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

基于机器学习的儿童癌症幸存者心肌病风险识别

• 批准号: 10730177
• 负责人: Patrick M Boyle
• 金额: $ 22.75万
• 依托单位: FRED HUTCHINSON CANCER CENTER
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10730177
• 关键词: 3-Dimensional; Address; Adult; Age; Anthracycline; Architecture; Artificial Intelligence; Biomedical Engineering; Calibration; Cancer Patient; Cancer Survivor; Cardiac; Cardiology; Cardiomyopathies; Cardiotoxicity; Chemotherapy and/or radiation; Chest; Child; Clinical; Community Clinical Oncology Program; Computers; Consensus; Data; Data Set; Dependence; Detection; Development; Diagnosis; Disease Outcome; Early Diagnosis; Early Intervention; Echocardiography; Equilibrium; Exposure to; Functional disorder; General Population; Geometry; Guidelines; Heart; Heart Diseases; Heart failure; Image; Individual; Interdisciplinary Study; International; Interobserver Variability; Ionizing radiation; Left; Left Ventricular Dysfunction; Left Ventricular Ejection Fraction; Long-Term Survivors; Machine Learning; Malignant Childhood Neoplasm; Measurement; Measures; Medical Imaging; Medicine; Methods; Monitor; Morbidity - disease rate; National Clinical Trials Network; Network-based; Oncology; Onset of illness; Patients; Pattern; Pediatric Oncology Group; Process; Radiation therapy; Recommendation; Research; Risk; Sedation procedure; Shortening Fraction; Standardization; Survivors; Testing; Therapeutic; Time; Training; Treatment-Related Cancer; Ventricular; cancer imaging; cardiac magnetic resonance imaging; childhood cancer survivor; cohort; convolutional neural network; cost; deep learning; disease diagnosis; efficacy evaluation; heart function; high risk; high risk population; image archival system; imaging biomarker; imaging modality; improved; improved outcome; insight; interest; outcome prediction; premature; programs; screening; standard of care; two-dimensional; ultrasound; unstructured data

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.

项目摘要/摘要