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 与治疗相关的心肌病/心力衰竭（CHF）是童年过早发病的主要原因 癌症存活。鉴于邻苯二酚和相关心脏毒性化学治疗剂的宽度使用 结合放射疗法暴露于胸部，超过一半的儿童癌症生存是 与年龄匹配的普通人群相比，早期CHF的风险显着增加。目前，国家 国际共识指南建议常规使用二维（2D）超声心动图 筛选此高风险人群的早期征兆，尤其是左心室（LV）收缩功能障碍和 LV几何形状的变化。目前，2d超声心动图代表了我们整个美国的护理标准 它的宽度可用性，相对较低的成本以及避免电离辐射或镇静。尽管如此， 2D超声心动图的局限性包括更大的患者内和观察者之间的变异性。结果，当前 基于超声心动图的监视继续具有有限的灵敏度，并且经常需要串行研究 在将患者确定为潜在异常之前。尽管没有足够的证据指导 CHF管理特定于儿科癌症生存，这是与非癌症相关心肌病的证据 儿童和成人都表明，早期的干预措施可以减轻或延迟CHF的进展。所以， 改善儿童癌症生存中早期CHF检测的方法可能具有重要的临床 含义。深度学习（DL）是机器学习的子场，可以自动从大型中提取图案 非结构化数据集，例如医学图像，并且越来越多地用于医学中的疾病 诊断以及疾病的发作和结果预测。我们建议利用独特的成像数据集 我们是从儿童肿瘤学小组（COG）组装的，这是NCI赞助的国家临床的一部分 试验网络和社区肿瘤研究计划，探索DL的潜力 检测CHF。我们有关于100多个儿童癌症生存的纵向超声心动图数据 开发了CHF和350多人没有使用标准化标准定义的，代表成像 > 3000个单独的超声心动图（以及增长）的存储库。使用此额外和临床注释的数据集， 我们建议：1）使用深度卷积神经网络（DCNN），确定DL-的最佳过程 基于小儿癌症存活中CHF的基于CHF的评估； 2）评估可行性和初步效率 CHF诊断前超声心动图的基于DCNN的心肌病发作的预测。预期结果 包括开发DCNN，该DCNN将区分异常和正常的超声心动图与 分别有和没有CHF的儿科癌症生存。优化后，我们将进行初步 有效性分析以确定可以使用冲浪者过渡到CHF的多年 优化的DCNN。