Deep Learning Coronary Artery Calcium Scores from SPECT/CT Attenuation Maps Improve Prediction of Major Adverse Cardiac Events

Miller, Robert J.H.; Pieszko, Konrad; Shanbhag, Aakash; Fehér, Attila; Lemley, Mark; Killekar, Aditya; Kavanagh, Paul; Kriekinge, Serge D. Van; Liang, Jiaming; Huang, Cathleen; Miller, Edward J.; Bateman, Timothy M.; Berman, Daniel S.; Dey, Damini; Slomka, Piotr J.

doi:10.2967/jnumed.122.264423

Cited by 23 publications

(12 citation statements)

References 34 publications

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“…The performance of our model could be further improved by utilizing data from other imaging modalities 26 . For instance, computed tomography attenuation correction scans could be used to automatically calculate calcium score, which could be included in the time-to event model 27 . Finally, the usefulness of the time-to-event predictions has not been evaluated in prospective studies.…”

Section: Discussionmentioning

confidence: 99%

Time and event-specific deep learning for personalized risk assessment after cardiac perfusion imaging

et al. 2023

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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.

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Section: Discussionmentioning

confidence: 99%

Time and event-specific deep learning for personalized risk assessment after cardiac perfusion imaging

et al. 2023

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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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“…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 Coronary Artery Calcium Scores from SPECT/CT Attenuation Maps Improve Prediction of Major Adverse Cardiac Events

Cited by 23 publications

References 34 publications

Time and event-specific deep learning for personalized risk assessment after cardiac perfusion imaging

Time and event-specific deep learning for personalized risk assessment after cardiac perfusion imaging

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

Holistic AI analysis of hybrid cardiac perfusion images for mortality prediction

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