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In Journal of oral pathology & medicine : official publication of the International Association of Oral Pathologists and the American Academy of Oral Pathology ; h5-index 0.0

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.

Wang Xiangjian, Yang Jin, Wei Changlei, Zhou Gang, Wu Lanyan, Gao Qinghong, He Xin, Shi Jiahong, Mei Yingying, Liu Ying, Shi Xueke, Wu Fanglong, Luo Jingjing, Guo Yiqing, Zhou Qizhi, Yin Jiaxin, Hu Tao, Lin Mei, Liang Zhi, Zhou Hongmei

2019-Dec-10

Cancer risk level prediction, Non-invasive screening, Oral potentially malignant disorders, Personalized model, Web application