Develop a framework to integrate lung CT scan with metabolomics data for patients with lung cancer and identify potential biomarkers

我的项目的主题是基于新的定量/计算人工智能技术在肺部疾病CT成像领域的应用。随着生物医学研究变得更加依赖数学和计算，新出现的放射组学领域正在迅速发展，成为真正的游戏规则改变者，使医学图像分析更快、更高效。这一量化领域仍处于初级阶段，缺乏可广泛应用于临床实践以改善患者结果的强大的自动化框架。具体地说，在我们的项目中，我们打算分析一组肺癌患者的成像数据。由于CT扫描是目前肺癌分析中应用最广泛的成像手段，我们的研究将集中在非结构化CT信息的定量建模和处理上，以创建具有临床意义的肺部成像特征。希望它们将使我们能够获得有关疾病早期发现和分期的潜在重要知识。此外，我们将寻求了解患者既往肺部疾病是否会增加肺癌发生的几率。这个问题将通过在分析中引入先前的肺部疾病信息来进行测试。结果将与不包含患者既往肺部疾病信息的模型进行比较。最后，我们打算使用这种放射组学分析来进一步研究生物分子数据。将放射组学结果与其他组学数据(最有可能是代谢组学)相结合，有望使我们更接近发现新的非侵入性生物标志物，为肿瘤学家和放射科医生提供更多工具来改善患者的预后。数据的项目计算分析将涉及实施传统的统计和机器学习方法以及基于深度学习的方法，并将考虑使用现有的主要平台来执行肺分割功能。然而，特征提取，它们的进一步处理，以及可能与其他组学数据的合成，将不得不在我们的工作期间设计和自动化。

The theme of my project deals with the application of novel quantitative/computational AI-based technologies in the area of CT imaging for pulmonary disease. As biomedical research becomes more mathematical and computing-dependent, the newly emerged field of radiomics is rapidly growing as a real game-changer making the medical image analysis faster and more efficient. This quantitative field is still in its infancy and lacks powerful automated frameworks that could be widely deployed in clinical practice to improve patients outcome. Specifically, in our project, we intend to analyse imaging data from a cohort of lung cancer patients. As CT scans, currently are the most widely used imaging modality in lung cancer analysis, our research will be focusing on the quantitative modelling and processing of unstructured CT information to create clinically meaningful lung imaging features. Hopefully, they will allow us to obtain potentially important knowledge regarding the early detection and staging of the disease. Additionally, we would seek to understand whether patients prior pulmonary disorders increase the chances of lung cancer occurrence. This question will be tested by introducing prior lung disease information into the analysis. The results will be compared against the model that did not contain information on prior lung disease of the patient. Finally, we intend to use this radiomics analysis for further investigation with biomolecular data. Synthesizing the radiomics results with other omics data, most likely metabolomics, will hopefully lead us closer to the discovery of novel non-invasive biomarkers giving oncologists and radiologists more tools to improve patients outcomes.The project computational analysis of the data will involve implementing both traditional statistical and machine learning methods as well as deep learning-based approaches and will consider using major existing platforms to perform lung segmentation function. However, feature extraction, their further processing, and possible synthesis with other omics data will have to be designed during our work and automated.