Oral cancer is often detected late which means that the patient survival rates are poor.
Artificial intelligence (AI) may help doctors better predict the risk of patients developing oral
cancer by ensuring accuracy, consistency and objectivity, according to researchers from the
University of Sheffield in the U.K.
The researchers are examining the use of AI and machine learning — the study of computer
algorithms that improve automatically through experience — to assist pathologists and
improve the early detection of oral cancer.
The rate of people being diagnosed with oral cancers including mouth, tongue, tonsil and
oropharyngeal cancer, has increased by almost 60% in the last 10 years, the researchers said
in a statement.
Evidence suggests tobacco and alcohol consumption, viruses, old age as well as not eating
enough fruit and vegetables can increase the risk of developing the disease, they said.
Oral cancer is often detected late which means that the patient survival rates are poor.
Currently, doctors must predict the likelihood of pre-cancerous changes, known as oral
epithelial dysplasia (OED), developing into cancer by assessing a patient’s biopsy on 15
different criteria to establish a score. This score then determines whether action is needed and
what treatment pathway should be taken. However, this score is subjective, which means
there are often 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 patient 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 Dr. Ali Khurram, 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, Khurram noted.
He said 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.
“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 revolutionise oral cancer diagnosis
and management by ensuring accuracy, consistency and objectivity.”
Samples of archived OED tissue samples with at least five years of follow up data will be
used in order to train AI algorithms and learn the statistical correlations between certain
classifiers and survival rates. These algorithms will aid pathologists in their assessment of
biopsies helping them to make a more informed and unbiased decision about the grading of
the cells and the patient’s treatment pathway.
The proposed algorithms have a strong translational angle and a potential to be rapidly
deployed as an aid to clinical and diagnostic practice worldwide.
“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.”
According to Professor Nasir Rajpoot, from the University of Warwick in the U.K., the pilot
project will pave the way towards the development of a tool that can help identify premalignant
changes in oral dysplasia, which is crucial for the early detection of oral cancer.
“Successful completion of this project carries significant potential for saving lives and
improving patient healthcare provision, said Rajpoot, one of the researchers.
https://www.thehindu.com/sci-tech/health/artificial-intelligence-helps-better-predict-mouthcancer-
risk/article33028342.ece?homepage=true
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