title: Quantitative risk stratification of oral leukoplakia with exfoliative cytology.
abstract: Exfoliative cytology has been widely used for early diagnosis of oral squamous cell carcinoma (OSCC). Test outcome is reported as "negative", "atypical" (defined as abnormal epithelial changes of uncertain diagnostic significance), and "positive" (defined as definitive cellular evidence of epithelial dysplasia or carcinoma). The major challenge is how to properly manage the "atypical" patients in order to diagnose OSCC early and prevent OSCC. In this study, we collected exfoliative cytology data, histopathology data, and clinical data of normal subjects (n=102), oral leukoplakia (OLK) patients (n=82), and OSCC patients (n=93), and developed a data analysis procedure for quantitative risk stratification of OLK patients. This procedure involving a step called expert-guided data transformation and reconstruction (EdTAR) which allows automatic data processing and reconstruction and reveals informative signals for subsequent risk stratification. Modern machine learning techniques were utilized to build statistical prediction models on the reconstructed data. Among the several models tested using resampling methods for parameter pruning and performance evaluation, Support Vector Machine (SVM) was found to be optimal with a high sensitivity (median>0.98) and specificity (median>0.99). With the SVM model, we constructed an oral cancer risk index (OCRI) which may potentially guide clinical follow-up of OLK patients. One OLK patient with an initial OCRI of 0.88 developed OSCC after 40 months of follow-up. In conclusion, we have developed a statistical method for qualitative risk stratification of OLK patients. This method may potentially improve cost-effectiveness of clinical follow-up of OLK patients, and help design clinical chemoprevention trial for high-risk populations.
authors.affiliation: Department of Oral Medicine, Beijing Stomatological Hospital, Capital Medical University, Beijing, China.
authors.forename: Yao
authors.initials: Y
authors.lastname: Liu
authors.affiliation: Euclados Bioinformatics Solutions, Cary, NC, United States of America.
authors.forename: Jianying
authors.initials: J
authors.lastname: Li
authors.affiliation: Department of Oral Medicine, Beijing Stomatological Hospital, Capital Medical University, Beijing, China.
authors.forename: Xiaoyong
authors.initials: X
authors.lastname: Liu
authors.affiliation: Ongwandada Resource Center, Queen's University, 191 Portsmouth Avenue, Kingston, ON, Canada.
authors.forename: Xudong
authors.initials: X
authors.lastname: Liu
authors.affiliation: Ross University School of Medicine, 2300 SW 145th Avenue, Miramar, FL, United States of America.
authors.forename: Waqaar
authors.initials: W
authors.lastname: Khawar
authors.affiliation: Beijing Institute of Dental Research, School of Stomatology, Capital Medical University, Beijing, China.
authors.forename: Xinyan
authors.initials: X
authors.lastname: Zhang
authors.affiliation: Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei Province, China.
authors.forename: Fan
authors.initials: F
authors.lastname: Wang
authors.affiliation: Cancer Research Program, Julius L. Chambers Biomedical Biotechnology Research Institute, North Carolina Central University, Durham, NC, United States of America.
authors.forename: Xiaoxin
authors.initials: X
authors.lastname: Chen
authors.affiliation: Department of Oral Medicine, Beijing Stomatological Hospital, Capital Medical University, Beijing, China.
authors.forename: Zheng
authors.initials: Z
authors.lastname: Sun
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D002294:Carcinoma, Squamous Cell / Q000473:pathology; D016022:Case-Control Studies; D003581:Cytodiagnosis / Q000379:methods; D055088:Early Detection of Cancer / Q000379:methods; D005260:Female; D006801:Humans; D007972:Leukoplakia, Oral / Q000473:pathology*; D008297:Male; D008875:Middle Aged; D009062:Mouth Neoplasms / Q000473:pathology*; D012306:Risk; D012680:Sensitivity and Specificity; D013223:Statistics as Topic; D055815:Young Adult