Medical imaging markers of cancer initiation, progression and therapeutic response in the breast based on tissue microstructure

基于组织微观结构的乳腺癌症发生、进展和治疗反应的医学成像标记

• 批准号: EP/K020439/1
• 负责人: David Hawkes
• 金额: $ 103.51万
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2013
• 资助国家: 英国
• 起止时间: 2013 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FK020439%2F1
• 关键词: Medical; imaging; markers; cancer; initiation

Studies of cancer DNA show that breast cancer comprises many different diseases. Some spread quickly and are very dangerous, while others are slow growing and are not life-threatening. Some respond well to chemotherapy or radiotherapy, while others are resistant to treatment. It is important to be able to select the best treatment for a particular patient's cancer, so-called "patient stratification". In this way appropriate treatment can be given whilst potential side-effects from an ineffective treatment are avoided.Recent discoveries have implicated the stroma, the connective tissue of the breast, in both the aggressiveness of breast cancer and its response to therapy. When small amounts of tissue are removed from a potential breast cancer by needle biopsy, or when a breast cancer is removed by surgery, subtle differences are seen in microscopic imaging (histology) of the stromal tissue around the tumour. These occur at the scale of a few microns and are far below the spatial resolution of X-ray mammography, magnetic resonance imaging (MRI) and ultrasound that radiologists routinely use to detect and assess breast cancer. A recent innovation, ultrasound shear wave elastography (USSWE), has enabled radiologists in Dundee to show differences in stromal tissue of patients with invasive cancer. USSWE measures the very small displacements of tissue that occur during conventional ultrasound examinations. The MR imaging is specifically designed to (a) assess the microscopic blood supply of the breast tissue, and any cancer present, using a method called dynamic contrast enhancement, DCE-MRI, and (b) assess the size and shape of microstructure by measuring the diffusion of protons, DWI-MRI. Accurate comparison of these with histology of the removed tissue enables the computer to learn the differences in the images between normal tissue and cancer and between normal stroma and stroma associated with cancer. In a further step scientists at the UCL Centre for Medical Image Computing have invented new methods to measure some of these microscopic changes. They generate computer models of these structures and use the subtle changes predicted by these models to adjust the imaging techniques to maximise the differences between normal tissue and the different types of cancer and stromal tissue changes that we see using histology. We propose a project in which we will develop ways to combine X-ray DBT, DCE-MRI, DWI-MRI and USSWE to best discriminate between normal stroma and stroma near to cancers, and between the different types of cancer: high risk, low risk, and those that do and do not respond to therapy. We will first ask 3 women who have recently been diagnosed with breast cancer to spend about an hour in a MR scanner to allow us to collect a wide range of DW-MRI images so we can optimise MRI acquisition. We will then ask a further 30 women recently diagnosed with cancer for permission to use their radiological images and the histology images of their tissue samples from biopsy and surgery. We will use these images to train our computer methods to best discriminate between the different types of stroma and cancer, and between the different components of the cancer itself. Ten of the patients selected will be undergoing a course of neo-adjuvant therapy (a type of chemotherapy that is given prior to surgery to shrink the tumour).If successful this project will discover new ways to identify those patients who will respond best to a particular treatment, those patients who do not have life threatening disease and so do not need aggressive therapy, and those patients with types of cancer that require more aggressive treatment. At the end of this project we will translate these findings to a clinical trial.

