Spatio-temporal Generative Manifolds for Prediction of Immunotherapy Response

用于预测免疫治疗反应的时空生成流形

基本信息

  • 批准号:
    RGPIN-2019-05402
  • 负责人:
  • 金额:
    $ 2.04万
  • 依托单位:
  • 依托单位国家:
    加拿大
  • 项目类别:
    Discovery Grants Program - Individual
  • 财政年份:
    2019
  • 资助国家:
    加拿大
  • 起止时间:
    2019-01-01 至 2020-12-31
  • 项目状态:
    已结题

项目摘要

Immunotherapy has been shown to provide long-term and curative benefits to cancer patients who have limited treatment options, with the potential to revolutionize cancer therapy and become an important part of comprehensive treatment options for curing many diseases. But while these advances are encouraging, there is still no reliable imaging tumor biomarker that can accurately identify patients responding to immunotherapy effects or monitor their response to adjust the therapeutic strategy, with highest reported detection rates of 71%. Towards this end, radiomics are quantitative non-invasive alternatives to biopsies generating image-driven biomarkers characterizing tumor spatial heterogeneity and better identify immunotherapy response, but lack the statistical framework to predict therapy outcomes.******The global objective of this research program is to develop a prediction platform for immunotherapy response from discriminant manifold embeddings, reducing complex and multi-modal medical imaging data in a temporal domain using locality preservation criterions. The spatial-temporal manifold learning framework will be able to recover radiomic signatures which are specific to immune phenotypes. The research program will incorporate highly innovative theoretical developments and applied projects with the following objectives: (1) Develop a manifold embedding technique mapping medical images of responsive and non-responsive patients to immunotherapy using probabilistic deep neural networks; (2) Parameterize manifolds to quantify therapeutic effects on outcomes, handling challenges in gradient-based optimization; (3) Create a treatment response prediction framework based on spatial-temporal manifolds anchored on Riemannian geometry; (4) Apply and validate the proposed methods on cancer patient biobanks to compare predicted results with actual outcomes.***The long-term goal is to offer the necessary tools for generating representative knowledge from a given cohort of immunotherapy cancer patients and discovering predictive biomarkers of immune system response and tumor evolution from medical imaging.******This research program will propose novel outcome prediction tools using recent advances in manifold and deep learning. While previous predictive immunotherapy response methods were based on linear regression using hand-crafted features and lacked interpretable representations of underlying features capturing immunologic effects, we will develop a framework that will learn spatial and temporal properties of underlying manifolds. Compared to traditional machine learning techniques sensitive to the intrinsic topology of high-dimensional data, we will exploit novel concepts in joint embeddings, adding flexibility and versatility capabilities to a wide range of imaging and clinical data. From a medical outlook, this research has the potential to contribute new insights and indications for possible early detection of cancer and predictors of therapeutic outcomes.
免疫治疗已被证明为治疗选择有限的癌症患者提供了长期和根治的好处,有可能使癌症治疗发生革命性变化,成为治愈许多疾病的综合治疗选择的重要组成部分。但是,尽管这些进展令人鼓舞,但仍然没有可靠的成像肿瘤生物标志物可以准确地识别患者对免疫治疗效果的反应或监测他们的反应以调整治疗策略,报告的最高发现率为71%。为此,放射组学是定量的非侵入性替代活检,产生图像驱动的生物标记物来表征肿瘤的空间异质性并更好地识别免疫治疗反应,但缺乏预测治疗结果的统计框架。*本研究计划的全球目标是开发一个基于鉴别多重嵌入的免疫治疗反应预测平台,使用局部性保存标准在时间域减少复杂和多模式的医学成像数据。时空流形学习框架将能够恢复特定于免疫表型的放射特征。该研究计划将结合高度创新的理论发展和应用项目,目标如下:(1)开发多种嵌入技术,利用概率深度神经网络将有反应和无反应的患者的医学图像映射到免疫治疗;(2)对流形进行参数化,以量化治疗效果对结果的影响,处理基于梯度优化的挑战;(3)基于基于黎曼几何的时空流形创建治疗反应预测框架;(4)在癌症患者生物库上应用并验证所提出的方法,以将预测结果与实际结果进行比较。*长期目标是提供必要的工具,以便从给定的免疫治疗癌症患者队列中生成具有代表性的知识,并从医学成像中发现免疫系统反应和肿瘤演变的预测生物标记物。*本研究计划将利用多种学习和深度学习的最新进展,提出新的结果预测工具。虽然以前的预测免疫治疗反应方法是基于使用手工制作的特征的线性回归,并且缺乏捕捉免疫效应的潜在特征的可解释表示,但我们将开发一个框架,该框架将学习潜在流形的空间和时间属性。与对高维数据的内在拓扑敏感的传统机器学习技术相比,我们将在联合嵌入方面采用新的概念,为广泛的成像和临床数据增加灵活性和通用性。从医学的角度来看,这项研究有可能为癌症的早期发现和治疗结果的预测提供新的见解和指征。

