Optimal screening and surveillance regimes for early diagnosis of cancer and precision medicine using mathematical modelling

癌症筛查的主要原理是早期发现疾病提供了改变预后的机会。与有症状的癌症相比，发现有癌前病变或早期发现的小癌症的患者的终生预后往往大大改善。因此，临床医生专注于在初始测试（屏幕）上识别具有癌前变化的患者，然后在此初始筛选之后，可以建议这些患者通过在其整个生命过程中以一定间隔返回诊所进行定期检查（监视屏幕）来进行长期定期筛选。然而，许多癌前病变在患者的一生中永远不会进展为癌症。因此，许多接受定期监测的患者在其一生中永远不会被诊断出患有癌症。总的来说，目前许多通过筛查和监测计划进行预防的方法在降低癌症死亡方面取得了很小的成功，但医疗保健服务的成本很高，因此矛盾的是，由于筛查不足而导致诊断不足，由于监测方案中无效的患者分层而导致过度诊断。这就是在风险分层过程中采取的平衡行动-确定谁最有“风险”发展为癌症，并为高危群体提出有效的监测和干预战略。出于这一动机，本研究计划中提出的数学建模的总体公共卫生目标是帮助提高癌前病变和癌症病变筛查和监测的有效性。虽然临床终点如癌症发病率和癌前变化的患病率是在人群水平上报告的，但在较小的物理和时间尺度上发生的许多重要生物学过程在从正常组织到偶发癌症的疾病进展期间在患者之间存在显著差异。由于在患者生命中的不同时间筛查患者的生物学和临床性质，组织和细胞水平上的这些细节提供了重要信息，以确定通过不同模式和方案获得的筛查结果。该研究计划将以创造性和严格的方式使用许多此类级别的数据，以改善个性化医疗保健。在MRC优先领域精密医学和诊断中，癌症形成的数学模型可用于推导和优化临床筛查的时间，以便在可能观察到早期癌症发展时，在一定的“机会窗口”内对个体进行干预筛查。通过使用流行病学研究的数据与长期患者随访，目前的经验方法可以帮助筛选设计，并可能告知成本效益分析，以比较拟议的筛选和干预策略。然而，结合了更高水平的生物学理解和正常组织如何以及何时进展为癌症的详细信息的机制模型可以用于比通常在人群筛查研究中实施的更精细的筛查设计。这项英国健康数据研究计划的目的是1）使用数学模型为人群提供最佳癌症筛查建议，2）通过识别谁是低风险与谁是高风险来进行患者风险分层，以便制定比“一刀切”方法更有效的个性化监测制度，3）建立有助于精确医学的工具，例如在视觉辅助中清楚地描绘患者未来的癌症风险。

The main rationale for cancer screening is that detecting disease early offers the opportunity to change its prognosis. Compared with symptomatic cancers, the lifetime prognosis is often greatly improved for patients found to have precancerous lesions or small cancers that are detected at an early stage. Therefore, clinicians focus on identifying patients with precancerous change on initial tests (screens), and then after this initial screening may advise these patients to undergo long-term, periodic screening by returning to the clinic at certain intervals for regular examinations (surveillance screens) throughout the course of their lives. However, many precancerous changes will never progress to cancer in the lifetime of the patient. Thus, many patients who undergo regular surveillance will never be diagnosed with cancer in their lifetimes. Overall, many current approaches for prevention by screening and surveillance programs have achieved minimal success in reducing cancer deaths at a high cost to healthcare services, and thus paradoxically yield both under-diagnosis due to inadequate screening and over-diagnosis due to ineffective patient stratification in surveillance protocols. Herein lies the balancing act performed during risk stratification - identify who is most `at risk' of progressing to cancer and suggest effective surveillance and intervention strategies for the high risk groups. With this motivation, the overall public health goal of the mathematical modelling presented in this research plan is to help improve the efficacy of screening and surveillance for precancerous and cancer lesions. Although clinical endpoints like cancer incidence and prevalence of pre-cancerous change are reported on a population level, many important biological processes on smaller physical and temporal scales occur with significant differences between patients during disease progression from normal tissue to incident cancer. Due to the biological and clinical nature of screening patients at various times during their lives, such details at tissue and cell levels provide vital information to determine screening outcomes obtained by different modalities and protocols. This research plan will use many such levels of data in inventive and rigorous ways to improve personalized healthcare. Within the MRC priority area Precision Medicine and Diagnostics, mathematical modelling of cancer formation can be used to derive and to optimise the timing of clinical screens so that an individual is screened within a certain "window of opportunity" for intervention when early cancer development may be observed. By using data from epidemiological studies with long-term patient follow-up, current empirical approaches can aid in screening design and may inform cost-effectiveness analyses to compare proposed screening and intervention strategies. However, mechanistic modelling that incorporates a greater level of biological understanding and detail for how and when normal tissues progress to cancer can be used for a more refined screening design than typically implemented in population screening studies.The aims of this Health Data Research UK research plan are1) To use mathematical modelling to inform optimal cancer screening recommendations for a population,2) To perform patient risk stratification by identifying who is low risk versus who is high risk in order to make personalised surveillance regimes that are more effective than "one-size-fits-all" approaches, 3) To build tools that will assist in precision medicine such as clearly portraying future cancer risk to a patient in visuals aids.