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

神经胶质瘤是一种侵袭性脑肿瘤，具有不同的存活率。在外科手术中，很难在降低复发风险和保护脑功能之间取得平衡。这主要是由于手动肿瘤勾画是主观的、劳动密集型的，并且在从业者之间变化，导致不可靠的分割和高复发率。因此，迫切需要可靠和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实现最佳平衡，减少医生花费的时间和资源，并为未来的分析提供定量数据。然而，当前的自动分割方法（包括深度学习技术）可能受到使用确定性边界来描绘肿瘤浸润区域的限制，这在具有高不确定性的情况下可能是有问题的。该项目的目的是开发一种新的统计机器学习方法，该方法利用部分标记的临床信息，以提供更多信息和更负责任的预处理。脑肿瘤切除术中的手术决策。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，提高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。这项新方法有望推广到其他类型的基于MRI的癌症诊断，并有可能显著推进人工智能驱动的肿瘤切除术，改善患者预后。该研究有两个受益者：（i）英国和国际临床外科医生的大型社区，他们以传统方式进行脑肿瘤切除术。该项目的结果将有助于术前做出肿瘤切除的决策，并大大改善数千名患者术后的生活质量，从而产生重大的社会经济影响。(ii)一个由英国和国际临床学者/专业人士组成的大型社区，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应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基于图像的肿瘤手术的负责任AI技术。为了有效地与这项研究的受益者接触，精心设计了一些活动。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/会议上发表结果，在GitHub上宣传最新的项目进展并分享开源软件，以及与领域专家和国家学术和非学术利益相关者在基于MRI的肿瘤手术中举办研讨会。

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.