Key result
Deep neural network detects cardiomyopathy from echocardiogram montages with 0.89 AUROC.
Why the study?
Despite routine echocardiographic surveillance in childhood cancer survivors, the ability to predict individual cardiomyopathy risk remains limited.
Can deep convolutional neural networks accurately predict the development of cancer therapy-related cardiomyopathy in at-risk childhood cancer survivors using serial echocardiograms?
Observational (n=115)
Yes
Can deep convolutional neural networks accurately predict the development of cancer therapy-related cardiomyopathy in at-risk childhood cancer survivors using serial echocardiograms?
Deep convolutional neural networks using routine echocardiogram images show promise in accurately predicting cancer therapy-related cardiomyopathy in childhood cancer survivors.
May aid cardiomyopathy screening in survivors; hypothesis-generating and requires prospective validation.
BACKGROUND: Despite routine echocardiographic surveillance for childhood cancer survivors, the ability to predict cardiomyopathy risk in individual patients is limited. We explored the feasibility and optimal processes for machine learning-enhanced cardiomyopathy prediction in survivors using serial echocardiograms from five centers. METHODS: We designed a series of deep convolutional neural networks (DCNNs) for prediction of cardiomyopathy (shortening fraction ≤ 28% or ejection fraction ≤ 50% on two occasions) for at-risk survivors ≥ 1-year post initial cancer therapy. We built DCNNs with four subsets of echocardiographic data differing in timing relative to case (survivor who developed cardiomyopathy) index diagnosis and two input formats (montages) with differing image selections. We used holdout subsets in a 10-fold cross-validation framework and standard metrics to assess model performance (e.g., F1-score, area under the precision-recall curve [AUPRC]). Performance of the input formats was compared using a combined 5 × 2 cross-validation F-test. RESULTS: The dataset included 542 pairs of montages: 171 montage pairs from 45 cases at time of cardiomyopathy diagnosis or pre-diagnosis and 371 pairs from 70 at-risk survivors who didn't develop cardiomyopathy during follow-up (non-case). The DCNN trained to distinguish between non-case and time of cardiomyopathy diagnosis or pre-diagnosis case montages achieved an AUROC of 0.89 ± 0.02, AUPRC 0.83 ± 0.03, and F1-score: 0.76 ± 0.04. When limited to smaller subsets of case data (e.g., ≥ 1 or 2 years pre-diagnosis), performance worsened. Model input format did not impact performance accuracy across models. CONCLUSIONS: This methodology is a promising first step toward development of a DCNN capable of accurately differentiating pre-diagnosis versus non-case echocardiograms to predict survivors more likely to develop cardiomyopathy.
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Edwards et al. (2024) conducted an observational in Cancer therapy-related cardiomyopathy (n=115). Deep convolutional neural network (DCNN) using echocardiogram montages was evaluated on Prediction of cardiomyopathy (shortening fraction ≤ 28% or ejection fraction ≤ 50% on two occasions) (AUROC 0.89). A deep convolutional neural network trained on echocardiogram montages distinguished between non-case and cardiomyopathy case echocardiograms with an AUROC of 0.89.
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