Echocardiography
Early Prediction of Cardiomyopathy in Pediatric Cancer Survivors Using Echocardiographic Video-Based Deep Learning
By Sharma S, Martinez Di Pietro B, Nandula A and 17 others.
Abstract
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.