项目成果

期刊论文数量(0)
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Kadoury, Samuel其他文献

Biomechanically driven intraoperative spine registration during navigated anterior vertebral body tethering
  • DOI:
    10.1088/1361-6560/ab1bfa
  • 发表时间:
    2019-06-01
  • 期刊:
  • 影响因子:
    3.5
  • 作者:
    Jobidon-Lavergne, Hugo;Kadoury, Samuel;Aubin, Carl-Eric
  • 通讯作者:
    Aubin, Carl-Eric
Probabilistic 4D predictive model from in-room surrogates using conditional generative networks for image-guided radiotherapy
  • DOI:
    10.1016/j.media.2021.102250
  • 发表时间:
    2021-10-01
  • 期刊:
  • 影响因子:
    10.9
  • 作者:
    Romaguera, Liset Vazquez;Mezheritsky, Tal;Kadoury, Samuel
  • 通讯作者:
    Kadoury, Samuel
The Liver Tumor Segmentation Benchmark (LiTS).
  • DOI:
    10.1016/j.media.2022.102680
  • 发表时间:
    2023-02
  • 期刊:
  • 影响因子:
    10.9
  • 作者:
    Bilic, Patrick;Christ, Patrick;Li, Hongwei Bran;Vorontsov, Eugene;Ben-Cohen, Avi;Kaissis, Georgios;Szeskin, Adi;Jacobs, Colin;Mamani, Gabriel Efrain Humpire;Chartrand, Gabriel;Lohoefer, Fabian;Holch, Julian Walter;Sommer, Wieland;Hofmann, Felix;Hostettler, Alexandre;Lev-Cohain, Naama;Drozdzal, Michal;Amitai, Michal Marianne;Vivanti, Refael;Sosna, Jacob;Ezhov, Ivan;Sekuboyina, Anjany;Navarro, Fernando;Kofler, Florian;Paetzold, Johannes C.;Shit, Suprosanna;Hu, Xiaobin;Lipkova, Jana;Rempfler, Markus;Piraud, Marie;Kirschke, Jan;Wiestler, Benedikt;Zhang, Zhiheng;Huelsemeyer, Christian;Beetz, Marcel;Ettlinger, Florian;Antonelli, Michela;Bae, Woong;Bellver, Miriam;Bi, Lei;Chen, Hao;Chlebus, Grzegorz;Dam, Erik B.;Dou, Qi;Fu, Chi-Wing;Georgescu, Bogdan;Giro-I-Nieto, Xavier;Gruen, Felix;Han, Xu;Heng, Pheng-Ann;Hesser, Jurgen;Moltz, Jan Hendrik;Igel, Christian;Isensee, Fabian;Jaeger, Paul;Jia, Fucang;Kaluva, Krishna Chaitanya;Khened, Mahendra;Kim, Ildoo;Kim, Jae-Hun;Kim, Sungwoong;Kohl, Simon;Konopczynski, Tomasz;Kori, Avinash;Krishnamurthi, Ganapathy;Li, Fan;Li, Hongchao;Li, Junbo;Li, Xiaomeng;Lowengrub, John;Ma, Jun;Maier-Hein, Klaus;Maninis, Kevis-Kokitsi;Meine, Hans;Merhof, Dorit;Pai, Akshay;Perslev, Mathias;Petersen, Jens;Pont-Tuset, Jordi;Qi, Jin;Qi, Xiaojuan;Rippel, Oliver;Roth, Karsten;Sarasua, Ignacio;Schenk, Andrea;Shen, Zengming;Torres, Jordi;Wachinger, Christian;Wang, Chunliang;Weninger, Leon;Wu, Jianrong;Xu, Daguang;Yang, Xiaoping;Yu, Simon Chun-Ho;Yuan, Yading;Yue, Miao;Zhang, Liping;Cardoso, Jorge;Bakas, Spyridon;Braren, Rickmer;Heinemann, Volker;Pal, Christopher;Tang, An;Kadoury, Samuel;Soler, Luc;van Ginneken, Bram;Greenspan, Hayit;Joskowicz, Leo;Menze, Bjoern
  • 通讯作者:
    Menze, Bjoern
Global geometric torsion estimation in adolescent idiopathic scoliosis
Automatic self-gated 4D-MRI construction from free-breathing 2D acquisitions applied on liver images

