Prediction of Coronary Artery Calcium and Cardiovascular Risk on Chest Radiographs Using Deep Learning

Abstract

Purpose: To assess the ability of deep convolutional neural networks (DCNNs) to predict coronary artery calcium (CAC) and cardiovascular risk on chest radiographs.

Materials and methods: In this retrospective study, 1689 radiographs in patients who underwent cardiac CT and chest radiography within the same year, between 2013 and 2018, were included (mean age, 56 years ± 11 [standard deviation]; 969 radiographs in women). Agatston scores were used as ground truth labels for DCNN training on radiographs. DCNNs were trained for binary classification of (a) nonzero or zero total calcium scores, (b) presence or absence of calcium in each coronary artery, and (c) total calcium scores above or below varying thresholds. Results from classification of test images were compared with established 10-year atherosclerotic cardiovascular disease (ASCVD) risk scores in each cohort. Classifier performance was measured using area under the receiver operating characteristic curve (AUC) with attention maps to highlight areas of decision-making.

Results: Binary classification between zero and nonzero total calcium scores reached an AUC of 0.73 on frontal radiographs, with similar performance on laterals (AUC, 0.70; P = .56). Performance was similar for binary classification of absolute total calcium score above or below 100 (AUC, 0.74). Frontal radiographs that tested positive for a predicted nonzero CAC score correlated with a higher 10-year ASCVD risk of 17.2% ± 10.9 compared with 11.9% ± 10.2 for a negative test, indicating predicted CAC score of zero (P < .001). Multivariate logistic regression demonstrated the algorithm could predict a nonzero calcium score independent of traditional cardiovascular risk factors. Performance was reduced for individual coronary arteries. Heat maps primarily localized to the cardiac silhouette and occasionally other cardiovascular findings.

Conclusion: DCNNs trained on chest radiographs had modest accuracy for predicting the presence of CAC correlating with cardiovascular risk.Keywords: Coronary Arteries, Cardiac, Calcifications/Calculi, Neural NetworksSee also the commentary by Gupta and Blankstein in this issue.©RSNA, 2021.

Keywords: Calcifications/Calculi; Cardiac; Coronary Arteries; Neural Networks.

Conflict of interest statement

Disclosures of Conflicts of Interest: P.I.K. disclosed no relevant relationships. P.H.Y. disclosed no relevant relationships. H.I.S. Activities related to the present article: disclosed no relevant relationships. Activities not related to the present article: author received payment from Demler, Armstrong, and Rowland for defense expert testimony; author's institution has grants/grants pending from the National Institutes of Health. Other relationships: disclosed no relevant relationships. C.T.L. disclosed no relevant relationships.

2021 by the Radiological Society of North America, Inc.

Figures

Figure 1:

Images in a 65-year-old woman with a total Agatston calcium score of 6056, with scores of 3387 in the right coronary artery and 1304 in the left anterior descending artery. Posteroanterior and lateral chest radiographs demonstrate very faint parallel curvilinear calcifications along the, A and B, right coronary artery and, D and E, left anterior descending artery distribution, which are much more apparent on,C and F, corresponding chest CT images.

Figure 2:

Histogram demonstrates the distribution of total calcium scores in the data set. Approximately 44% of radiographs (681 of 1543 for which total calcium scores could be computed) were associated with a calcium score of 0.

Figure 3:

Receiver operating characteristic curve for the binary classification of zero and nonzero Agatston scores on frontal chest radiographs, demonstrating an area under the curve (AUC) of 0.73.

Figure 4a:

Attention-based heat maps produced when training models on(a) frontal and (b) lateral chest radiographs labeled with total Agatston scores. Despite training only on calcium score numbers, algorithms learned to focus on and prioritize the cardiac silhouette to predict the presence of coronary artery calcium. Left column, radiographs; middle column, attention-based heat maps; right column, heat maps overlaid on radiographs.

Figure 4b:

Attention-based heat maps produced when training models on(a) frontal and (b) lateral chest radiographs labeled with total Agatston scores. Despite training only on calcium score numbers, algorithms learned to focus on and prioritize the cardiac silhouette to predict the presence of coronary artery calcium. Left column, radiographs; middle column, attention-based heat maps; right column, heat maps overlaid on radiographs.

Figure 5:

Secondary findings associated with coronary artery disease, such as the presence of a cardiac defibrillator, strongly biased the algorithm, as shown in these two examples. Left column, radiographs; middle column, attention-based heat maps; right column, heat maps overlaid on radiographs.