课题基金 / 基金详情

Intelligent and Personalised Risk Stratification and Early Diagnosis of Lung Cancer

Intelligent and Personalised Risk Stratification and Early Diagnosis of Lung Cancer
肺癌智能个性化风险分层及早期诊断
批准号:
EP/P023509/1
负责人:
Julia Schnabel
金额:
$120.7万
依托单位:
依托单位国家:
英国
项目类别:
Research Grant
财政年份:
2017
资助国家:
英国
项目状态:
已结题
起止时间:
2017 至 --

项目摘要

项目成果

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中文摘要
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英文摘要
Lung cancer is the second most common cancer in both males and females, and has a very poor prognosis, causing >35,000 of cancer-related deaths each year (nearly 100 every day). This is due to the mostly very late-stage diagnosis of cancer: nearly 50% of all lung cancer cases are only diagnosed at very late Stage IV where no curative treatment exists. The annual cost of lung cancer to the UK economy is estimated to be around £2.4 billion, taking into account the cost of treatment and premature death, the cost to business of sick leave and of unpaid care by friends and family. It eclipses the cost of any other cancer, and continues to present a significant economic and healthcare burden. There is currently no national lung cancer screening programme in the UK, as current tests are deemed to be inadequate, and not outweighing risks associated with screening that involves radiation exposure.The vision for our Healthcare Impact Partnership is to pave the way for a fundamentally different approach in which lung cancer can be detected at an early stage in patients identified as being at high risk of developing lung cancer: In a close three-way partnership between clinicians specialising in lung disease, healthcare industry, and computational imaging/machine learning researchers, we will jointly develop, test and clinically evaluate new computational methods for detecting, classifying and monitoring lung nodules in low-dose longitudinal CT of patients identified of being at risk of developing lung cancer. Specifically, we will embed powerful machine learning methods within a lung registration framework that has been previous developed using EPSRC funding. Through the use of deep feature learning techniques as well as through learning complex respiratory motion patterns, we will explore transfer learning from large, annotated lung CT image databases to a high-risk patient cohort receiving low-dose CT imaging. Through this partnership, which brings together expertise in computational imaging and deep learning, expert clinicians and a leading CT manufacturer, we aim to achieve both computational innovations in personalised diagnostics, as well as a strong translational focus for improved patient management, effectively leading the way in providing advanced healthcare technologies for a future UK lung cancer screening programme.
期刊论文(10)
专著(0)
科研奖励(0)
会议论文
A variational Bayesian method for similarity learning in non-rigid image registration
非刚性图像配准中相似性学习的变分贝叶斯方法
DOI: 10.1109/cvpr52688.2022.00022
发表时间: 2022
期刊:
影响因子: --
作者: [Grzech D]
通讯作者: Grzech D
FastReg: Fast Non-Rigid Registration via Accelerated Optimisation on the Manifold of Diffeomorphisms
FastReg:通过微分流形加速优化实现快速非刚性配准
DOI: 10.48550/arxiv.1903.01905
发表时间: 2019
期刊:
影响因子: --
作者: [Grzech D]
通讯作者: Grzech D
DOI: --
发表时间: 2018-06
期刊: ArXiv
影响因子: --
作者: [K. Kamnitsas;Daniel Coelho de Castro;L. L. Folgoc-L.;Ian Walker;Ryutaro Tanno;D. Rueckert;Ben Glocker;A. Criminisi;A. Nori]
通讯作者: K. Kamnitsas;Daniel Coelho de Castro;L. L. Folgoc-L.;Ian Walker;Ryutaro Tanno;D. Rueckert;Ben Glocker;A. Criminisi;A. Nori
Information Processing in Medical Imaging - 26th International Conference, IPMI 2019, Hong Kong, China, June 2-7, 2019, Proceedings
医学影像信息处理 - 第 26 届国际会议,IPMI 2019,中国香港,2019 年 6 月 2-7 日,会议记录
DOI: 10.1007/978-3-030-20351-1_17
发表时间: 2019
期刊:
影响因子: --
作者: [Le Folgoc L]
通讯作者: Le Folgoc L
8
    INSPIRE: Integration of Non-linear Sliding Processes into Image REgistration
    • 批准号:
      EP/H050892/1
    • 项目类别:
      Research Grant
    • 资助金额:
      $12.76万
    • 财政年份:
      2011
    • 负责人:
      Julia Schnabel
    • 依托单位:
    海外基金