Quantitative prediction of oral cancer risk in patients with oral leukoplakia

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.

Nomograms and risk scores for predicting the risk of oral cancer in different sexes: a large-scale case-control study

“…In addition to predicting the probability of oral cancer, several risk models or risk scores have been constructed in previous studies for risk assessment. Liu et al 16 developed a peaks random forest model according to exfoliative cytology and clinical data for predicting oral cancer risk. Krishna et al 17 built a risk score model using lifestyle data with a small samples in India.…”

Role of Artificial Intelligence in the Early Diagnosis of Oral Cancer. A Scoping Review

“…A total of 384 publications were found to be duplicates and were discarded, thus leaving 1167 articles of which 1110 were excluded after evaluation of the title and abstract. In the second step, and after full-text reading of the 62 selected articles, a total of 36 were included in the review [ 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 ], with a description of the relevant findings ( Figure 1 ).…”

“…(5) the diagnosis of OPMDs such as solar cheilosis [53], oral lichen planus [41,46], leukoplakia [29,40,58], with a prediction of their course [43]; (6)…”

“…(5) the diagnosis of OPMDs such as solar cheilosis [53], oral lichen planus [41,46], leukoplakia [29,40,58], with a prediction of their course [43]; (6) aids to differential diagnosis by classifying the lesions as benign or precancerous [50]; normal mucosa or oral cancer [27,32,35,36,39,42,62]; or as different benign, premalignant and malignant lesions [31,33,39,46,59]; (7) classification of oral cancer [56]; and (8) development of oral cancer risk predictive models [42,47,48,51,55,60].…”

Nanotechnology: a promising method for oral cancer detection and diagnosis

“…The visual tools are highly subjective and depend on the expertise of the investigators [ 16 – 18 ]. The main deficiency of exfoliative cytology technology, which is based on the quantitative cytomorphometry and DNA aneuploidy, is the low detection specificity, resulting from the collection of disaggregated cells [ 12 , 33 , 34 ]. Moreover, the sensitivity for traditional detection methods is limited as the biomarkers with low concentrations in the tissue samples or body fluids may not be detected [ 35 ].…”