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
- 来源:
- 关键词:AddressArchitectureCancer PrognosisCardiovascular DiseasesCase StudyClinicalColonoscopyColorectal CancerComplementComplexComputer softwareComputerized Medical RecordDataData AnalysesDatabasesDevelopmentDevicesDiseaseElderlyEnsureErlotinibExcisionFluorouracilFutureInfrastructureLiteratureLobectomyMalignant NeoplasmsMalignant neoplasm of lungMalignant neoplasm of prostateMedical RecordsMedicareMethodologyMethodsModelingNetwork-basedNon-Small-Cell Lung CarcinomaObservational StudyOperative Surgical ProceduresOutcomePaclitaxelPerformancePharmaceutical PreparationsPolicy ResearchPopulationProceduresPublic PolicyPublishingPythonsRadical ProstatectomyReproducibilityResearch DesignResourcesSurvival AnalysisTechniquesTestingUnited States Department of Veterans Affairsadvanced pancreatic cancercancer diagnosiscancer survivalclinical practiceclinical trial analysisclinically significantcomparative effectivenesscooperative studycost effectivedata accessdata warehousedeep learningdeep neural networkdesigneffectiveness researchexperienceflexibilitygemcitabineinsurance claimsneglectprogramsprototyperandomized, clinical trialsrelative effectivenessscreeningsimulationsoftware developmentsuccesstreatment effectuser friendly software
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:
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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