Multimodal Active Adaptive Risk Stratification For Cancer

根据英国癌症研究中心的数据，2018年，英格兰近一半（45.5%）的癌症病例在第3和第4阶段被诊断出来。在2016 - 2018年，超过一半的新癌症病例是乳腺癌，前列腺癌，肺癌或肠癌。肠癌的症状并不特异，这使得医生更难识别有风险或患有早期癌症的患者。英国也是欧洲结直肠癌生存率最低的国家之一，部分原因是晚期表现，以及诊断和治疗的延误。目前还没有适应性的可解释的决策工具来实现早期癌症诊断。这些工具可能会提高患者的5年生存率和治疗选择。此外，没有系统随着新信息的出现而更新和改进癌症风险。虽然存在风险评分系统实施的NHS，如第一类型QCancer风险分层系统，这些只提供静态的风险estimations.The目的哲学博士建议是创建一个动态的风险评分和诊断测试的早期癌症检测的推荐系统。有三个主题：㈠为初级保健中的多模式和多变量时间序列数据建立癌症风险预测;（iii）建立一个嵌入临床工作流程的诊断测试推荐系统，为早期癌症检测提供决策支持。我们将集中在静态和动态变量的建模在多模态和多变量设置。然后，我们将通过合并患者生存分析来创建CAMELOT++，以指导聚类形成并在新的多标签环境中改善风险预测。进一步的工作将包括开发可解释的表型，这将使我们能够识别与不同类型癌症相关的突出生物标志物。我们还将跟踪这些随时间的变化，并得出不同癌症类型的每种临床表型的可能性。最初的工作将集中在肠癌风险评分的发展上。这些方法将在全科医生电子健康记录的QResearch数据库中进行测试，该数据库与来自英格兰全科医生记录的二级护理结果（REC参考03/4/021; 18/EM/0400）和癌症登记数据相关。该数据库包含有关变量的信息，如人口统计学，诊断，治疗和结果。进一步的探索将集中在创建一个个人的数字双胞胎测试建议，并最终扩大到包括其他癌症类型，如乳腺癌和肺癌。表型算法将使我们能够在初级保健环境中首次识别癌症患者亚组。导出的基于聚类的患者特征以及在每个临床表型中考虑的测试或诊断将有助于告知临床医生要执行的必要测试以辅助决策过程。基于这些，我们的目标是开发基于聚类的患者特异性可解释性图，这将有助于我们了解某些生物标志物和诊断测试如何有助于结果表型。这些地图将通过为临床医生提供透明的框架来进一步支持快速诊断决策。该项目福尔斯EPSRC医疗保健技术主题，以及EPSRC人工智能和机器人以及EPSRC数字孪生领域。我们希望通过在初级保健环境中开发癌症的动态风险评分来解决改变早期预测和诊断的挑战2。我们的决策支持系统将有助于获得患者表型，并确定不同类型癌症的基于聚类的患者特征。最后，通过使用诊断测试的数字孪生或个性化推荐系统，我们希望为患者及其临床医生提供早期预警。

According to Cancer Research UK, almost half (45.5%) of all cancer cases in England were diagnosed at stage 3 & 4 in 2018. More than half of new cancer cases were breast, prostate, lung, or bowel cancer in 2016 - 2018. Symptoms of bowel cancer are not specific, making it harder for doctors to identify patients at risk or with early-stage cancer. The UK also has one of the poorest survival rates for colorectal cancer in Europe, thought to be partly due to late presentation, and delays in diagnosis and treatment. Currently there are no adaptive interpretable decision tools to enable earlier cancer diagnosis. These tools could potentially improve the 5-year survival rate and treatment options available to patients. Furthermore, there are no systems that update and refine cancer risk over time as new information becomes available. Whilst there exist risk scoring systems implemented in the NHS such as the First-of-Type QCancer risk stratification system, these provide only static risk estimates.The goal of this DPhil proposal is to create a dynamic risk score and a recommendation system of diagnostic tests for early cancer detection. There are three themes: (i) Creation of cancer risk prediction for multimodal and multivariate timeseries data in primary care; (ii) Identification of patient subgroups for cancer phenotypes; (iii) Building a recommendation system of diagnostic tests embedded in a clinical workflow to provide decision support for early cancer detection.Building on an in-house phenotyping algorithm, CAMELOT, we will focus on the modelling of static and dynamic variables in the multimodal and multivariate settings. We will then create CAMELOT++ by incorporating patient survival analysis to guide cluster formation and improve risk prediction in a novel multi-label setting. Further work will include the development of interpretable phenotypes that will allow us to identify salient biomarkers that are associated with different types of cancer. We will also follow how these change over time and derive the likelihood of each clinical phenotype with the different cancer types.Initial work would focus on the development of the risk score for bowel cancer. These methods will be tested on the QResearch database of GP electronic health records linked to secondary care outcomes from GP records across England (REC reference 03/4/021; 18/EM/0400) and cancer registry data. The database contains information on variables such as demographics, diagnoses, treatments, and outcomes. Further exploration will be focused on the creation of a personal digital twin for test recommendation and finally be expanded by including other cancer types, such as breast and lung cancer.The phenotyping algorithm would enable us to identify cancer patient subgroups for the first time in primary care settings. The derived cluster-based patient characteristics as well as tests or diagnoses considered in each clinical phenotype would help inform the clinicians of the necessary tests to perform to assist the decision-making process. Based on these, we aim to develop cluster-based patient-specific interpretability maps which will help us understand how certain biomarkers and diagnostic tests contribute to the outcome phenotypes. These maps would further support rapid diagnostic decisions by providing clinicians with a transparent framework.This project falls within the EPSRC healthcare technologies theme, and the EPSRC artificial intelligence and robots and EPSRC digital twin area. We hope to address challenge 2 of transforming early prediction and diagnosis through developing a dynamic risk score for cancer in a primary care setting. Our decision-support system would help derive patient phenotypes and identify cluster-based patient characteristics for different types of cancer. Lastly, by using a digital twin or personalised recommendation system of diagnostic tests we hope to provide early warnings to the patients and their clinicians.