekinge, Serge D. Van; Kavanagh, Paul; Miller, Edward J.; Slomka, Piotr; Liang, Jiaming; Berman, Daniel S.; Dey, Damini; Slomka, Piotr J.

doi:10.1016/j.jcmg.2022.06.006

Cited by 26 publications

(9 citation statements)

References 25 publications

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“…The model incorporates CAC, which is currently not available for most patients presenting with suspected CAD. However, CAC can also be estimated from standard nongated, noncontrast chest computed tomography 30 , 31 or quantified automatically from nongated computed tomography attenuation scans using artificial intelligence 32 , 33 , 34 ; therefore, CAC assessment could potentially be available in a much larger proportion of patients. For example, Peng et al recently demonstrated that CAC could be derived automatically from chest computed tomography scans performed for a variety of reasons to help predict cardiovascular events.…”

Section: Discussionmentioning

confidence: 99%

Simplified Approach to Predicting Obstructive Coronary Disease With Integration of Coronary Calcium: Development and External Validation

Miller,

Gransar,

Rozanski

et al. 2023

JAHA

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Background The Diamond‐Forrester model was used extensively to predict obstructive coronary artery disease (CAD) but overestimates probability in current populations. Coronary artery calcium (CAC) is a useful marker of CAD, which is not routinely integrated with other features. We derived simple likelihood tables, integrating CAC with age, sex, and cardiac chest pain to predict obstructive CAD. Methods and Results The training population included patients from 3 multinational sites (n=2055), with 2 sites for external testing (n=3321). We determined associations between age, sex, cardiac chest pain, and CAC with the presence of obstructive CAD, defined as any stenosis ≥50% on coronary computed tomography angiography. Prediction performance was assessed using area under the receiver‐operating characteristic curves (AUCs) and compared with the CAD Consortium models with and without CAC, which require detailed calculations, and the updated Diamond‐Forrester model. In external testing, the proposed likelihood tables had higher AUC (0.875 [95% CI, 0.862–0.889]) than the CAD Consortium clinical+CAC score (AUC, 0.868 [95% CI, 0.855–0.881]; P =0.030) and the updated Diamond‐Forrester model (AUC, 0.679 [95% CI, 0.658–0.699]; P <0.001). The calibration for the likelihood tables was better than the CAD Consortium model (Brier score, 0.116 versus 0.121; P =0.005). Conclusions We have developed and externally validated simple likelihood tables to integrate CAC with age, sex, and cardiac chest pain, demonstrating improved prediction performance compared with other risk models. Our tool affords physicians with the opportunity to rapidly and easily integrate a small number of important features to estimate a patient's likelihood of obstructive CAD as an aid to clinical management.

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Section: Discussionmentioning

confidence: 99%

Simplified Approach to Predicting Obstructive Coronary Disease With Integration of Coronary Calcium: Development and External Validation

Miller,

Gransar,

Rozanski

et al. 2023

JAHA

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“…97 Coronary calcium scoring can be performed by AI with full automation for both gated and ungated cardiac CT. 15,98 Deep learning–based coronary calcium scoring has been shown to predict future adverse cardiovascular outcomes in large data sets, increasing the value of attenuation correction CT performed for myocardial perfusion positron emission tomography imaging. 15 For cardiac CT angiography, AI can enable automated quantification of luminal stenosis and atherosclerotic plaque, which has been validated against invasive reference standards, 99 and characterization of atherosclerotic plaque into noncalcified, low-attenuation noncalcified, and calcified components. 13 Morphological features of high-risk plaque correspond to future cardiovascular event risk 100 and can be used to guide management, 101 including referral for invasive angiography.…”

Section: Ai Implementation Within the Value Chainmentioning

confidence: 99%

“…13 In addition, quantifying the extent of coronary artery calcium from ungated CT may allow opportunistic cardiovascular risk assessment. 14,15 AI can also assist imagers in optimizing imaging acquisition protocols to achieve higher reproducibility and automate quantitative measurements. 16 For CT, AI could assist imagers with dose optimization by automating patient positioning using the specific acquisition protocol.…”

Section: Imagermentioning

confidence: 99%

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Value Creation Through Artificial Intelligence and Cardiovascular Imaging: A Scientific Statement From the American Heart Association

