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.
项目摘要
客观量化药物、器械和治疗程序对癌症的相对有效性
预后,严格设计和执行的随机临床试验(RCT)仍然是金标准。
然而,正如本申请和许多已发表的研究所示,RCT并不总是可行的。
幸运的是,电子病历和保险索赔数据库的快速发展,
可以挖掘大量的观察性数据,并有效地补充RCT。这项战略
得到了多个国家组织的热烈支持。在现有的观测数据分析中,
技术,旨在得出RCT类型的结论,仿真已经成为特别有吸引力的,其试验-
比如架构、可解释性和可扩展性。它已被应用于多种癌症和其他复杂的癌症。
疾病,并导致临床上有意义的发现。
这项研究有两个同样重要的目的。第一个目标是开发基于深度神经网络(DNN)的
仿真分析方法和软件。现有的仿真分析大多是基于经典的
回归技术与回归相比,DNN具有上级模型拟合和更高的灵活性。
最近,我们的团队率先开发了基于DNN的仿真分析方法,并将其应用于
心血管疾病在最近的成功基础上,我们将开发更多可解释的,
为RCT分析定制的稳定DNN。然后,我们将进一步扩大分析范围,并进行基于DNN的
一系列模拟试验的分析。对于单个模拟试验和一系列试验,我们将
开发有效的推理,这对于RCT分析至关重要,但在大多数DNN研究中被忽视。User-
将开发友好的软件。这一方法的发展将大大扩大
仿真分析、深度学习、因果推理、观察数据分析和医疗记录/保险
索赔数据分析。第二个目标是开发和分析两个仿真试验。我们将解决
SEER-Medicare中肺叶切除术和有限切除术对肺癌生存率的比较有效性
老年人群,和(B)根治性前列腺切除术和观察局限性前列腺癌的生存
VA人群。调查结果将得到全面和严格的评价。提供比较全面的
图片,我们还将使用多种替代方法进行分析,并与现有的RCT进行比较,
观察性研究随着方法的重大进步和强大的数据,我们的分析将
导致更明确的发现,直接通知临床实践,并作为未来应用的原型。
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
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10203852 - 财政年份:2015
- 资助金额:
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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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