A personalized computational model predicts cancer risk level of oral potentially malignant disorders and its web application for promotion of non-invasive screening

Background: Despite their high accuracy to recognize oral potentially malignant disorders (OPMDs) with cancer risk, non-invasive oral assays are poor in discerning whether the risk is high or low. However, it is critical to identify the risk levels, since high-risk patients need active intervention, while low-risk ones simply need to be follow-up. This study aimed at developing a personalized computational model to predict cancer risk level of OPMDs and explore its potential web application in OPMDs screening.

Methods: Each enrolled patient was subjected to the following procedure: personal information collection, non-invasive oral examination, oral tissue biopsy and histopathological analysis, treatment, and follow-up. Patients were randomly divided into a training set (N = 159) and a test set (N = 107). Random forest was used to establish classification models. A baseline model (model-B) and a personalized model (model-P) were created. The former used the non-invasive scores only, while the latter was incremented with appropriate personal features.

Results: We compared the respective performance of cancer risk level prediction by model-B, model-P, and clinical experts. Our data suggested that all three have a similar level of specificity around 90%. In contrast, the sensitivity of model-P is beyond 80% and superior to the other two. The improvement of sensitivity by model-P reduced the misclassification of high-risk patients as low-risk ones. We deployed model-P in web.opmd-risk.com, which can be freely and conveniently accessed.

Conclusion: We have proposed a novel machine-learning model for precise and cost-effective OPMDs screening, which integrates clinical examinations, machine learning, and information technology.