# Performance of Automated Oral Cancer Screening Algorithm in Tobacco Users vs. Non-Tobacco Users
## 1 Introduction  
Five-year survival rate in the U.S.A. for oral cancer (OC) approximates 62% and has not improved over decades despite considerable progress in the outcomes of all other major cancers [1]. Additionally, 3-84% of the U.S. population manifests oral potentially premalignant lesions (OPMLs) [2, 3], which carry a risk of malignant transformation of up to 10% per year [4]. The poor survival rate for OC is mainly due to late diagnosis, leading to late treatment onset, reduced treatment options, and greater treatment morbidity and mortality [5]. The primary determinant affecting survival is the cancer's stage at diagnosis [5, 6, 7, 8, 9, 10]. As more than 2/3 of OC lesions are detected late, after the onset of metastasis, treatment outcomes and prognoses are extremely poor [1, 11]. A study in 200,000 individuals found that carefully executed screening for OC in tobacco users resulted in considerably earlier OC detection and a significantly lower rate of OC deaths [12]. However, a preponderance of other studies has reported low screening accuracy, and consequently little or no impact by screening on OC outcomes within the target populations [13, 14]. OPMLs and OC are typically asymptomatic until they reach an advanced stage, so that patients do not experience symptoms that might motivate them to seek professional diagnosis and treatment [1, 11]. Thus, effective, and regular screeningin at-risk individuals is essential. Nevertheless, because OPMLs and early stage OC can have a similar appearance as many other intraoral lesions, they are easily overlooked or misdiagnosed during non-specialist screening. This is especially in tobacco-users, where 60-80% manifest confounding tobacco use-related soft tissue changes [15]. Because tobacco use commonly changes the appearance of the oral soft tissues, and because this habit itself is considered the most important predictor for OC risk [2; 16; 21; 22; 23; 24; 25], it is important that any imaging-based, and/or algorithmic approaches to evaluating OC risk are able to correctly and appropriately evaluate the relevance of each variable to OC risk. This process is essential to ensure that each variable is assigned the appropriate significance or \"weighting\" in the architecture of the analytical process. To the best of our knowledge, this important matter remains to be addressed. In low-resource environments, 80-90% of the population has no ready access to oral medicine or otolaryngology specialists skilled at diagnosing OC [26; 27; 28]. Specialist access and compliance with specialist referral are dependent on factors such as physical proximity of a specialist center, the availability and cost of transportation, child- and eldercare duties, language, cultural, financial, and social barriers, fear, and unfamiliarity with specialist and academic centers. Thus, there exists a strong impetus to develop and upgrade community-based OC screening programs to improve screening and referral accuracy, and to establish new pathways for overcoming barriers to referral compliance. In tobacco users, the oral mucosa commonly manifests a panoply of visible mucosal changes which may impact screening accuracy [15; 29]. Because the accuracy of non-specialist screenings is low (30-60%) [26; 30; 31], providing additional non-specialist screening manpower will have little impact on OC outcomes unless tools are developed that improve screening accuracy. The primary barrier to better OC outcomes is the lack of an effective screening tool at the local point-of-care level. A range of adjunctive screening tools to support effective OC screening in low resource and community settings is under investigation [32; 33; 34; 35; 36; 37]. The accuracy and usefulness of these approaches is predicated on many variables, depending on the specific imaging modality and the applications under consideration [37; 38; 39; 40; 41]. Barriers to the adoption and the effectiveness of such approaches, especially in community-based and low-resource settings, are considerable.