A Novel Contour-based Machine Learning Tool for Reliable Brain Tumour Resection (ContourBrain)

一种基于轮廓的新型机器学习工具，用于可靠的脑肿瘤切除（ContourBrain）

• 批准号: EP/Y021614/1
• 负责人: Xi Chen
• 金额: $ 38.17万
• 依托单位: University of Bath
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FY021614%2F1
• 关键词: Novel; Contour; based; Machine; Learning

Glioma is a type of aggressive brain tumor that has varying survival rates. In surgical operations, it can be difficult to strike a balance between reducing the risk of recurrence and preserving brain function. This is mainly due to the fact that manual tumor delineation is subjective, labor-intensive, and varies among practitioners, leading to unreliable segmentation and high recurrence rates. Therefore, there is an urgent need for reliable and automated tumor segmentation tools to assist surgeons in achieving an optimal balance between cancer control and functional preservation, reducing the time and resources spent by doctors, and providing quantitative data for future analysis. However, current automated segmentation approaches, including deep learning techniques, can be limited by the use of a deterministic boundary to delineate the tumor-infiltrating area, which can be problematic in cases with high uncertainty.Therefore, the aim of this project is to develop a novel statistical machine learning approach that utilises partially labelled clinical information for more informative and accountable pre-surgery decision making in brain tumour resection. This new method is expected to provide more informative and nuanced guidance to surgeons, enhancing their ability to plan the surgery accurately, reducing the risk of tumour recurrence while preserving function and reliability. The new approach is expected to be generalised to other types of MRI-based cancer diagnostics and have the potential to significantly advance AI powered tumour resection and improve patient outcomes.The research has two streams of beneficiaries: (i) A large community of UK and international clinical surgeons that conduct brain tumor resection in traditional ways. The outcomes of this project would assist the pre-operative decision making for tumor resection, and substantially improve thousands of patients' quality of life after surgery, therefore achieve significant socioeconomic impact. (ii) A large community of UK and international clinical academics/professionals who work on MRI-based tumor research. The novel statistical machine learning tool and idea generated by this project will be more widely applicable to other types of MRI-based cancer diagnostics and delineations. This will assist further investigation of accountable AI techniques for image-based tumor surgery. A number of activities have been carefully designed to effectively engage with beneficiaries of this research. These activities include co-production and validation of knowledge with clinical academics, publishing of the results in leading academic journals/conferences, publicize up-to-date project advances and share open-source software on GitHub, and a workshop with field specialists and national academic and non-academic stakeholders in MRI-based tumor surgery.

神经胶质瘤是一种侵袭性脑肿瘤，其存活率各不相同。在外科手术中，很难在降低复发风险和保留脑功能之间取得平衡。这主要是由于手工肿瘤勾画具有主观性、劳动强度大，且不同医师之间存在差异，导致分割不可靠、复发率高。因此，迫切需要可靠、自动化的肿瘤分割工具来协助外科医生在癌症控制和功能保留之间实现最佳平衡，减少医生花费的时间和资源，并为未来分析提供定量数据。然而，当前的自动分割方法，包括深度学习技术，可能会受到使用确定性边界来描绘肿瘤浸润区域的限制，这在高度不确定性的情况下可能会出现问题。因此，该项目的目的是开发一种新颖的统计机器学习方法，利用部分标记的临床信息在脑肿瘤切除术中做出更信息丰富、更负责任的术前决策。这种新方法有望为外科医生提供更多信息和细致入微的指导，增强他们准确计划手术的能力，降低肿瘤复发的风险，同时保留功能和可靠性。新方法预计将推广到其他类型的基于 MRI 的癌症诊断，并有可能显着推进人工智能驱动的肿瘤切除并改善患者的治疗效果。该研究有两类受益者：(i) 英国和英国的一个大社区以传统方式进行脑肿瘤切除术的国际临床外科医生。该项目的成果将有助于肿瘤切除术的术前决策，并大幅改善数以千计患者的术后生活质量，从而产生重大的社会经济影响。 (ii) 由从事基于 MRI 的肿瘤研究的英国和国际临床学者/专业人士组成的大型社区。该项目产生的新型统计机器学习工具和想法将更广泛地适用于其他类型的基于 MRI 的癌症诊断和描绘。这将有助于进一步研究用于基于图像的肿瘤手术的可靠人工智能技术。我们精心设计了许多活动，以便有效地与本研究的受益者互动。这些活动包括与临床学者共同制作和验证知识、在领先的学术期刊/会议上发布结果、宣传最新的项目进展并在 GitHub 上共享开源软件，以及与领域专家和国家级专家共同举办研讨会。基于 MRI 的肿瘤手术的学术和非学术利益相关者。