A Novel Deep Raman Spectroscopy Platform for Non-Invasive In-Vivo Diagnosis of Breast Cancer
A Novel Deep Raman Spectroscopy Platform for Non-Invasive In-Vivo Diagnosis of Breast Cancer
批准号:
EP/P012442/1
负责人:
Nicholas Stone
金额:
$152.78万
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 --
中文摘要
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英文摘要
Recently, we have pioneered a portfolio of revolutionary optical technologies in the area of laser spectroscopy, namely deep Raman spectroscopy, for non-invasive molecular probing of biological tissue. The developments have the potential of making a step-change in many fields of medicine including cancer diagnosis. The techniques comprise spatially offset Raman spectroscopy (SORS) and Transmission Raman (both patented by the applicants). The methods are described in detail in a tutorial review: http://pubs.rsc.org/en/content/articlelanding/2016/cs/c5cs00466g. There is an urgent clinical need for early objective diagnosis and prediction of likely treatment outcomes for many types of subsurface cancers. This is not addressed by existing technologies. There are numerous steps along the cancer clinical pathway where real-time, in vivo, molecular specific disease analysis would have a major impact. This would significantly reduce needle biopsy, in around 80% of those recalled following mammographic screening this step is unnecessarily - ie leading to the diagnosis of benign lesions. Our novel approach would allow for more accurate and immediate diagnosis in conjunction with mammography at first presentation by improving screening or surveillance techniques, leading to earlier diagnosis and better treatment outcomes. Secondly it would allow surgical margin assessment and treatment monitoring in real-time and thirdly identification of metastatic invasion in the lymphatic system during routine surgery. There are numerous other areas where a rapid molecular analysis of a tissue sample in the clinic or theatre environment would allow improved clinical decision-making, for example when pre- operatively staging the disease and particularly when non-invasively monitoring tumour response during chemo/radiotherapy. Clearly these approaches would be beneficial to the patient by reducing cancer recurrence rates; but also by minimising the numbers of invasive procedures required, thus reducing costs and patient anxiety.Raman spectroscopy is a highly molecular-specific method, which itself has proven to be a useful tool in early epithelial cancer diagnostics, although in its conventional form it has been restricted to sampling the tissue surface of much less than 1 mm deep. The new technology unlocks unique access to tissue abnormalities of up to several cm's deep, i.e. at depths one to two orders of magnitude higher than those previously possible with Raman.Following on from our previous project, where we were able to demonstrate conceptually a ~100x improvement in signal recovery compared to our early feasibility work, we are now able to rapidly develop a platform for real-clinical tools using this approach. We propose to make major breakthroughs in this area and advance diagnostics particularly focussed on breast cancer and lymph node metastasis initially as focused case studies and then potentially applied to prostate cancers (outside the scope of this proposal). This will be explored as a joint cross-disciplinary research venture between Profs Stone and Matousek, the two key researchers in this area. We now seek funding to progress this work in a timely manner by developing a novel medical diagnostic platform of major societal impact. We propose to bring together key players from multidisciplinary areas covering physical sciences, spectroscopy, radiology, cancer diagnostic and therapeutic surgery, and histopathology to exploit all of the relevant skills and develop a critical mass of expertise to tackle these challenging issues.
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DOI:
10.1021/acs.analchem.7b01469
发表时间:
2017-09-19
期刊:
ANALYTICAL CHEMISTRY
影响因子:
7.4
作者:
[Gardner, Benjamin, Stone, Nicholas, Matousek, Pavel]
通讯作者:
Matousek, Pavel
Guided principal component analysis (GPCA): a simple method for improving detection of a known analyte
引导主成分分析 (GPCA):一种改进已知分析物检测的简单方法
DOI:
10.1039/d3an00820g
发表时间:
2023
期刊:
The Analyst
影响因子:
--
作者:
[Gardner B]
通讯作者:
Gardner B
DOI:
10.1038/s41598-018-25465-x
发表时间:
2018-05-30
期刊:
Scientific reports
影响因子:
4.6
作者:
[Ghita A, Matousek P, Stone N]
通讯作者:
Stone N
DOI:
10.1002/jrs.5875
发表时间:
2020-03-20
期刊:
JOURNAL OF RAMAN SPECTROSCOPY
影响因子:
2.5
作者:
[Gardner, Benjamin, Stone, Nicholas, Matousek, Pavel]
通讯作者:
Matousek, Pavel
Raman Nanotheranostics - RaNT - developing the targeted diagnostics and therapeutics of the future by combining light and functionalised nanoparticles
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批准号:EP/R020965/1
-
项目类别:Research Grant
-
资助金额:$733.0万
-
财政年份:2018
-
负责人:Nicholas Stone
-
依托单位:
A novel Deep Raman spectroscopy platform for non-invasive in situ molecular analysis of disease specific tissue compositional changes.
-
批准号:EP/K020374/1
-
项目类别:Research Grant
-
资助金额:$92.46万
-
财政年份:2013
-
负责人:Nicholas Stone
-
依托单位:
国内基金
海外基金
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Deep Seek引导下预防肝硬化腹水患者发生腹腔感染的约翰霍普金斯循证实践模型下中医护理策略的构建研究
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批准号:2026JJ81909
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项目类别:省市级项目
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资助金额:--
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批准年份:2026
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负责人:胡曦
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依托单位:
基于Deep Unrolling的高分辨近红外二区荧光分子断层成像方法研究
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批准号:12271434
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项目类别:面上项目
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资助金额:46万元
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批准年份:2022
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负责人:贺小伟
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依托单位:
基于深度森林(Deep Forest)模型的表面增强拉曼光谱分析方法研究
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批准号:2020A151501709
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项目类别:省市级项目
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资助金额:10.0万元
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批准年份:2020
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负责人:谢怡
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依托单位:
面向Deep Web的数据整合关键技术研究
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批准号:61872168
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项目类别:面上项目
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资助金额:62.0万元
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批准年份:2018
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负责人:董永权
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依托单位:
基于Deep-learning的三江源区冰川监测动态识别技术研究
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批准号:51769027
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项目类别:地区科学基金项目
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资助金额:38.0万元
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批准年份:2017
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负责人:张大奇
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依托单位:
具有时序处理能力的Spiking-Deep Learning(脉冲深度学习)方法研究
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批准号:61573081
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项目类别:面上项目
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资助金额:64.0万元
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批准年份:2015
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负责人:屈鸿
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依托单位:
基于语义计算的海量Deep Web知识探索机制研究
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批准号:61272411
-
项目类别:面上项目
-
资助金额:80.0万元
-
批准年份:2012
-
负责人:赵峰
-
依托单位:
Deep Web数据集成查询结果抽取与整合关键技术研究
-
批准号:61100167
-
项目类别:青年科学基金项目
-
资助金额:20.0万元
-
批准年份:2011
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负责人:董永权
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依托单位:
面向Deep Web的大规模知识库自动构建方法研究
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批准号:61170020
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项目类别:面上项目
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资助金额:57.0万元
-
批准年份:2011
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负责人:崔志明
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依托单位:
Deep Web敏感聚合信息保护方法研究
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批准号:61003054
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项目类别:青年科学基金项目
-
资助金额:20.0万元
-
批准年份:2010
-
负责人:赵朋朋
-
依托单位: