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Ahn S, Chang Y, Kwon R, Kang J, Choi J, Lim GY, Kwon MR, Ryu S, Shin J. Mammography-based deep learning model for coronary artery calcification. Eur Heart J Cardiovasc Imaging 2024; 25:456-466. [PMID: 37988168 DOI: 10.1093/ehjci/jead307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 09/30/2023] [Accepted: 11/08/2023] [Indexed: 11/23/2023] Open
Abstract
AIMS Mammography, commonly used for breast cancer screening in women, can also predict cardiovascular disease. We developed mammography-based deep learning models for predicting coronary artery calcium (CAC) scores, an established predictor of coronary events. METHODS AND RESULTS We evaluated a subset of Korean adults who underwent image mammography and CAC computed tomography and randomly selected approximately 80% of the participants as the training dataset, used to develop a convolutional neural network (CNN) to predict detectable CAC. The sensitivity, specificity, area under the receiver operating characteristic curve (AUROC), and overall accuracy of the model's performance were evaluated. The training and validation datasets included 5235 and 1208 women, respectively [mean age, 52.6 (±10.2) years], including non-zero cases (46.8%). The CNN-based deep learning prediction model based on the Resnet18 model showed the best performance. The model was further improved using contrastive learning strategies based on positive and negative samples: sensitivity, 0.764 (95% CI, 0.667-0.830); specificity, 0.652 (95% CI, 0.614-0.710); AUROC, 0.761 (95% CI, 0.742-0.780); and accuracy, 70.8% (95% CI, 68.8-72.4). Moreover, including age and menopausal status in the model further improved its performance (AUROC, 0.776; 95% CI, 0.762-0.790). The Framingham risk score yielded an AUROC of 0.736 (95% CI, 0.712-0.761). CONCLUSION Mammography-based deep learning models showed promising results for predicting CAC, performing comparably to conventional risk models. This indicates mammography's potential for dual-risk assessment in breast cancer and cardiovascular disease. Further research is necessary to validate these findings in diverse populations, with a particular focus on representation from national breast screening programmes.
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Affiliation(s)
- Sangil Ahn
- Department of Electrical and Computer Engineering, Sungkyunkwan University, 2066, Seobu-Ro, Jangan-Gu, Suwon 16149, Republic of Korea
| | - Yoosoo Chang
- Center for Cohort Studies, Total Healthcare Center, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul 04514, Republic of Korea
- Department of Occupational and Environmental Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Samsung Main Building B2, 250, Taepyung-ro 2ga, Jung-gu, Seoul 04514, Republic of Korea
- Department of Clinical Research Design & Evaluation, SAIHST, Sungkyunkwan University, 81 Irwon-Ro, Gangnam-Gu, Seoul 06351, Republic of Korea
| | - Ria Kwon
- Center for Cohort Studies, Total Healthcare Center, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul 04514, Republic of Korea
- Institute of Medical Research, Sungkyunkwan University School of Medicine, Suwon 16419, Republic of Korea
| | - Jeonggyu Kang
- Center for Cohort Studies, Total Healthcare Center, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul 04514, Republic of Korea
| | - JunHyeok Choi
- School of Mechanical Engineering, Sungkyunkwan University, Republic of Korea
| | - Ga-Young Lim
- Center for Cohort Studies, Total Healthcare Center, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul 04514, Republic of Korea
- Institute of Medical Research, Sungkyunkwan University School of Medicine, Suwon 16419, Republic of Korea
| | - Mi-Ri Kwon
- Department of Radiology, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Seungho Ryu
- Center for Cohort Studies, Total Healthcare Center, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul 04514, Republic of Korea
- Department of Occupational and Environmental Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Samsung Main Building B2, 250, Taepyung-ro 2ga, Jung-gu, Seoul 04514, Republic of Korea
- Department of Clinical Research Design & Evaluation, SAIHST, Sungkyunkwan University, 81 Irwon-Ro, Gangnam-Gu, Seoul 06351, Republic of Korea
| | - Jitae Shin
- Department of Electrical and Computer Engineering, Sungkyunkwan University, 2066, Seobu-Ro, Jangan-Gu, Suwon 16149, Republic of Korea
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