Deep learning in cervical cancer radiogenomics

宫颈癌放射基因组学中的深度学习

基本信息

  • 批准号:
    10424854
  • 负责人:
  • 金额:
    $ 22.09万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2022
  • 资助国家:
    美国
  • 起止时间:
    2022-06-13 至 2024-05-31
  • 项目状态:
    已结题

项目摘要

PROJECT SUMMARY/ABSTRACT The overall goal of this proposal is to optimize the use of radiomic and genomic data to develop biomarkers which make clinical predictions that change cancer patient management. While the need for such predictive biomarkers is evident across cancer types, we focus our proposal on the particularly prevalent and damaging condition of recurrent, locally-advanced cervical cancer (LACC). Cervical cancer remains the third most common cancer diagnosis of women, and treatment failure for locally-advanced disease is 30-50% following chemoradiation therapy. There is a pressing need to identify patients at risk for treatment failure to allow for personalized treatment including modified chemoradiation regimens, early escalation of therapy, and clinical trial enrollment. To develop radiogenomic biomarkers for LACC recurrence, this proposal addresses three outstanding methodological needs: limited availability of gene expression data for cancer subtypes, noisy and redundant imaging feature data, and lack of disease-informed, interpretable -omics integration, each addressed in its own specific aim. Aim 1 will use generative adversarial networks (GAN) to augment the small gene expression datasets for all high-risk HPV subtypes. Aim 2 will optimize imaging feature selection using a deep convolutional autoencoder (CAE). Aim 3 will integrate radiogenomic features through a structural equation modeling (SEM) approach incorporating HPV-specific oncogenic mechanisms as latent variables. Together, we expect fulfillment of these aims will create an optimized recurrence biomarker which will out- perform other prediction modalities as well as standard-of-care follow-up imaging. Beyond the specific application to HPV-driven malignancies, our proposal will generate novel tools and methods to integrate any high-dimensional radiogenomic data with hypothesis-driven research findings to improve cancer prediction.
项目总结/文摘

项目成果

期刊论文数量(0)
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会议论文数量(0)
专利数量(0)

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Jin Zhang其他文献

Jin Zhang的其他文献

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{{ truncateString('Jin Zhang', 18)}}的其他基金

Integrating multi-omics, imaging, and longitudinal data to predict radiation response in cervical cancer
整合多组学、成像和纵向数据来预测宫颈癌的放射反应
  • 批准号:
    10734702
  • 财政年份:
    2023
  • 资助金额:
    $ 22.09万
  • 项目类别:
HPV genomic structure in cervical cancer radiation response and recurrence detection
HPV基因组结构在宫颈癌放射反应和复发检测中的作用
  • 批准号:
    10634999
  • 财政年份:
    2023
  • 资助金额:
    $ 22.09万
  • 项目类别:
Deep learning in cervical cancer radiogenomics
宫颈癌放射基因组学中的深度学习
  • 批准号:
    10643978
  • 财政年份:
    2022
  • 资助金额:
    $ 22.09万
  • 项目类别:
HPV alternative splicing in cervical cancer radiation response
HPV选择性剪接在宫颈癌放射反应中的作用
  • 批准号:
    10308435
  • 财政年份:
    2020
  • 资助金额:
    $ 22.09万
  • 项目类别:
HPV alternative splicing in cervical cancer radiation response
HPV选择性剪接在宫颈癌放射反应中的作用
  • 批准号:
    9891761
  • 财政年份:
    2020
  • 资助金额:
    $ 22.09万
  • 项目类别:
HPV alternative splicing in cervical cancer radiation response
HPV选择性剪接在宫颈癌放射反应中的作用
  • 批准号:
    10523104
  • 财政年份:
    2020
  • 资助金额:
    $ 22.09万
  • 项目类别:
FASEB SRC on Protein Kinases and Protein Phosphorylation
FASEB SRC 关于蛋白激酶和蛋白磷酸化
  • 批准号:
    9754337
  • 财政年份:
    2019
  • 资助金额:
    $ 22.09万
  • 项目类别:
Live-cell Activity Architecture in Cancer
癌症中的活细胞活性结构
  • 批准号:
    10673027
  • 财政年份:
    2015
  • 资助金额:
    $ 22.09万
  • 项目类别:
Live-cell Activity Architecture in Cancer
癌症中的活细胞活性结构
  • 批准号:
    9319218
  • 财政年份:
    2015
  • 资助金额:
    $ 22.09万
  • 项目类别:
Signal Transduction by PI3K/Akt/mTOR Pathway
通过 PI3K/Akt/mTOR 途径进行信号转导
  • 批准号:
    9108384
  • 财政年份:
    2015
  • 资助金额:
    $ 22.09万
  • 项目类别:

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