Research Papers:

Quantitative prediction of oral cancer risk in patients with oral leukoplakia

Yao Liu, Yicheng Li, Yue Fu, Tong Liu, Xiaoyong Liu, Xinyan Zhang, Jie Fu, Xiaobing Guan, Tong Chen, Xiaoxin Chen _ and Zheng Sun

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Oncotarget. 2017; 8:46057-46064. https://doi.org/10.18632/oncotarget.17550

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Yao Liu1,*, Yicheng Li2,*, Yue Fu1, Tong Liu1, Xiaoyong Liu3, Xinyan Zhang4, Jie Fu1, Xiaobing Guan1, Tong Chen5, Xiaoxin Chen2 and Zheng Sun1

1Department of Oral Medicine, Beijing Stomatological Hospital, Capital Medical University, Beijing, China

2Cancer Research Program, Julius L. Chambers Biomedical Biotechnology Research Institute, North Carolina Central University, Durham, North Carolina, USA

3Department of Pathology, Beijing Stomatological Hospital, Capital Medical University, Beijing, China

4Beijing Institute of Dental Research, School of Stomatology, Capital Medical University, Beijing, China

5Division 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: [email protected]

Zheng Sun, email: [email protected]

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


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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