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，其存活率不同。在外科手术中，很难在降低复发风险和保留大脑功能之间取得平衡。这主要是由于手动肿瘤描述是主观的，劳动密集型的，并且在从业者中有所不同，导致不可靠的分割和高复发率。因此，迫切需要可靠且自动化的肿瘤分割工具，以帮助外科医生在癌症控制和功能保护之间取得最佳平衡，减少医生所花费的时间和资源，并提供定量数据以供未来分析。但是，当前的自动分割方法（包括深度学习技术）可以受到使用确定性边界来描述肿瘤内部浮雕领域的限制，在高不确定性的情况下，这可能是有问题的，因此，该项目的目的是开发出一种新型的统计机器学习方法，从而在临床上进行了部分标记的临床信息，以便在较大的临床上进行预测，以进行更详细的范围，以进行更详细的范围，以进行更详细的叙事。预计这种新方法将为外科医生提供更多信息和细微的指导，增强其准确计划手术的能力，从而降低肿瘤复发的风险，同时保留功能和可靠性。预计这种新方法将被推广到其他类型的基于MRI的癌症诊断，并有可能显着提高AI驱动的肿瘤切除并改善患者的结果。这项研究具有两种受益人：（i）英国和国际临床医生的大型社区，以传统方式进行脑肿瘤。该项目的结果将有助于肿瘤切除的术前决策，并大大改善了数千名患者手术后的生活质量，因此产生了重大的社会经济影响。 （ii）一个大型英国和国际临床学者/专业人士，他们从事基于MRI的肿瘤研究。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的癌症诊断和描述。这将有助于进一步研究用于基于图像的肿瘤手术的责任AI技术。已经精心设计了许多活动，以有效地与这项研究的受益者互动。这些活动包括与临床学者的共同制作和知识验证，在领先的学术期刊/会议中发布结果，宣传最新项目的进步并在Github上共享开源软件，以及与现场专家以及基于MRI的基于MRI肿瘤手术的现场专家以及国家学术和非学术利益相关者的讲习班。