Early Prediction of Cardiomyopathy in Pediatric Cancer Survivors Using Echocardiographic Video-Based Deep Learning.
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PURPOSE: Cardiomyopathy (CM) is a leading cause of late mortality in childhood cancer survivors, but it is often identified only after irreversible cardiac remodeling has already occurred. In this proof-of-concept study, we assessed whether deep learning applied to routine echocardiographic videos can identify features predictive of future CM. METHODS: We analyzed parasternal short-axis clips from cancer survivors ≤21 years old at cancer diagnosis. 3D deep convolutional neural networks were trained to classify CM-positive versus CM-negative studies from echocardiograms conducted up to five years prior to CM diagnosis. The performance was assessed on internal hold-out validation (Center 1) and external validation cohorts (Centers 2, 3). The model's prognostic value was evaluated by stratifying patients into high- and low-risk groups for developing CM, and probability of future CM was assessed using cumulative incidence and proportional hazards regression analysis. RESULTS: The model achieved an area under the receiver operating characteristic curve (AUROC) of 0.87±0.05 and an area under the precision-recall curve (AUPRC) of 0.74±0.07 on the internal hold-out set. External validation showed near-chance single-study discrimination (AUROC 0.55±0.02 and 0.60±0.03; AUPRC 0.22±0.02 and 0.21±0.03 for Centers 2 and 3, respectively). Nevertheless, analysis of the external cohort showed significantly higher probability of CM in high- versus low-risk groups (hazard ratio 2.78, 95% CI = 1.52-5.10, p = 0.00056). CONCLUSION: Deep learning models have the potential to identify subtle echocardiographic markers years before CM diagnosis in pediatric cancer survivors and could serve as a preliminary step toward risk-guided CM surveillance. Further refinement and validation are needed to determine the utility of deep learning for early identification of pathologic cardiac remodeling in this population.