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HKU Scientists Develop AI-Based Web Tool

Image Credits: HKU, Press Release

Researchers from the Faculty of Dentistry and the Li Ka Shing Faculty of Medicine, the University of Hong Kong (HKU); Department of Pathology, Queen Mary Hospital, HK; and College of Medicine and Dentistry, James Cook University, Queensland collaborated to develop a web platform that can be applied to automatically generate an individualiSed prediction of the risk of oral cancer occurrence in those with OL or OLM for up to 20 years following diagnosis. The results are published in the journal Cancers in an article entitled “Deep learning predicts the malignant-transformation-free survival of oral potentially malignant disorders”.

The freely available web tool, based on the artificial intelligence algorithm ‘DeepSurv’ was trained and tested with data from patients with OL/OLM treated in Hong Kong (716 patients) and Newcastle Upon Tyne, UK (382 patients). As the patients have already been under review for many years, their true risk levels were already known, and the study showed that the artificial intelligent model was able to accurately predict their risk levels at different time points during their follow-up hospital visits.

The DeepSurv algorithm was selected due to its superior performance for the use of routine demographic, clinical, pathological, and treatment information of these patients for cancer risk prediction following a series of validation exercises.

On a validation subset of the Hong Kong cohort, ‘DeepSurv’ was able to predict the correct cancer risk level for 95% of the cases. This was according to an integrated Brier score of 0.04, with a score below 0.25 generally depicting a tool that may be useful in real-world applications. The algorithm is further able to generate correct risk levels for 82% of the patients in the British cohort which suggests its utility in other populations as well.

To expatiate on how the interactive web tool functions, it requires 26 pieces of information on the demography, clinical and pathological description of the disease, and treatment received by the OL/OLM patient. The predicted output from the web tool includes a curve from which the different risk levels (vertical axis) can be visualised at each time point (horizontal axis). These predicted risk levels are accurate up to 17 years from the time that the information was entered.

This will assist health professionals in the selection and prioritisation of treatment strategies and close-monitoring schedules for high-risk patients, especially in resource-limited hospitals. Ultimately, this is expected to improve the currently available methods of prevention and early diagnosis of oral cancer. The prediction curve may also be used for individual cancer risk estimation and to inform health professionals when to commence very close monitoring of patients when a certain risk level is reached.

For OL/OLM patients, risk awareness may motivate them to regularly attend routine follow-up visits and allow them to make informed decisions when providing consent for biopsy when required. Of note, the predicted risk curve may change with varying input data such as smoking and alcohol drinking status, parts of the mouth that are affected, treatment received, lesion recurrence, and the severity of epithelial dysplasia during treatment monitoring.

Dr Richard Su, Clinical Associate Professor in Oral and Maxillofacial Surgery (OMFS), Faculty of Dentistry, HKU, who led the study stated that while the web tool has been found promising based on the team’s validation exercises, users should know that it is still primarily a research-based tool and requires further prospective optimisation. She also noted that since cancer development involves many alterations at the molecular level that may occur before disease diagnosis, in the future, the team will optimise the web tool by including information on molecular biomarkers for cancer development in OL and OLM.

It is expected that the inclusion of the information in the web tool will improve the precision of the predicted risk estimates. The updated web tool will then be evaluated for its clinical efficacy and its impact on the care of OL and OLM in a clinical trial.

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