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

基本信息

  • 批准号:
    10730177
  • 负责人:
  • 金额:
    $ 22.75万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 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

项目摘要

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

项目成果

期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ monograph.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ sciAawards.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ conferencePapers.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ patent.updateTime }}

Patrick M Boyle其他文献

Natural strategies for the spatial optimization of metabolism in synthetic biology
合成生物学中代谢空间优化的自然策略
  • DOI:
    10.1038/nchembio.975
  • 发表时间:
    2012-05-17
  • 期刊:
  • 影响因子:
    13.700
  • 作者:
    Christina M Agapakis;Patrick M Boyle;Pamela A Silver
  • 通讯作者:
    Pamela A Silver

Patrick M Boyle的其他文献

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

{{ truncateString('Patrick M Boyle', 18)}}的其他基金

Mechanistic Relationships Between Fibrosis, Fibrillation, and Stroke: Multi-Scale, Multi-Physics Simulations
纤维化、颤动和中风之间的机制关系:多尺度、多物理场模拟
  • 批准号:
    10441932
  • 财政年份:
    2022
  • 资助金额:
    $ 22.75万
  • 项目类别:
Mechanistic Relationships Between Fibrosis, Fibrillation, and Stroke: Multi-Scale, Multi-Physics Simulations
纤维化、颤动和中风之间的机制关系:多尺度、多物理场模拟
  • 批准号:
    10617841
  • 财政年份:
    2022
  • 资助金额:
    $ 22.75万
  • 项目类别:

相似海外基金

Rational design of rapidly translatable, highly antigenic and novel recombinant immunogens to address deficiencies of current snakebite treatments
合理设计可快速翻译、高抗原性和新型重组免疫原,以解决当前蛇咬伤治疗的缺陷
  • 批准号:
    MR/S03398X/2
  • 财政年份:
    2024
  • 资助金额:
    $ 22.75万
  • 项目类别:
    Fellowship
CAREER: FEAST (Food Ecosystems And circularity for Sustainable Transformation) framework to address Hidden Hunger
职业:FEAST(食品生态系统和可持续转型循环)框架解决隐性饥饿
  • 批准号:
    2338423
  • 财政年份:
    2024
  • 资助金额:
    $ 22.75万
  • 项目类别:
    Continuing Grant
Re-thinking drug nanocrystals as highly loaded vectors to address key unmet therapeutic challenges
重新思考药物纳米晶体作为高负载载体以解决关键的未满足的治疗挑战
  • 批准号:
    EP/Y001486/1
  • 财政年份:
    2024
  • 资助金额:
    $ 22.75万
  • 项目类别:
    Research Grant
Metrology to address ion suppression in multimodal mass spectrometry imaging with application in oncology
计量学解决多模态质谱成像中的离子抑制问题及其在肿瘤学中的应用
  • 批准号:
    MR/X03657X/1
  • 财政年份:
    2024
  • 资助金额:
    $ 22.75万
  • 项目类别:
    Fellowship
CRII: SHF: A Novel Address Translation Architecture for Virtualized Clouds
CRII:SHF:一种用于虚拟化云的新型地址转换架构
  • 批准号:
    2348066
  • 财政年份:
    2024
  • 资助金额:
    $ 22.75万
  • 项目类别:
    Standard Grant
The Abundance Project: Enhancing Cultural & Green Inclusion in Social Prescribing in Southwest London to Address Ethnic Inequalities in Mental Health
丰富项目:增强文化
  • 批准号:
    AH/Z505481/1
  • 财政年份:
    2024
  • 资助金额:
    $ 22.75万
  • 项目类别:
    Research Grant
ERAMET - Ecosystem for rapid adoption of modelling and simulation METhods to address regulatory needs in the development of orphan and paediatric medicines
ERAMET - 快速采用建模和模拟方法的生态系统,以满足孤儿药和儿科药物开发中的监管需求
  • 批准号:
    10107647
  • 财政年份:
    2024
  • 资助金额:
    $ 22.75万
  • 项目类别:
    EU-Funded
BIORETS: Convergence Research Experiences for Teachers in Synthetic and Systems Biology to Address Challenges in Food, Health, Energy, and Environment
BIORETS:合成和系统生物学教师的融合研究经验,以应对食品、健康、能源和环境方面的挑战
  • 批准号:
    2341402
  • 财政年份:
    2024
  • 资助金额:
    $ 22.75万
  • 项目类别:
    Standard Grant
Ecosystem for rapid adoption of modelling and simulation METhods to address regulatory needs in the development of orphan and paediatric medicines
快速采用建模和模拟方法的生态系统,以满足孤儿药和儿科药物开发中的监管需求
  • 批准号:
    10106221
  • 财政年份:
    2024
  • 资助金额:
    $ 22.75万
  • 项目类别:
    EU-Funded
Recite: Building Research by Communities to Address Inequities through Expression
背诵:社区开展研究,通过表达解决不平等问题
  • 批准号:
    AH/Z505341/1
  • 财政年份:
    2024
  • 资助金额:
    $ 22.75万
  • 项目类别:
    Research Grant
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了