The Division of Biostatistics at the Department of Preventive Medicine invites you to attend the following seminar.
Time: June 29th at 2 P.M.
Presenter: Dr. Fatma Gunturkun (UTHSC CBMI)
Title: “Artificial Intelligence for Prediction of Late Onset Cardiomyopathy among Childhood Cancer Survivors”
Background: Early identification of childhood cancer survivors at high risk for treatment-related cardiomyopathy may improve outcomes by enabling timely intervention. We implemented deep learning and signal processing methods using the Children’s Oncology Group (COG) guideline-recommended baseline electrocardiography (ECG) to predict future cardiomyopathy.
Methods: Signal processing and deep learning tools were applied to 12-lead electrocardiogarms (ECG) obtained on 1,217 adult survivors (≥ 18 years of age, ≥ 10 years from diagnosis) of childhood cancer, without evidence of cardiomyopathy, prospectively followed in the St. Jude Lifetime Cohort (SJLIFE) Study. Clinical and echocardiographic assessment of cardiac function was performed at baseline and follow-up evaluations and graded per a modified version of the Common Terminology Criteria for Adverse Events (CTCAE). Extreme gradient boosting (XGboost) algorithms were applied, and model performance evaluated by 5-fold stratified cross validation.
Results: Median age at baseline evaluation was 31.7 years (range 18.4-66.4), and median age at cancer diagnosis was 8.4 years (range 0.01 – 22.7). The average length of follow-up time following baseline SJLIFE evaluation was 5 years (0.5-9). Among survivors, 67.1% were exposed to chest radiation (median dose of 1,200 cGy (4-6,200 cGy) and 76.6% were exposed to anthracyclines (mean dose of 168.7 mg/m2 (35.1-734.2 mg/m2). A total of 117 (9.6%) survivors developed cardiomyopathy during follow-up. In the model based on ECG features, the cross-validation AUC was 0.87 (95% CI 0.83-0.90), with sensitivity 76% and specificity 79%, and in the model based on ECG and clinical features, the cross-validation AUC was 0.89 (95% CI 0.86-0.91), with sensitivity 78% and specificity 81%.
Conclusion: Artificial intelligence using electrocardiographic data may assist in the early identification of childhood cancer survivors at high risk for cardiomyopathy.
ZOOM Virtual Room Connection:
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Meeting ID: 993 2268 0124
International numbers available: https://tennessee.zoom.us/u/abNgxz4Vyw
NOTE: The host will moderate questions. Questions can be typed in the chat box at any time during the seminar, and the host will ask them or invite seminar attenders to ask others at the end of the seminar.
We look forward to seeing you all among us.
Chi-Yang Chiu, Ph.D.
Assistant Professor of Biostatistics
Department of Preventive Medicine, UTHSC