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

我的项目的主题是新型定量/计算AI技术在肺部疾病CT成像领域的应用。随着生物医学研究越来越依赖数学和计算，放射组学的新兴领域正在迅速发展，成为一个真实的游戏规则改变者，使医学图像分析更快，更有效。这个定量领域仍处于起步阶段，缺乏强大的自动化框架，可以广泛部署在临床实践中，以改善患者的结果。具体来说，在我们的项目中，我们打算分析来自肺癌患者队列的成像数据。由于CT扫描是目前肺癌分析中使用最广泛的成像方式，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。希望它们能让我们获得有关疾病早期检测和分期的潜在重要知识。此外，我们将试图了解患者既往肺部疾病是否会增加肺癌发生的机会。将通过在分析中引入既往肺部疾病信息来检验该问题。将结果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与生物分子数据的进一步调查。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，将有望带领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。该项目的数据计算分析将涉及实施传统的统计和机器学习方法以及深度学习-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、进一步处理以及与其他组学数据的可能合成必须在我们的工作中设计并自动化。

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.