A personalized computational model predicts cancer risk level of oral potentially malignant disorders and its web application for promotion of non‐invasive screening. (4th January 2020)

• Record Type: Journal Article
• Authors: 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
• Is Part Of: Journal of oral pathology & medicine. Volume 49:Number 5(2020)
• Page Start: 417
• Page End: 426
• Publication Date: 2020-01-04
• Subjects: cancer risk level prediction -- non‐invasive screening -- oral potentially malignant disorders -- personalized model -- web application; Dentistry -- Periodicals; Teeth -- Diseases -- Periodicals; 617
• DOI: 10.1111/jop.12983
• Languages: English
• ISSNs: 0904-2512

Abstract

Abstract: 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.