Artificial intelligence model for cardiovascular disease risk prediction in breast cancer patients using electronic
Isha Shah1, Sarah Lucas2, Reece Walsh3
1BC Cancer - Kelowna, 399 Royal Ave, Kelowna, BC V1Y 5L3, Canada; Department of Computer Science, Mathematics, Physics and Statistics, The University of British Columbia, 3333 University Way, Kelowna, BC V1V 1V7, Canada.
Insights
A new deep learning model accurately predicts cardiovascular disease (CVD) mortality in breast cancer patients using CT scans and EHR data. This fusion model offers improved early detection of CVD risk during cancer treatment.
Area of Science:
- Oncology
- Cardiology
- Artificial Intelligence in Medicine
Background:
- Cardiovascular disease (CVD) is a leading cause of death globally and among cancer survivors.
- Early CVD detection is crucial for breast cancer patients undergoing treatment due to cardiotoxicity risks.
Purpose of the Study:
- To develop and evaluate a fusion deep learning model for predicting CVD mortality in breast cancer patients.
- To assess the model's performance using computed tomography (CT) scans and electronic health records (EHR).
Main Methods:
- A fusion deep learning model was developed and tested on a large cohort (23,067 patients).
- The model integrated data from CT scans (∼5 million slices) and EHR documents (∼600,000).
Main Results:
- The fusion model achieved high predictive performance.
- Area Under the Curve (AUC) was 0.946 (95% CI: 0.939–0.950).
- Accuracy was 0.93 (95% CI: 0.92–0.94).
Conclusions:
- Fusion models effectively combine medical images and text data for enhanced predictive tasks.
- This approach demonstrates high accuracy in predicting CVD mortality in breast cancer patients.
Background And Purpose:
Cardiovascular disease (CVD) is the leading cause of death globally [1] as well as the leading cause of death among cancer survivors [2]. The outcomes of CVD mortality among cancer patients, particularly those with breast cancer, highlight the need for early detection of CVD at the beginning of cancer treatment as cardiotoxicity can also lead to accelerated development of chronic diseases, especially in the presence of risk factors [3].
Materials And Methods:
A fusion deep learning model was developed and tested using computed tomography (CT) scans and electronic health records (EHR) for CVD mortality prediction in breast cancer patients undergoing radiation therapy. The model utilizes computed tomography (CT) scans and electronic health records (EHR) for CVD mortality prediction in breast cancer patients undergoing radiation therapy. A cohort of 23,067 patients consisting of ∼5 million CT slices and ∼600,000 EHR documents was used for the model development and testing.
Results:
Performance of the model is assessed using the AUC and accuracy at a 95% confidence level. The fusion model achieves an AUC of 0.946 [0.939---0.950], and accuracy of, 0.93 [0.92 - 0.94] at 95% confidence interval (CI).
Conclusions:
These results show that fusion models can learn versatile representations from medical images and medical text documents and can effectively be combined for tasks like predicting CVD mortality with higher accuracy when employing the appropriate fusion strategy.
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