Quantitative prediction of oral cancer risk in patients with oral leukoplakia

医学 脱落细胞学 口腔白斑 北京 癌症 口腔医学 内科学 白斑 病理 中国 细胞学 牙科 政治学 法学
作者
Yao Liu,Yicheng Li,Yue Fu,Tong Liu,Xiaoyong Liu,Xinyan Zhang,Jie Fu,Xiaobing Guan,Tong Chen,Xiaoxin Chen,Zheng Sun
出处
期刊:Oncotarget [Impact Journals LLC]
卷期号:8 (28): 46057-46064 被引量:31
标识
DOI:10.18632/oncotarget.17550
摘要

// Yao Liu 1, * , Yicheng Li 2, * , Yue Fu 1 , Tong Liu 1 , Xiaoyong Liu 3 , Xinyan Zhang 4 , Jie Fu 1 , Xiaobing Guan 1 , Tong Chen 5 , Xiaoxin Chen 2 and Zheng Sun 1 1 Department of Oral Medicine, Beijing Stomatological Hospital, Capital Medical University, Beijing, China 2 Cancer Research Program, Julius L. Chambers Biomedical Biotechnology Research Institute, North Carolina Central University, Durham, North Carolina, USA 3 Department of Pathology, Beijing Stomatological Hospital, Capital Medical University, Beijing, China 4 Beijing Institute of Dental Research, School of Stomatology, Capital Medical University, Beijing, China 5 Division of Medical Oncology, Department of Internal Medicine, The Ohio State University, Columbus, Ohio, USA * These authors have contributed equally to this work Correspondence to: Xiaoxin Chen, email: lchen@nccu.edu Zheng Sun, email: zhengsun12@vip.126.com Keywords: exfoliative cytology, oral cancer risk, oral leukoplakia, oral squamous cell carcinoma, quantitative prediction Received: January 13, 2017 Accepted: February 28, 2017 Published: May 02, 2017 ABSTRACT Exfoliative cytology has been widely used for early diagnosis of oral squamous cell carcinoma. We have developed an oral cancer risk index using DNA index value to quantitatively assess cancer risk in patients with oral leukoplakia, but with limited success. In order to improve the performance of the risk index, we collected exfoliative cytology, histopathology, and clinical follow-up data from two independent cohorts of normal, leukoplakia and cancer subjects (training set and validation set). Peaks were defined on the basis of first derivatives with positives, and modern machine learning techniques were utilized to build statistical prediction models on the reconstructed data. Random forest was found to be the best model with high sensitivity (100%) and specificity (99.2%). Using the Peaks-Random Forest model, we constructed an index (OCRI2) as a quantitative measurement of cancer risk. Among 11 leukoplakia patients with an OCRI2 over 0.5, 4 (36.4%) developed cancer during follow-up (23 ± 20 months), whereas 3 (5.3%) of 57 leukoplakia patients with an OCRI2 less than 0.5 developed cancer (32 ± 31 months). OCRI2 is better than other methods in predicting oral squamous cell carcinoma during follow-up. In conclusion, we have developed an exfoliative cytology-based method for quantitative prediction of cancer risk in patients with oral leukoplakia.
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