A pilot study to develop an efficacious oral cancer screening strategy for India

口腔癌在全球范围内造成了巨大的负担，特别是在低收入和中等收入国家（LMIC）（8）。口腔癌的高负担可归因于烟草、槟榔（tamul）和酒精等风险因素的高流行率。在大多数国家，吸烟是烟草使用的主要形式（9）。然而，在印度，大约75%的烟草消费是无烟形式的（9）。印度是全球口腔癌负担最高的国家（10）。人们认识到，无烟烟草会导致口腔癌、食道癌和胰腺癌（11）。拟议的研究将在阿萨姆邦进行，那里的烟草流行率为48%（12）。在阿萨姆邦，塔穆尔传统上被视为尊重和吉祥开端的标志。我们最近在阿萨姆邦进行了一项关于泰米尔使用的调查。总的来说，34%的受试者报告使用tamul。影响因素是家庭（58%）和朋友（34%）。大多数受试者（60%）由于工作压力、饭后和休闲时咀嚼tamul，平均开始年龄为15岁。虽然对tamul的不良影响的知识很低，戒烟意愿很高（77%），但矛盾的是，81%的受试者在过去6个月内没有尝试戒烟。口腔癌的5年生存率在早期诊断的患者中为80%，而在晚期诊断的患者中仅为20%，强调了早期发现的必要性（13）。在印度，大多数口腔癌患者表现为晚期疾病，生存率低至3- 5%。因此，迫切需要早期诊断，最好是在癌前阶段（2）。在印度和其他中低收入国家，目前的口腔癌筛查方法依赖于对30岁以上吸烟/饮酒者口腔内壁的目视检查（常规口腔检查[COE]）（4）。然而，由于筛选方法（COE的低敏感性）和目标人群（30岁以上）的规模问题，这种方法并不有效。例如，在印度，根据COE，符合口腔癌筛查条件的人口约为3亿，这降低了其操作可行性，使其成本过高。在低收入国家开发的任何筛查系统都应具备两个特点，以实现最佳效果：1）筛查测试应具有高度准确性2）筛查系统（基础设施、人力资源和转诊中心）必须在财政上可行。不幸的是，印度和其他几个中低收入国家目前的口腔癌目视检查筛查策略在这两个层面上都失败了。系统识别高危人群的风险分层工具可能会大大减少筛查的目标人群，我们提出了一个3步口腔癌筛查策略，使筛查计划的有效实施。首先，我们会采用一个风险预测模式，以识别最有可能患上口腔癌的人士，从而减少接受检查的人数，并提高成本效益。第二，高风险的个体将使用自体荧光进行筛查，这是一种诊断口腔癌的高灵敏度测试，通过只使异常区域发光来排除疾病。最后，自体荧光阳性的个体将通过刷检活检来寻找可疑细胞，这是一种对诊断口腔癌具有高度特异性的测试。我们将验证所提出的3步策略的风险预测模型，作为该泵预充应用的一部分，随后将在印度东北部人群中进行一项大型随机对照试验，以测试其有效性。我们的研究将为在中低收入国家实施口腔癌筛查提供3步筛查策略的原理证明。

Oral cancer imposes a huge burden globally, especially in low and middle income countries (LMICs) (8). This high burden of oral cancer is attributable to high prevalence of risk factors such as tobacco, areca nut (tamul) and alcohol. In most countries, cigarette smoking is the predominant form of tobacco use (9). However, in India, approximately 75% of tobacco consumption is in smokeless form (9). India has the highest burden of oral cancer globally (10). It is recognized that smokeless tobacco causes cancers of the mouth, gullet and pancreas (11). The proposed study will be conducted in Assam, where the prevalence of tobacco is 48% (12). In Assam, tamul is traditionally offered as a mark of respect and auspicious beginnings. We recently conducted a survey in Assam on tamul use. In all, 34% of subjects reported using tamul. Influencing factors were family (58%) and friends (34%). Majority of the subjects (60%) chewed tamul due to pressure at work, after food and during leisure with a mean age of initiation at 15 years. While knowledge about the ill-effects of tamul was low and willingness to quit high (77%), paradoxically, 81% of subjects had not attempted to quit the habit in the preceding 6 months.The 5-year survival rate for oral cancer is 80% in those diagnosed at early stage and only 20% in those diagnosed at advanced stage, underscoring the need for early detection (13). In India, most oral cancer patients present with advanced disease and have survival rates as low as 3-5%. There is, therefore, a pressing need to diagnose early and ideally at a pre-cancerous stage (2).In India and other LMICs, the current approach for oral cancer screening relies on visual inspection of the inner lining of the oral cavity (conventional oral examination [COE]) in tobacco/ alcohol users aged over 30 years (4). Yet, owing to issues with the method of screening (low sensitivity of COE) and size of the target population (ages 30+), this approach is not efficacious. For example, the eligible population for oral cancer screening in India based on COE is ~300 million, which reduces its operational feasibility and makes it cost prohibitive. Any screening system developed in LMICs should fulfill two features for optimal effectiveness: 1) The screening test should have high accuracy 2) The screening system (infrastructure, human resource and referral centres) has to be financially viable. Unfortunately, the current oral cancer screening strategy of visual inspection in India and several other LMICs fails at both these levels. Risk stratification tools for systematic identification of high-risk population could potentially drastically reduce the target population for screening.We propose a 3-step oral cancer screening strategy to enable effective implementation of the screening program. First, a risk prediction model will be used for identification of those at greatest risk of oral cancer, thus reducing the number of screened individuals and enhancing cost effectiveness. Second, high-risk individuals will be screened using autoflourescence, a highly sensitive test for diagnosing oral cancer to rule-out disease by causing only the abnormal areas to light up. Finally, individuals positive on autofluorescence will be ruled-in through a brush biopsy to look for suspicious cells, a test that is highly specific for diagnosing oral cancer. We will validate the risk prediction model of the proposed 3-step strategy as part of this pump priming application which will subsequently lead to a large randomized controlled trial in the population of North East India to test its effectiveness. Our study will provide proof-of-principle of the 3-step screening strategy for implementation of the oral cancer screening in LMICs.