Title: Artificial Intelligence based assessment of oral precancer to aid early detection of oral cancer
Funder: Department of Health and Social Care

Abstract: Background Oral squamous cell carcinoma (OSCC) is amongst the top ten leading cancers worldwide, with an increasing incidence and worsening prognosis. UK OSCC rates have increased by 64% over the last decade, with almost 8,000 people affected each year. Despite advancement in medical and surgical techniques, less than 50% of OSCC patients survive longer than five-years. Patients with OSCC are more likely to die than those with breast, lung, colorectal and cervical cancers emphasising the importance of early and accurate diagnosis. Oral Epithelial Dysplasia (OED) represents precancerous oral changes and is the precursor to OSCC. Around 30-80% of OED lesions can progress to malignancy, but with early intervention up to 70% can be prevented. Histopathological analysis remains the diagnostic gold standard. However, oral precancer/OED grading based on the World Health Organisation criteria remains highly subjective. Since the grade is used to determine cancer risk and treatment decisions, an inaccurate diagnosis can be detrimental to patients. Artificial Intelligence (AI) can eliminate this variability by introducing objective grading and reproducibility, which can play a key role in early detection and improved patient management.Aim To develop novel AI software to aid early and accurate oral precancer diagnosis and prediction of malignant transformation. Research Questions 1. Can AI accurately and consistently diagnose and grade oral precancer? 2. Can AI reveal novel information about the progression from oral precancer to cancer? 3. Can AI be used to predict the risk of oral cancer development? Methods High-resolution digital images of oral precancerous tissue, in addition to normal and cancerous tissue will be used to develop AI algorithms. Cases will be identified from the pathology archive in Sheffield between 2008-2013 to yield five-year clinical follow-up data. Ethical approval is already in place. Stage one will involve training the AI algorithms to detect specific histological features of OED (as outlined in WHO criteria). Stage two will use existing AI algorithms to identify novel features predictive of OED progression into cancer. Stage three will develop a cancer-risk prediction tool correlating clinical information with significant features identified in the first two stages.Potential Impact This research will be innovative and translational and has the potential to positively impact on the reduction of OSCC rates by aiding early and accurate precancer detection. The AI software will provide patients and clinicians with faster and more reliable information about the diagnosis and prognosis of oral precancer, which will be crucial to guide future patient management. Patients will be engaged throughout the study to obtain feedback on the ongoing work and results to enhance translational impact. Study findings will be widely shared with academic and clinical colleagues. This study will be important to facilitate future multi-centric research with other UK hospitals and cancer centres internationally (including low-and middle-income countries) to further optimise the developed software and determine its impact on early and accurate detection. It is anticipated that this study will lead to a clinical trial to provide patients and clinicians with objective information about the diagnosis and prognosis of suspicious oral lesions.

Lay abstract: Mouth cancer is amongst the top ten most common cancers worldwide, with increasing numbers and worsening survival. UK mouth cancer incidence figures have alarmingly risen by 64% in the last decade with almost 8,000 people diagnosed and 3,000 deaths every year. Despite recent national mouth cancer awareness campaigns and advancements in medical and surgical treatments, survival remains poor with less than 50% of people surviving longer than 5 years. Detection of early cancerous areas (precancer) is critical to reduce cancer development, since up to 70% of cases can be prevented through early treatment. Mouth cancer can affect the lips, tongue, gums, inside cheek, roof of mouth and throat. It commonly affects males, people who smoke, chew tobacco or drink excessive alcohol. It can start as a non-healing ulcer, an unexplained swelling, red or white patch. Since the appearance of mouth precancer is variable, it can be difficult for dentists and doctors to detect it at an early stage. Removal of a part of these suspicious areas (i.e. biopsy) and examination under a microscope by a pathologist is the standard method for diagnosis. However, the criterion conventionally used for diagnosis and prediction of cancer risk is ambiguous, resulting in differences in opinion and interpretation. This variation poses a significant challenge, because surgeons use the pathologist's diagnosis to decide whether the area needs to be surgically removed or clinically monitored, and how often patients need to be reviewed. This highlights the need for new methods and technologies to provide more reliable information about mouth precancer diagnosis and the risk of developing cancer. Artificial Intelligence (AI) has become a common part of our lives with applications most of us are familiar with and regularly use (e.g. Google-maps, Siri, Alexa and fingerprint unlock features on mobile devices). The use of AI for cancer diagnosis is growing in popularity and has shown to be successful in providing faster and more objective information by overcoming errors and variations in human/clinical interpretation. This has been demonstrated in a recent study which showed AI to be superior to expert pathologists in detecting the spread of breast cancer from digital images of lymph node biopsies. My research will focus on the use of AI to improve the accuracy and speed of mouth precancer diagnosis, providing patients and clinicians with more objective information about the risk of developing cancer. A three-year study will develop unique AI software using existing mouth biopsies of different stages of precancer (compared to normal and cancerous areas). The software will be trained to detect important microscopic features of precancer to a high level of accuracy, enabling prediction of diagnosis. Quantitative measures of feature detection will be correlated with patient data (medical and social factors) to determine the risk of cancer development - a key stage developed with patient input. This will be crucial to guide future patient management and reduce cancer associated NHS costs. This idea has already received positive response from a local group of patients and public who felt this research could 'revolutionise mouth cancer diagnosis'. Study findings will be widely disseminated through patient/public engagement programmes, social media and presentations at clinical and academic meetings/conferences. Future work will involve a clinical trial with other UK and international cancer hospitals, to validate the developed software and determine its impact on patient management. Further modification of the software may aid the detection of other cancers and lead to the development of an App to be used by clinicians and patients. The success of this research has the potential to make a significant reduction in mouth cancer rates by enabling early detection and treatment.

Research activity codes: 4.2 EVALUATION OF MARKERS AND TECHNOLOGIES
Health categories: CANCER AND NEOPLASMS