MRI Radiomics of adult brain tumours

拟议的研究位于医学成像，神经科学和计算机科学之间的跨学科点，包括对伦敦大学圣乔治的各种可用数据集的放射组学分析，与林肯大学的视觉工程实验室合作。这项研究旨在提供进一步个性化医疗的方法，使用深度学习架构来数学描述脑肿瘤的潜在组织病理学，以开发用于新的和不可见的MRI数据的人工智能分类系统。脑肿瘤在本质上是非常异质的，患者之间的形状，对比度和位置存在很大差异。较低级别的神经胶质瘤需要监测，以防转化为需要立即给予治疗的较高级别脑肿瘤。对于低级别胶质瘤，我们采用观察和等待的方法，任何即将发生变化的指标都可以证明对预测患者病情恶化至关重要。MRI扫描是用于识别脑肿瘤的常用技术，可以通过捕获这些受损区域的纹理和形状外观来描述癌组织的不同区域，例如水肿，坏死和进行性肿瘤。不同的MRI图像有可能以不同的方式可视化这些区域中的每一个，并且MRI图像的组合可以通过开发在广泛的图像类型上训练的深度学习架构来实现对脑癌的更全面的诊断。MRI可以使用深度学习技术进行计算分析，通过该技术使用图像滤波器操作处理肿瘤区域。该过程采用MRI图像区域，并利用滤波来识别图像纹理和空间结构，从而生成对组织子类（例如，正常脑组织、坏死、水肿）的理解。这种人工智能学习过程采用包含脑癌的逻辑标记图像，并制定对构成相关癌症组织的纹理和结构成分的理解。然后，这些信息将被应用于未经分析的新图像，这些新图像没有被标记为健康和癌症组织的子区域。还可以从训练的神经网络中提取特征，以存储在深度学习架构外部。可以进一步分析这些大型生物标志物数据集，以更深入地了解通过外部机器学习调查从人工智能架构获得的信息的统计意义。额外的数据分析允许系统设计的模块化，以探索与肿瘤组织病理学、遗传变异和患者结局相关的进一步研究机会。这项研究旨在建立在现有工作的基础上，并在适用的情况下提供更好的解决方案：肿瘤分割：在新的和未知的患者MRI中识别整个肿瘤区域。监测：检查纵向数据可用的患者病史的变化，以及确定由于给予治疗而导致的潜在变化。提高效率：支持放射科医生识别包含潜在癌症组织等级的区域，以及该等级的概率。特征融合：从不同MRI图像的组合中创建新特征。标准化：试图找到不同位置和不同扫描系统生成的MRI之间的共同特征。MRC技能优先级：-数据分析和信息学-机器学习-人工智能-统计学-跨学科

The proposed research sits at an interdisciplinary point between Medical Imaging, Neuroscience and Computer Science and comprises radiomic analysis on a wide range of available datasets from St George's, University of London, in collaboration with the Laboratory of Vision Engineering at Lincoln University. This research aims to provide approaches to further personalised medicine using deep learning architectures to mathematically describe the underlying histopathology of Brain Tumours for the development of artificially intelligent classification systems for use on new and unseen MRI data.Brain Tumours are incredibly heterogenous in nature, with wide differences in shape, contrast and location between patients. Lower grade gliomas require monitoring in case of transformation into higher grade brain tumours which require immediate administration of treatment. A watch and wait approach is used for low grade glioma, and any indicator of imminent change could prove vital in predicting a deterioration in patient condition.MRI scanning is a common technique used in the identification of Brain Tumours and can describe varying regions of cancerous tissue, such as oedema, necrosis, and progressive tumour, through the capturing of the textural and shape appearance of these damaged regions. Different MRI images have the potential to visualise each of these areas differently, and a combination of MRI images can lead to a more rounded diagnosis of brain cancer through the development of deep learning architectures trained on a wide range of image types.MRI can be computationally analysed using deep learning techniques through which tumour regions are processed using image filter operations. This process takes MRI image regions and generates an understanding of tissue subclasses (e.g. normal brain tissue, necrosis, oedema) utilising filtering for the identification of image texture and spatial constructs. This artificially intelligent learning process takes a logically labelled image containing brain cancer and formulates an understanding of the textural and structural components which comprise the associated cancer tissue. This information is then applied to new images which have not been analysed, and are not labelled for classification into comprising sub-regions of healthy and cancerous tissue.It is also possible to extract features from trained neural networks for storage external to the deep learning architecture. These large biomarker datasets can be further analysed for a deeper understanding of the statistical significance of information obtained from artificially intelligent architectures through external machine learning investigations. Additional data analysis allows for modularity in system design to explore further research opportunities in relation to tumour histopathology, genetic variants and patient outcome.This research seeks to build upon existing work in, and where applicable, provide better solutions to: Tumour segmentation:The identification of entire tumour regions in new and unknown patient MRI. Monitoring:Examine changes in a patient's history where longitudinal data is available, as well as identifying potential changes as a result of administered treatment. Improved efficiency:Support radiologists in the identification of regions containing potential grades of cancer tissue, as well as the probability of that grading. Feature fusion:Create new features from the combination of different MRI images. Standardisation:Attempting to find shared characteristics between MRI generated in different locations and from different scanning systems. MRC Skill Priority:-Data analytics and informatics-Machine learning-Artificial Intelligence-Statistics-Interdisciplinary