Deep Learning for Large-Scale Analysis of Tumour-Infiltrating Lymphocytes on Whole-Slide Images.

用于大规模分析全玻片图像上肿瘤浸润淋巴细胞的深度学习。

• 批准号: 2882929
• 金额: --
• 依托单位: University of Warwick
• 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-2882929
• 关键词: Deep; Learning; Large; Scale; Analysis

Background: Cancer is a significant contributor to global morality, with an increasing number of cancer patients each year, our healthcare systems have been under huge pressure. There is an urgent need to improve the accuracy and the efficiency of cancer diagnosis, prognosis and the quality of treatment plans. In recent years, the study of tumour microenvironment (TME) has received lots of attention. One type of cells in the TME are Tumour-Infiltrating Lymphocytes (TILs), which are a type of immune cells that can play a part in killing tumour cells, measuring TILs can help better target treatments, particularly immunotherapy. Aims and Objectives: This project aims to develop fully automated deep learning models that can detect TILs on whole-slide images of varies cancer types and measure the extent of TILs infiltrate. The project also aims to predict treatment outcomes based on TIL scores.Novelty of research methodology: We will build our deep learning models on top of existing state-of-the-art models. We will also provide accessible user interface which will enable the models to be integrated seamlessly into pathology workflow.Research impact: Using AI to detect and measure TILs on a large scale will massively speed up pathology workflow, allowing diagnosis and prognosis to be produced faster. Predicting treatment outcomes based on TILs score will allow the optimal treatment to be selected for each patient, therefore maximising survival rate.Alignment to research council strategies and research areas: This project falls into the EPSRC's research area of "AI for healthcare", it supports the strategy "Securing better health, ageing and wellbeing".

背景：癌症是全球道德的重要贡献者，随着癌症患者的数量每年都在增加，我们的医疗系统一直承受着巨大的压力。迫切需要提高癌症诊断、预后和治疗计划的准确性和效率。近年来，肿瘤微环境(TME)的研究受到了广泛关注。TME中的一种细胞是肿瘤浸润性淋巴细胞(TILs)，这是一种可以在杀死肿瘤细胞方面发挥作用的免疫细胞，测量TILs可以帮助更好地进行靶向治疗，特别是免疫治疗。目的和目标：本项目旨在开发全自动深度学习模型，该模型可以检测各种癌症类型的全幻灯片图像上的TIL，并测量TIL的渗透程度。该项目还旨在根据TIL评分预测治疗结果。研究方法创新：我们将在现有最先进的模型基础上建立我们的深度学习模型。我们还将提供可访问的用户界面，使模型能够无缝地集成到病理工作流中。研究影响：使用人工智能大规模检测和测量TIL将极大地加快病理工作流程，使诊断和预后能够更快地产生。根据TILS评分预测治疗结果将允许为每个患者选择最佳治疗，从而最大化存活率。调整研究委员会的战略和研究领域：该项目属于EPSRC的“医疗保健人工智能”研究领域，它支持“确保更好的健康、老龄化和福祉”战略。