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，包括对来自伦敦大学圣乔治分校的广泛可用数据集的放射学分析，并与林肯大学视觉工程实验室合作。本研究旨在提供进一步个性化医疗的方法，使用深度学习架构以数学方式描述脑肿瘤的潜在组织病理学，以开发用于新的和未见过的MRI数据的人工智能分类系统。脑肿瘤本质上具有令人难以置信的异质性，患者之间在形状、对比和位置上存在很大差异。低级别胶质瘤需要监测，以防转化为高级别脑肿瘤，这需要立即给予治疗。观察和等待方法用于低级别胶质瘤，任何即将发生变化的指标都可能证明对预测患者病情恶化至关重要。MRI扫描是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，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，并且通过在广泛的图像类型上训练的深度学习架构的发展，MRI图像的组合可以导致更全面的脑癌诊断。MRI可以使用深度学习技术进行计算分析，通过该技术，使用图像过滤操作处理肿瘤区域。该过程采用MRI图像区域，并利用过滤来识别图像纹理和空间结构，从而产生对组织亚类（例如正常脑组织、坏死组织、水肿组织）的理解。这种人工智能学习过程需要一个包含脑癌的逻辑标记图像，并形成对构成相关癌症组织的纹理和结构成分的理解。然后将这些信息应用于未经分析的新图像，并且没有标记以分类包括健康和癌组织的子区域。也可以从训练好的神经网络中提取特征以存储在深度学习架构外部。这些大型生物标志物数据集可以进一步分析，以便更深入地了解通过外部机器学习调查从人工智能架构中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、遗传变异和患者预后相关的进一步研究机会。本研究旨在建立在现有工作的基础上，并在适用的情况下，为肿瘤分割提供更好的解决方案：在新的和未知的患者MRI中识别整个肿瘤区域。监测：在有纵向数据的情况下，检查患者病史的变化，并确定由于给予治疗而产生的潜在变化。提高效率：支持放射科医生识别包含潜在癌症组织分级的区域，以及该分级的可能性。特征融合：从不同MRI图像的组合中创建新的特征。标准化：试图找到在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：-数据分析与信息学-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