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Predictive Analytics

Artificial Intelligence Assisted Prediction of Late Onset Cardiomyopathy among Childhood Cancer Survivors


PI: Fatma Gunturken
Co-Invgestigators (UTHSC CBMI): Robert L. Davis, Oguz Akbilgic
Co-Investigators (St. Jude): Gregory T. Armstrong, John Jefferies, Kirsten K. Ness, Daniel M. Green, John Lucas, Deokumar Srivastava, Melissa M. Hudson, Leslie L Robison, Daniel Mulrooney, Elsayed Z. Soliman , Ibrahim Karabayir

Early identification of childhood cancer survivors at high risk for treatment-related cardiomyopathy may improve outcomes by enabling timely intervention.  We plan to implement deep learning and signal processing methods using the Children’s Oncology Group (COG) guideline-recommended baseline electrocardiography (ECG) to predict future cardiomyopathy. We will apply signal processing and deep learning tools to 12-lead electrocardiogarms (ECG) obtained on 1,217 adult survivors of childhood cancer who are ≥ 18 years of age and ≥ 10 years from diagnosis. Subjects will be limited to those without evidence of cardiomyopathy at baseline and who are prospectively followed in the St. Jude Lifetime Cohort (SJLIFE) Study. Data to be used includes clinical and echocardiographic assessment of cardiac function  performed at baseline and follow-up evaluations and graded per a modified version of the Common Terminology Criteria for Adverese Events (CTCAE). Machine learning approaches will include genetic algorithm and extreme gradient boosting (XGboost).

Last Published: May 26, 2022