Novel Biomarkers for Liver Imaging in the Monitoring of Cancer Therapy

用于监测癌症治疗的肝脏成像的新型生物标志物

• 批准号: 1870061
• 金额: --
• 依托单位: King's College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-1870061
• 关键词: Novel; Biomarkers; Liver; Imaging; Monitoring

Hepatocellular carcinoma (HCC) causes approximately 1500 UK deaths per year and the incidence is rising, predominantly due to lifestyle factors. Recently developed non-invasive quantitative magnetic resonance imaging (MRI) techniques, such as corrected T1 mapping and MR elastography (commercialised as LiverMultiScan and Resoundant respectively) have shown promise in characterising liver fibrosis (Banerjee et al. J Hep (2014), Huwart et al. (2008)). However, their potential in characterising liver cirrhosis under surveillance, as a means to predict onset of HCC, has not yet been explored, and could complement and enhance routine imaging, such as ultrasound (US), single-phase contrast-enhanced computed tomography (CE-CT), diffusion MRI or multi-phasic CE-MRI commonly used for such patients.This studentship proposal aims at developing novel imaging biomarkers to characterise precancerous liver disease (cirrhosis) under surveillance, using a range of novel quantitative MRI techniques alongside routine imaging. Imaging will be conducted in the Department of Radiology, as part of their standard of care, with clinical support from the Gastroeneterology and Hepatology team. The project will require the development of dedicated computational tools that can compensate for tissue motion/deformation between different types of scans, and scans that are acquired longitudinally, by taking into account imaging-derived patient and disease-specific tissue or tissue perfusion parameters, in order to detect (and ultimately predict) changes in tissue characteristics from precancerous to cancerous states. This project contains a very strong translational component, by embedding the developed techniques into a software product for rapid deployment into clinical practice.PhD Roadmap (it is proposed to embed / strongly align this studentship with the EPSRC Centre for Doctoral Training (CDT) in Medical Imaging, which is run jointly between King's College London and Imperial College London): 1st year delivery: MRes in Medical Imaging Sciences, including course options on image acquisition & reconstruction, imaging chemistry & biology, and medical image computing & computational modelling; MRes project on "Tools for HCC detection in T1 mapping" at Perspectum Diagnostics.2nd year delivery: Development of deformable registration of the liver for routine imaging of patients with cirrhosis. Prototype for co-registration of routine imaging (US or CE-CT/MR) to T1 and MR elastography imaging. Conference/workshop publication on methodology.3rd year delivery: Collation of clinical data. Full comparison and performance evaluation of different T1 mapping techniques for patients with cirrhosis under surveillance, and correlation against MR elastography findings. Technical/clinical conference/journal publication(s).4th year delivery: Complete software integration, write up Thesis and additional publications with clinical evaluation.

每年约有1500例英国人死于肝细胞癌，而且发病率还在上升，主要是由于生活方式的原因。最近开发的非侵入性定量磁共振成像(MRI)技术，如校正的T1映射和MR弹性成像(分别商业化为liverMultiScan和Resoundant)在表征肝纤维化方面显示出希望(Banerjee等人)。J Hep(2014)，Huwart et al.(2008))。然而，作为一种预测肝细胞癌发病的手段，它们在描述监测中的肝硬变方面的潜力还没有被探索，并且可以补充和增强常规成像，如超声(US)、单相对比增强计算机断层扫描(CE-CT)、弥散成像或多相CE-MRI，通常用于此类患者。这项学生计划旨在开发新的成像生物标记物来表征在监测中的癌前肝病(肝硬变)，使用一系列新的定量MRI技术和常规成像。成像将在放射科进行，作为他们标准护理的一部分，由胃遗传学和肝病团队提供临床支持。该项目将需要开发专用的计算工具，通过考虑来自成像的患者和特定疾病的组织或组织的灌注参数，能够补偿不同类型的扫描和纵向获得的扫描之间的组织运动/变形，以便检测(并最终预测)从癌前状态到癌症状态的组织特征的变化。该项目包含一个非常强大的翻译成分，通过将开发的技术嵌入到软件产品中，以便快速部署到临床实践中。博士路线图(建议将该学员资格嵌入/强烈调整为EPSRC医学成像博士培训中心(CDT，该中心由伦敦国王学院和帝国学院联合运营)：第一年交付：医学成像科学MRE，包括图像采集和重建、成像化学和生物学以及医学图像计算和计算建模的课程选项；在透视诊断中的MREs项目“用于肝细胞癌T1定位的检测工具”。第二年交付：开发用于肝硬变患者常规成像的可变形登记的肝脏。常规成像(US或CE-CT/MR)与T1和MR弹性成像联合配准的原型。会议/研讨会关于方法学的出版物。第三年交付：临床数据的整理。对接受监测的肝硬变患者的不同T1标测技术进行充分的比较和性能评估，并与磁共振弹性成像结果进行相关性。技术/临床会议/期刊发表(S)。4年交付：完成软件集成，撰写论文和补充出版物并进行临床评估。

项目成果

Functional Parameters Derived from Magnetic Resonance Imaging Reflect Vascular Morphology in Preclinical Tumors and in Human Liver Metastases.

• DOI: 10.1158/1078-0432.ccr-18-0033
• 发表时间: 2018-10-01
• 期刊: Clinical cancer research : an official journal of the American Association for Cancer Research
• 作者: Kannan P;Kretzschmar WW;Winter H;Warren D;Bates R;Allen PD;Syed N;Irving B;Papiez BW;Kaeppler J;Markelc B;Kinchesh P;Gilchrist S;Smart S;Schnabel JA;Maughan T;Harris AL;Muschel RJ;Partridge M;Sharma RA;Kersemans V
• 通讯作者: Kersemans V

Comparison of linear and nonlinear implementation of the compartmental tissue uptake model for dynamic contrast-enhanced MRI.

• DOI: 10.1002/mrm.26324
• 发表时间: 2017-06
• 期刊: Magnetic resonance in medicine
• 影响因子: 3.3
• 作者: Kallehauge JF;Sourbron S;Irving B;Tanderup K;Schnabel JA;Chappell MA
• 通讯作者: Chappell MA

Comparison of Classical Machine Learning & Deep Learning to Characterise Fibrosis & Inflammation Using Quantitative MRI

• DOI: 10.1109/isbi48211.2021.9433962
• 发表时间: 2021-04
• 期刊: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)
• 作者: E. Chan;Matt Kelly;J. Schnabel

GIFTed Demons: deformable image registration with local structure-preserving regularization using supervoxels for liver applications.

• DOI: 10.1117/1.jmi.5.2.024001
• 发表时间: 2018-04
• 期刊: Journal of medical imaging (Bellingham, Wash.)
• 作者: Papież BW;Franklin JM;Heinrich MP;Gleeson FV;Brady M;Schnabel JA
• 通讯作者: Schnabel JA

Model-Based and Data-Driven Strategies in Medical Image Computing

• DOI: 10.1109/jproc.2019.2943836
• 发表时间: 2020-01-01
• 期刊: PROCEEDINGS OF THE IEEE
• 影响因子: 20.6
• 作者: Rueckert, Daniel;Schnabel, Julia A.
• 通讯作者: Schnabel, Julia A.