Blood metabolomics as a next-generation cancer diagnostic platform technology

癌症的早期诊断可以改善患者的预后并挽救生命。不幸的是，许多癌症目前被诊断为晚期，在治疗选择有限的阶段。这个问题的一个根本原因是常规成像技术（如CT扫描和MRI）的不完善处方，这两种技术都常用于癌症诊断。目前，几乎所有的癌症都是通过观察有症状的患者进行诊断的，但是医生们正在使用不完整的支持信息，并且需要对何时对患者进行成像以及成像结果表明什么进行不完善的呼叫。除了患者预后较差之外，目前的方法对医疗保健系统来说既昂贵又浪费，同时也给患者及其家人带来压力。Oxomics技术使用简单通用的血液测试诊断癌症，再加上机器学习，识别血液中称为代谢物的小分子的癌症特异性模式。我们的测试适合现有的患者护理途径，并提供快速的早期诊断，允许在正确的时间转诊正确的患者进行正确的成像，改善患者的预后。我们已经证明，我们的技术可以用于准确地发现患有非特异性症状的患者中的癌症，包括我们在成像可见之前发现癌症的病例。这项应用的目标是进一步发展我们的技术，使其成为医生每天都可以使用的受监管的临床测试。我们将通过开发基础分类软件，在特定的关键癌症中建立临床证据，并与监管机构合作，以确保我们测试的质量和可重复性。

Early diagnosis of cancer improves patient outcomes and saves lives. Unfortunately, a lot of cancers are currently diagnosed late, at stages where treatment options are limited. One underlying cause of this problem is imperfect prescription of routine imaging technologies like CT scanning and MRI, both of which are commonly used for cancer diagnosis. Currently, almost all cancers are diagnosed by observing a symptomatic patient with imaging, but doctors are working with incomplete supporting information and a requirement to make imperfect calls about when to image patients and what imaging results indicate, if anything at all. Apart from poorer patient outcomes, the current approach is both costly and wasteful for the healthcare system while also being stressful for patients and their families.Oxomics technology diagnoses cancer using a simple, universal blood test, coupled with machine learning which identifies cancer-specific patterns of small molecules in the blood called metabolites. Our test fits into existing patient care pathways and offers rapid earlier diagnoses, allowing referral of the right patients for the right imaging at the right time, improving patient outcomes. We have already shown that our technology can be used to accurately find cancer in an under-served population of patients with nonspecific symptoms, including cases where we found cancers before they were visible by imaging.The goal of this application is to develop our technology further so that it is available as a regulated clinical test that can be used every day by doctors. We will do this by developing the underlying classification software, building clinical evidence in specific key cancers and working with regulators to ensure quality and reproducibility from our tests.