对癌症DNA的研究表明，乳腺癌包括许多不同的疾病。有些传播迅速，非常危险，而另一些则生长缓慢，不会危及生命。有些人对化疗或放疗反应良好，而另一些人则对治疗有抗药性。重要的是能够为特定患者的癌症选择最佳治疗方法，即所谓的“患者分层”。通过这种方式，可以给予适当的治疗，同时避免无效治疗的潜在副作用。最近的研究发现，乳腺间质（乳腺结缔组织）与乳腺癌的侵袭性及其对治疗的反应有关。当通过针吸活检从潜在的乳腺癌中取出少量组织时，或者当通过手术切除乳腺癌时，在肿瘤周围基质组织的显微镜成像（组织学）中可以看到细微的差异。这些发生在几微米的尺度上，远远低于X射线乳房X线摄影，磁共振成像（MRI）和超声的空间分辨率，放射科医生通常使用这些技术来检测和评估乳腺癌。最近的一项创新，超声剪切波弹性成像（USSWE），使邓迪的放射科医生能够显示浸润性癌症患者间质组织的差异。USSWE测量在常规超声检查期间发生的非常小的组织位移。MR成像专门设计用于（a）使用称为动态对比增强（DCE-MRI）的方法评估乳腺组织的微观血液供应和存在的任何癌症，以及（B）通过测量质子扩散（DWI-MRI）评估微观结构的大小和形状。这些与切除组织的组织学的准确比较使计算机能够学习正常组织和癌症之间以及正常基质和与癌症相关的基质之间的图像差异。在进一步的步骤中，UCL医学图像计算中心的科学家发明了新方法来测量这些微观变化。他们生成这些结构的计算机模型，并使用这些模型预测的细微变化来调整成像技术，以最大限度地提高正常组织与我们使用组织学看到的不同类型癌症和基质组织变化之间的差异。我们提出了一个项目，在该项目中，我们将开发结合联合收割机X射线DBT，DCE-MRI，DWI-MRI和USSWE的方法，以最好地区分正常间质和癌旁间质，以及不同类型的癌症：高风险，低风险，以及对治疗有反应和无反应的癌症。我们将首先要求3名最近被诊断患有乳腺癌的女性在MR扫描仪中花费大约一个小时，以便我们收集广泛的DW-MRI图像，以便我们可以优化MRI采集。然后，我们将要求另外30名最近被诊断患有癌症的妇女允许使用她们的放射学图像和来自活检和手术的组织样本的组织学图像。我们将使用这些图像来训练我们的计算机方法，以最好地区分不同类型的基质和癌症，以及癌症本身的不同成分。10名入选患者将接受新辅助治疗（一种在手术前进行的化疗，以缩小肿瘤）。如果成功的话，这个项目将发现新的方法来确定那些对特定治疗反应最好的患者，那些没有危及生命的疾病，因此不需要积极治疗的患者，以及那些需要更积极治疗的癌症患者。在本项目结束时，我们将把这些发现转化为临床试验。

项目成果

Modelling vasculature and cellular restriction in breast tumours using diffusion MRI

使用扩散 MRI 模拟乳腺肿瘤的脉管系统和细胞限制

• 发表时间: 2015
• 作者: Bailey C

Modeling vascularity in breast cancer and surrounding stroma using diffusion MRI and intravoxel incoherent motion

使用扩散 MRI 和体素内不相干运动对乳腺癌和周围基质的血管分布进行建模

• 发表时间: 2014
• 作者: Bailey C

Biomechanically guided prone-to-supine image registration of breast MRI using an estimated reference state

使用估计参考状态进行生物力学引导的乳腺 MRI 俯卧位图像配准

• DOI: 10.1109/isbi.2013.6556450
• 发表时间: 2013
• 作者: Eiben B

Breast MRI segmentation for density estimation: Do different methods give the same results and how much do differences matter?

• DOI: 10.1002/mp.12320
• 发表时间: 2017-09
• 期刊: Medical physics
• 影响因子: 3.8
• 作者: Doran SJ;Hipwell JH;Denholm R;Eiben B;Busana M;Hawkes DJ;Leach MO;Silva IDS
• 通讯作者: Silva IDS

Microstructure Characterization of Bone Metastases from Prostate Cancer with Diffusion MRI: Preliminary Findings.

• DOI: 10.3389/fonc.2018.00026
• 发表时间: 2018
• 期刊: Frontiers in oncology
• 影响因子: 4.7
• 作者: Bailey C;Collins DJ;Tunariu N;Orton MR;Morgan VA;Feiweier T;Hawkes DJ;Leach MO;Alexander DC;Panagiotaki E
• 通讯作者: Panagiotaki E