Developing Novel Technology for Data Driven Clinical Decision Making

胶质母细胞瘤（GBM）是一种致命且无法治愈的脑肿瘤。迫切需要有效的GBM治疗方案。手术切除GBM肿瘤的程度对患者的生存有显著的积极影响，但对正常脑组织的损伤会严重影响生活质量，因此必须尽量减少。因此,当务之急是我们为手术切除可开发工具:A)精确消除癌细胞,减少热影响正常脑细胞B)提供反馈时切除外科医生,以便他们知道哪些地区的脑组织恶性和需要删除和C)给医生的迹象癌症的本质,这样可以使用最有效的治疗手术后癌细胞留下。这项技术的发展将对外科医生治疗癌症的方式产生革命性的影响。为了做到这一点，我们将开发高精度激光器，并使用高清摄像机了解它们对组织的影响，然后我们将收集这些激光器在切割时产生的“烟雾”，并将其输入质谱仪，该质谱仪能够测量来自组织的所有个体代谢物化学物质。当癌症存在时，细胞代谢会发生变化，因此可以使用上述技术进行测量。观察新陈代谢的变化可以帮助我们了解复杂的疾病，如癌症和可能有效的治疗方法。因此，在这个项目中，我们将从我们的新技术中获取数据，并使用机器学习来了解组织化学成分的变化，以及它与不同亚型癌症的关系，并使用这些信息来预测任何复发性疾病的性质。如果我们能从原发组织预测疾病的复发性质，我们也许能够更有效地使用治疗方法来完全阻止复发。

Glioblastoma (GBM) brain tumours are deadly and incurable. Effective treatment options for GBM are urgently needed. The extent of surgical GBM tumour resection substantially positively impacts patient survival, but damage to normal brain tissue can severely affect quality of life and hence must be minimised. Therefore, it is imperative that we develop tools for surgical removal which can:A) precisely remove cancer cells, with reduced thermal impact on normal brain cells B) give feedback at the time of resection to surgeons so that they know which areas of the brain tissue are malignant and need to be removed and C) give doctors indications of the nature of the cancer so that the most effective therapy can be employed post-operatively for any cancer cells left behind. Development of this technology would be instrumental to revolutionising the way surgeons treat cancer. To do this we will develop high-precision lasers and understand their effects on tissue using very high-definition cameras, we will then collect the 'smoke' produced by these lasers when cutting and feed it into a mass spectrometer which is capable of measuring all of the individual metabolite chemicals representative of the tissue it has come from. When cancer is present, cells metabolise differently and hence this can be measured using the technology described. Observing the changes in metabolism can help us to understand complex diseases such as cancer and the therapies which may be effective against them. Therefore, in this project we will take the data from our novel technology and use machine learning to understand changes in the chemical compositions of the tissues and how that correlates to the different subtypes of cancer and use this information to predict the nature of any recurrent disease. If we can predict the recurrent nature of disease from the primary tissue, we may be able to use therapies more effectively to stop recurrence altogether.