Clinical Review and Management of Oral Potentially Malignant Disorders with Epithelial Dysplasia

Background: Oral potentially malignant disorders (OPMDs) comprise any disorders, tumors, in addition to any microscopic alterations that have a risk of malignant development of cancers of the mouth. When epithelial dysplasia is detected in an oral lesion, it is termed as a precancerous lesion. Finding: Several changes in the color or thickness of normal oral mucosa might be detected during the clinical diagnosis of the oral lesions. Leukoplakia of the oral cavity is a clinical name for one of the most predominant OPMDs of the oral mucosa. When comparing oral examination with naked eyes to planning to apply staining with special stain or using an image of optical fluorescence, the incidence of patients with oral epithelial dysplasia may rise, as well as the clearing of the lesion boundary. Increased size of more than 2cm2, the presence of colored regions with a red hue, the presence of lichenoid process characteristics, and severe epithelial dysplasia are all considered risk factors. One-third of premalignant lesions may progress to cancer, whereas the other two-thirds may stay stable or regress without progressing to malignancy. Conclusion: It is critical to research the patients' unique characteristics, which include psychological, genetic, dietary, and dental problems. When epithelial dysplasia is present in an oral lesion, it is termed a precancerous lesion. Oral potential malignant diseases with epithelial dysplasia may or may not develop into carcinoma and may or may not be recurrent.

Deep Learning Predicts the Malignant-Transformation-Free Survival of Oral Potentially Malignant Disorders

Machine-intelligence platforms for the prediction of the probability of malignant transformation of oral potentially malignant disorders are required as adjunctive decision-making platforms in contemporary clinical practice. This study utilized time-to-event learning models to predict malignant transformation in oral leukoplakia and oral lichenoid lesions. A total of 1098 patients with oral white lesions from two institutions were included in this study. In all, 26 features available from electronic health records were used to train four learning algorithms—Cox-Time, DeepHit, DeepSurv, random survival forest (RSF)—and one standard statistical method—Cox proportional hazards model. Discriminatory performance, calibration of survival estimates, and model stability were assessed using a concordance index (c-index), integrated Brier score (IBS), and standard deviation of the averaged c-index and IBS following training cross-validation. This study found that DeepSurv (c-index: 0.95, IBS: 0.04) and RSF (c-index: 0.91, IBS: 0.03) were the two outperforming models based on discrimination and calibration following internal validation. However, DeepSurv was more stable than RSF upon cross-validation. External validation confirmed the utility of DeepSurv for discrimination (c-index—0.82 vs. 0.73) and RSF for individual survival estimates (0.18 vs. 0.03). We deployed the DeepSurv model to encourage incipient application in clinical practice. Overall, time-to-event models are successful in predicting the malignant transformation of oral leukoplakia and oral lichenoid lesions.

Scope of Conventional and Fractal Morphometry in Oral Potentially Malignant Disorders and Oral Cancer

World Health Organization (WHO) defined the terminology ‘Oral Potentially malignant disorders’ (OPMD) as the presence of risk of malignancy in a lesion or condition either during the time of initial diagnosis or at a future date with the commonly accepted prevalence of 1–5%. All OPMDs may not transform into malignancy, many factors have been explored which effectively assess the risk of malignant transformation in OPMDs including many clinical, pathological and molecular factors. Qualitative & experimental factors of different cells in cytological preparations and/or biopsy specimens are reliable parameters for pathologists. The analysis and assessment of histological units can be enhanced by image analysis assisted by a computer that can be used for statistical comparisons. Fractal geometry is considered to be an ideal method of image analysis in quantitative microscopy & histopathology. Fractal dimension analysis is not only limited to determining cell and tumour types but can also be used for determining cellular behaviours in vitro such as cell migration, apoptosis and cellular differentiation, which can be a useful characterization of oral cancer lines and further help in the treatment planning. Keywords: Oral Cancer, Oral Potentially Malignant Disorders (OPMDs), Conventional morphometry, Fractal Dimension, Photomicrograph