Cancer Emulation Analysis with Deep Neural Network

使用深度神经网络进行癌症仿真分析

基本信息

  • 批准号:
    10725293
  • 负责人:
  • 金额:
    $ 16.75万
  • 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:
    2023-09-19 至 2025-08-31
  • 项目状态:
    未结题

项目摘要

Project Summary To objectively quantify the relative effectiveness of drugs, devices, and treatment procedures on cancer prognosis, rigorously designed and executed randomized clinical trials (RCTs) remain the gold standard. However, as exemplified in this application and many published studies, RCTs are not always feasible. Fortunately, the fast development of electronic medical records and insurance claims databases has made it possible to mine a large amount of observational data and efficiently complement RCTs. This strategy has been enthusiastically endorsed by multiple national organizations. Among the available observational data analysis techniques that aim to draw RCT-type conclusions, emulation has emerged as especially appealing, with its trial- like architecture, interpretability, and scalability. It has been applied to multiple cancers and other complex diseases and led to clinically significant findings. This study has two equally important aims. The first aim is to develop deep neural network (DNN)-based emulation analysis methods and software. Most of the existing emulation analyses are based on classic regression techniques. Compared to regression, DNN excels with superior model fitting and higher flexibility. Recently, our group was the first to develop a DNN-based emulation analysis approach and applied it to cardiovascular diseases. Advancing from this recent success, we will develop more interpretable and more stable DNNs tailored to RCT analysis. We will then further expand the analysis scope and conduct DNN-based analysis of a sequence of emulated trials. For both a single emulated trial and a sequence of trials, we will develop valid inference, which is essential for RCT analysis but has been neglected in most DNN studies. User- friendly software will be developed. This methodological development will substantially expand the scope of emulation analysis, deep learning, causal inference, observation data analysis, and medical record/insurance claims data analysis. The second aim is to develop and analyze two emulated trials. We will address the comparative effectiveness of (a) lobectomy and limited resection on lung cancer survival for the SEER-Medicare elderly population, and (b) radical prostatectomy and observation on localized prostate cancer survival for the VA population. The findings will be comprehensively and rigorously evaluated. To provide a more comprehensive picture, we will also analyze using multiple alternative methods and compare against existing RCTs and observational studies. With the significant methodological advancements and powerful data, our analysis will lead to more definitive findings, directly inform clinical practice, and serve as the prototype for future applications.
项目总结

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)

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Shuangge Ma其他文献

Shuangge Ma的其他文献

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{{ truncateString('Shuangge Ma', 18)}}的其他基金

Deep Learning-based Emulation Analysis: Methodological Developments and Case Studies
基于深度学习的仿真分析:方法发展和案例研究
  • 批准号:
    10515491
  • 财政年份:
    2022
  • 资助金额:
    $ 16.75万
  • 项目类别:
Deep Learning-based Emulation Analysis: Methodological Developments and Case Studies
基于深度学习的仿真分析:方法发展和案例研究
  • 批准号:
    10676303
  • 财政年份:
    2022
  • 资助金额:
    $ 16.75万
  • 项目类别:
Integrated Cancer Modeling: A New Dimension
综合癌症建模:新维度
  • 批准号:
    9812144
  • 财政年份:
    2019
  • 资助金额:
    $ 16.75万
  • 项目类别:
Assisted Network-based Analysis of Cancer Gene Expression Studies
癌症基因表达研究的辅助网络分析
  • 批准号:
    9306472
  • 财政年份:
    2017
  • 资助金额:
    $ 16.75万
  • 项目类别:
Novel Methods for Identifying Genetic Interactions for Cancer Prognosis
识别癌症预后基因相互作用的新方法
  • 批准号:
    10668282
  • 财政年份:
    2016
  • 资助金额:
    $ 16.75万
  • 项目类别:
Novel Methods for Identifying Genetic Interactions for Cancer Prognosis
识别癌症预后基因相互作用的新方法
  • 批准号:
    10311368
  • 财政年份:
    2016
  • 资助金额:
    $ 16.75万
  • 项目类别:
Novel methods for identifying genetic interactions in cancer prognosis
识别癌症预后中遗传相互作用的新方法
  • 批准号:
    9079917
  • 财政年份:
    2016
  • 资助金额:
    $ 16.75万
  • 项目类别:
Novel Methods for Identifying Genetic Interactions for Cancer Prognosis
识别癌症预后基因相互作用的新方法
  • 批准号:
    10451680
  • 财政年份:
    2016
  • 资助金额:
    $ 16.75万
  • 项目类别:
Core B: Biostatistics and Bioinformatics Core
核心 B:生物统计学和生物信息学核心
  • 批准号:
    10203852
  • 财政年份:
    2015
  • 资助金额:
    $ 16.75万
  • 项目类别:
Penalization methods for identifying gene envrionment interactions and applications to melanoma and other cancer types
识别基因环境相互作用的惩罚方法及其在黑色素瘤和其他癌症类型中的应用
  • 批准号:
    9238753
  • 财政年份:
    2014
  • 资助金额:
    $ 16.75万
  • 项目类别:

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