Deep learning based Lung Nodule Detection and Cancer risk stratification through the effective Integration of Imaging and Electronic Medical Records

通过影像和电子病历的有效整合，基于深度学习的肺结节检测和癌症风险分层

• 批准号: 2276519
• 金额: --
• 依托单位: University of Leeds
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2276519
• 关键词: Deep; learning; based; Lung; Nodule

Lung cancer is the leading cause of cancer related deaths in the UK with very low five- and ten-year survival rates. This is attributed to the fact that the cancer is typically diagnosed at an advanced stage as early detection is particularly challenging. Consequently, there is a clear need for automated and robust systems that facilitate its early detection, diagnosis and treatment. The goal of this project is to develop a system to improve patient management in the context of both lung cancer screening and incidental nodule discovery. The project objectives are defined to this end: Develop a fully supervised lung nodule detection framework for automatic assessment of low dose chest CTs. Develop a cancer risk stratification system based on the official clinical guidelines. Investigate the use of NLP tools for extracting useful information from electronic medical records (EMRs), for subsequent integration with the image analytics. Develop a weakly supervised nodule detection algorithm for the effective integration of NLP-derived data from radiology reports with imaging data (low-dose chest CTs).

肺癌是英国癌症相关死亡的主要原因，五年和十年生存率非常低。这是因为癌症通常在晚期被诊断出来，因为早期检测特别具有挑战性。因此，显然需要自动化和强大的系统，以促进其早期检测，诊断和治疗。该项目的目标是开发一个系统，以改善肺癌筛查和偶然发现结节的背景下的患者管理。该项目的目标是：开发一个完全监督的肺结节检测框架，用于自动评估低剂量胸部CT。 根据官方临床指南制定癌症风险分层系统。 研究使用NLP工具从电子病历（EMR）中提取有用信息，以便随后与图像分析集成。开发一种弱监督结节检测算法，用于有效整合来自放射学报告的NLP衍生数据与成像数据（低剂量胸部CT）。