Artificial Intelligence Could Help Doctors Better Predict Oral Cancer Risks

Dentistry Today


Artificial intelligence (AI) can help doctors better predict the risk of patients developing oral cancer, according to researchers at the University of Sheffield, University of Warwick, University of Birmingham, and Queen’s University Belfast. 

Funded by Cancer Research UK (CRUK), the study is examining the use of machine learning and AI to assist pathologists and improve the early detection of oral cancer.

The rate of oral cancer diagnoses including mouth, tongue, tonsil, and oropharyngeal cancer has increased by almost 60% in the past 10 years, the researchers said.

Tobacco and alcohol consumption, viruses, age, and a lack of fruits and vegetables in the diet all can increase the risk of developing the disease, the researchers said. Also, oral cancer often is detected late, which means survival rates are poor.

Doctors currently predict the likelihood of precancerous changes, known as oral epithelial dysplasia (OED), developing into cancer by assessing a patient’s biopsy on 15 different criteria to establish a score that determines whether action is needed and what treatment pathway should be taken.

The score is subjective, which means there often are huge variations in how patients with similar biopsy results are treated. For example, one patient may be advised to undergo surgery and intensive treatment, while another may be monitored for further changes.

“The precise grading of OED Is a huge diagnostic challenge, even for experienced pathologists, as it is so subjective,” said Ali Khurram, PhD, senior clinical lecturer at the University of Sheffield’s School of Clinical Dentistry.

“At the moment, a biopsy may be graded differently by different pathologists. The same pathologist may even grade the same biopsy differently on a different day,” said Khurram.

“Correct grading is vital in early oral cancer detection to inform treatment decisions, enabling a surgeon to determine whether a lesion should be monitored or surgically removed,” Khurram said.

“Machine learning and AI can aid tissue diagnostics by removing subjectivity, using automation and quantification to guide diagnosis and treatment,” Khurram said.

“Until now, this hasn’t been investigated, but AI has the potential to revolutionize oral cancer diagnosis and management by ensuring accuracy, consistency, and objectivity,” Khurram said.

Samples of archived OED tissue samples with at least five years of follow-up data will be used to train AI algorithms and learn the statistical correlations between certain classifiers and survival rates.

These algorithms will help pathologists assess biopsies and make more informed and unbiased decisions about grading cells and the patient’s treatment pathway, the researchers said.

The proposed algorithms have a strong translational angle and the potential to be rapidly deployed as an aid to clinical and diagnostic practice worldwide, according to the researchers.

“People often feel threatened by AI. However, rather than replacing a doctor’s expertise, exceptionally high level of training, and experience, the technology can help to assist their decision-making and compliment their skills,” said Khurram.

“This will help them to give a more accurate assessment and enable them to recommend the most beneficial treatment pathway for individual patients, which we hope will help to improve survival rates,” said Khurram.

“Early detection of cancer is a key focus area of research in our lab, and this award by CRUK adds to the portfolio of research at  the TIA lab on early detection of cancer,” said co-principal investigator Nasir Rajpoot, PhD, of the University of Warwick Department of Computer Science.

“The pilot project will pave the way towards the development of a tool that can help identify premalignant changes in oral dysplasia, crucial for the early detection of oral cancer,” said Rajpoot.

“Successful completion of this project carries significant potential for saving lives and improving patient healthcare provision,” Rajpoot said.

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