Grading of Oral Epithelial dysplasia: Comparison of WHO three tier grading and the binary grading of Classical and Differentiated dysplasia

Author:

Selvan Preetha1,Ranganathan Kannan1

Affiliation:

1. Ragas Dental College and Hospital

Abstract

Abstract

Background: The WHO Oral Epithelial Dysplasia (OED) three-tier grading is widely used to assess the risk of malignant transformation. There are other grading systems that address the limitations of the WHO grading, but these have not been widely used, and need to be validated. These include the binary systems of low/high grade dysplasia and differentiated/classical dysplasia. This study was done to evaluate the correlation and reproducibility of the grading, between these three systems Methods: Twenty-four OED cases were histologically graded, independently by two oral pathologists. The grading systems used were: the WHO three tier, the binary grading system proposed by Kujan’s (high and low risk) and the binary system of differentiated and classic dysplasia by Arsenic and Kurrer’s . The results were tabulated and kappa statistics was analysed using SPSSTM (version 25) to assess the inter and intra-observer variability Results: Among the twenty-four cases evaluated, 25% (6 cases) were classified as differentiated dysplasia, while the remaining 75% (18 cases) were categorized as classical dysplasia. All the six cases of differentiated dysplasia were high risk by the Kujan’s binary categorization, and by the WHO grading five of the six were severe and one was moderate dysplasia. In the binary system -Kujan’s; of the 24 cases 12 cases were graded as high-risk dysplasia and 12 as low-risk dysplasia by both the observers. The six cases of differentiated dysplasia were all high risk according to the binary categorization by both the observers. Conclusion: The grading of differentiated dysplasia correlates well with the WHO three tier and Kujan’s binary system. These findings highlight the usefulness of identifying differentiated dysplasia and necessity to further validate these grading systems for better prediction models of malignant transformation

Publisher

Research Square Platform LLC

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