Hanneman,

Playford,

Dey

et al. 2024

Circulation

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Multiple applications for machine learning and artificial intelligence (AI) in cardiovascular imaging are being proposed and developed. However, the processes involved in implementing AI in cardiovascular imaging are highly diverse, varying by imaging modality, patient subtype, features to be extracted and analyzed, and clinical application. This article establishes a framework that defines value from an organizational perspective, followed by value chain analysis to identify the activities in which AI might produce the greatest incremental value creation. The various perspectives that should be considered are highlighted, including clinicians, imagers, hospitals, patients, and payers. Integrating the perspectives of all health care stakeholders is critical for creating value and ensuring the successful deployment of AI tools in a real-world setting. Different AI tools are summarized, along with the unique aspects of AI applications to various cardiac imaging modalities, including cardiac computed tomography, magnetic resonance imaging, and positron emission tomography. AI is applicable and has the potential to add value to cardiovascular imaging at every step along the patient journey, from selecting the more appropriate test to optimizing image acquisition and analysis, interpreting the results for classification and diagnosis, and predicting the risk for major adverse cardiac events.

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“…Our formerly validated deep learning model was used for CAC segmentation and scoring. 17,18 To segment heart mask and CAC on CTAC images, two convolutional long short-term memory (convLSTM) networks were tested externally on data (10,480 CTAC scans) from 4 different sites. To automatically obtain CAC scores from the deep learning segmentation, established methods were used.…”

Section: Automated Coronary Artery Calcium Scoringmentioning

confidence: 99%

Holistic AI analysis of hybrid cardiac perfusion images for mortality prediction

Michalowska,

Zhang,

Shanbhag

et al. 2024

Preprint

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BackgroundWhile low-dose computed tomography scans are traditionally used for attenuation correction in hybrid myocardial perfusion imaging (MPI), they also 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)-driven image framework for image assessment.MethodsPatients with SPECT/CT MPI from 4 REFINE SPECT registry sites were studied. A multi-structure model segmented 33 structures and quantified 15 radiomics features for each on CT attenuation correction (CTAC) scans. Coronary artery calcium and epicardial adipose tissue scores were obtained from separate deep-learning models. Normal standard quantitative MPI features were derived by clinical software. Extreme Gradient Boosting derived all-cause mortality risk scores from SPECT, CT, stress test, and clinical features utilizing a 10-fold cross-validation regimen to separate training from testing data. The performance of the models for the prediction of all-cause mortality was evaluated using area under the receiver-operating characteristic curves (AUCs).ResultsOf 10,480 patients, 5,745 (54.8%) were male, and median age was 65 (interquartile range [IQR] 57-73) years. During the median follow-up of 2.9 years (1.6-4.0), 651 (6.2%) patients died. The AUC for mortality prediction of the model (combining CTAC, MPI, and clinical data) was 0.80 (95% confidence interval [0.74-0.87]), which was higher than that of an AI CTAC model (0.78 [0.71-0.85]), and AI hybrid model (0.79 [0.72-0.86]) incorporating CTAC and MPI data (p<0.001 for all).ConclusionIn patients with normal perfusion, the comprehensive model (0.76 [0.65-0.86]) had significantly better performance than the AI CTAC (0.72 [0.61-0.83]) and AI hybrid (0.73 [0.62-0.84]) models (p<0.001, for all).CTAC significantly enhances AI risk stratification with MPI SPECT/CT beyond its primary role - attenuation correction. A comprehensive multimodality approach can significantly improve mortality prediction compared to MPI information alone in patients undergoing cardiac SPECT/CT.

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Deep Learning of Coronary Calcium Scores From PET/CT Attenuation Maps Accurately Predicts Adverse Cardiovascular Events

Cited by 26 publications

References 25 publications

Simplified Approach to Predicting Obstructive Coronary Disease With Integration of Coronary Calcium: Development and External Validation

Simplified Approach to Predicting Obstructive Coronary Disease With Integration of Coronary Calcium: Development and External Validation

Value Creation Through Artificial Intelligence and Cardiovascular Imaging: A Scientific Statement From the American Heart Association

Holistic AI analysis of hybrid cardiac perfusion images for mortality prediction

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