Deep learning approaches to imaging genomics for precision medicine

精准医学成像基因组学的深度学习方法

• 批准号: 2898221
• 金额: --
• 依托单位: University of St Andrews
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2898221
• 关键词: Deep; learning; approaches; imaging; genomics

This project will investigate the use of deep learning applied to imaging genomics to support precision health. Precision medicine aims to tailor treatment to the individual, rather than assuming everyone will respond like the average patient. The biggest drivers in precision medicine have been developments in genomics. For example, knowing the genomic make-up of a tumour, e.g., lung cancer, allows clinicians to use highly effective targeted treatments against the tumour. However, a biopsy tissue sample is required to sequence the tumour genome, which is invasive and involves some risk. In addition, rapid mutation means tumours are often genetically heterogeneous. This heterogeneity is difficult to capture in a small biopsy sample, which can mislead and result in ineffective treatment.Imaging genomics (sometimes known as radiogenomics) uses features derived from non-invasive medical images to infer the spatial distribution of the tumour genotype(s). Traditional imaging features have included the shape, greylevel intensity statistics, and texture of the tumour.Deep learning is an artificial intelligence neural network technique based on multiple layers of neurons. It has had a huge impact on medical image analysis, setting the state-of-the-art performance in many benchmarks and applications, and outperforming human observers in some situations. This project will determine where deep learning can best be applied in the imaging genomic pipeline, to help ensure that every patient gets the right treatment at the treatment.

该项目将研究如何将深度学习应用于成像基因组学以支持精准健康。精准医学的目标是根据个人情况制定治疗方案，而不是假设每个人都会像普通患者一样做出反应。精准医学的最大推动力是基因组学的发展。例如，了解肿瘤（例如肺癌）的基因组构成使临床医生能够对肿瘤使用高效的靶向治疗。然而，需要活检组织样本来对肿瘤基因组进行测序，这是侵入性的并且存在一定的风险。此外，快速突变意味着肿瘤通常具有遗传异质性。这种异质性很难在小活检样本中捕获，这可能会误导并导致治疗无效。成像基因组学（有时称为放射基因组学）使用来自非侵入性医学图像的特征来推断肿瘤基因型的空间分布。传统的成像特征包括肿瘤的形状、灰度强度统计和纹理。深度学习是一种基于多层神经元的人工智能神经网络技术。它对医学图像分析产生了巨大影响，在许多基准和应用中设定了最先进的性能，并在某些情况下超越了人类观察者。该项目将确定深度学习最适合在成像基因组管道中应用的位置，以帮助确保每位患者在治疗时得到正确的治疗。