Kadoury, Samuel的其他文献

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{{ truncateString('Kadoury, Samuel', 18)}}的其他基金

Intelligent Image Guided Interventions
智能图像引导干预
  • 批准号:
    CRC-2017-00281
  • 财政年份:
    2022
  • 资助金额:
    $ 2.04万
  • 项目类别:
    Canada Research Chairs
Prediction of Immunotherapy Response with Geometric Deep Learning in Medical Imaging
利用医学影像中的几何深度学习预测免疫治疗反应
  • 批准号:
    RGPIN-2020-06558
  • 财政年份:
    2022
  • 资助金额:
    $ 2.04万
  • 项目类别:
    Discovery Grants Program - Individual
Prediction of Immunotherapy Response with Geometric Deep Learning in Medical Imaging
利用医学影像中的几何深度学习预测免疫治疗反应
  • 批准号:
    RGPIN-2020-06558
  • 财政年份:
    2021
  • 资助金额:
    $ 2.04万
  • 项目类别:
    Discovery Grants Program - Individual
Intelligent Image Guided Interventions
智能图像引导干预
  • 批准号:
    CRC-2017-00281
  • 财政年份:
    2021
  • 资助金额:
    $ 2.04万
  • 项目类别:
    Canada Research Chairs
Intelligent Image Guided Interventions
智能图像引导干预
  • 批准号:
    CRC-2017-00281
  • 财政年份:
    2020
  • 资助金额:
    $ 2.04万
  • 项目类别:
    Canada Research Chairs
Spatio-temporal motion prediction model for liver cancer radiotherapy
肝癌放疗时空运动预测模型
  • 批准号:
    517413-2017
  • 财政年份:
    2020
  • 资助金额:
    $ 2.04万
  • 项目类别:
    Collaborative Research and Development Grants
Prediction of Immunotherapy Response with Geometric Deep Learning in Medical Imaging
利用医学影像中的几何深度学习预测免疫治疗反应
  • 批准号:
    RGPIN-2020-06558
  • 财政年份:
    2020
  • 资助金额:
    $ 2.04万
  • 项目类别:
    Discovery Grants Program - Individual
Intelligent Image Guided Interventions
智能图像引导干预
  • 批准号:
    CRC-2017-00281
  • 财政年份:
    2019
  • 资助金额:
    $ 2.04万
  • 项目类别:
    Canada Research Chairs
Spatio-temporal motion prediction model for liver cancer radiotherapy
肝癌放疗时空运动预测模型
  • 批准号:
    517413-2017
  • 财政年份:
    2019
  • 资助金额:
    $ 2.04万
  • 项目类别:
    Collaborative Research and Development Grants
Image-Guided Molecular Optical Spectroscopy for Tumor-Targeted Prostate Cancer Interventions
用于肿瘤靶向前列腺癌干预的图像引导分子光谱
  • 批准号:
    523532-2018
  • 财政年份:
    2019
  • 资助金额:
    $ 2.04万
  • 项目类别:
    Collaborative Health Research Projects

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