Machine Learning-Based Identification of Cardiomyopathy Risk in Childhood Cancer Survivors

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PROJECT SUMMARY / ABSTRACT Treatment-related cardiomyopathy/heart failure (CHF) is a leading cause of premature morbidity in childhood cancer survivors. Given the widespread use of anthracycline and related cardiotoxic chemotherapeutics, and in combination with radiotherapy exposure to the chest, over half of long-term survivors of childhood cancer are at significantly increased risk of early CHF compared with an age-matched general population. Currently, national and international consensus guidelines recommend the routine use of 2-dimensional (2D) echocardiography to screen this high-risk population for early signs of CHF, in particular, left ventricular (LV) systolic dysfunction and changes in LV geometry. At present, 2D echocardiography represents the standard of care across the US given its widespread availability, relatively lower cost, and avoidance of ionizing radiation or sedation. Nevertheless, limitations of 2D echocardiography include greater intra-patient and inter-observer variability. As a result, current echocardiography-based surveillance continues to have limited sensitivity and often requires serial studies before a patient is identified as having a potential abnormality. Although there is insufficient evidence to guide CHF management specific to pediatric cancer survivors, the evidence for non-cancer-related cardiomyopathy in both children and adults suggests that earlier intervention can mitigate or delay CHF progression. Therefore, methods that improve the detection of early CHF in childhood cancer survivors may have important clinical implications. Deep learning (DL), a subfield of machine learning, can automatically extract patterns from large unstructured datasets, such as medical images, and is increasingly being utilized in medicine for disease diagnosis as well as disease onset and outcome prediction. We propose to leverage a unique imaging dataset we have assembled from the Children’s Oncology Group (COG), a part of the NCI-sponsored National Clinical Trials Network and Community Oncology Research Program, to explore the potential of DL for enhanced detection of CHF. We have longitudinal echocardiographic data on over 100 survivors of childhood cancer who developed CHF and over 350 who did not, all defined using standardized criteria, representing an imaging repository of >3000 individual echocardiograms (and growing). Using this extant and clinically annotated dataset, we propose to: 1) Using a deep convolutional neural network (DCNN), identify the optimal process for a DL- based assessment of CHF in pediatric cancer survivors; and 2) Assess the feasibility and preliminary efficacy of DCNN-based prediction of cardiomyopathy onset from pre-CHF diagnosis echocardiograms. Expected results include the development of a DCNN that will differentiate between abnormal and normal echocardiograms from pediatric cancer survivors with and without CHF, respectively. After optimization, we will conduct a preliminary efficacy analysis to determine how many years in advance a survivor's transition to CHF can be predicted using an optimized DCNN.