Agreement evaluation of the severity of oral epithelial dysplasia in oral leukoplakia.
10.3760/cma.j.cn112144-20211206-00537
- Author:
Jia Kuan PENG
1
;
Hong Xia DAN
1
;
Hao XU
1
;
Xin ZENG
1
;
Qianming CHEN
2
Author Information
1. Department of Oral Medicine, West China Hospital of Stomatology, Sichuan University & State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, Chengdu 610041, China.
2. Department of Oral Medicine, Stomatology Hospital, School of Stomatology & Zhejiang University School of Medicine & Clinical Research Center for Oral Diseases of Zhejiang Province & Key Laboratory of Oral Biomedical Research of Zhejiang Province & Cancer Center of Zhejiang University, Hangzhou 310006, China.
- Publication Type:Journal Article
- MeSH:
Artificial Intelligence;
China;
Humans;
Leukoplakia, Oral;
Observer Variation;
Precancerous Conditions
- From:
Chinese Journal of Stomatology
2022;57(9):921-926
- CountryChina
- Language:Chinese
-
Abstract:
Objective: To evaluate the inter-observer agreement of the severity of oral epithelial dysplasia in oral leukoplakia, providing a theoretical basis for the development of a more objective grading system. Methods: This study included 60 digital pathological slides of oral leukoplakia from Oral Medicine Department of West China Hospital of Stomatology, Sichuan University, and 239 tissue microarray images of oral leukoplakia from State Key Laboratory of Oral Diseases, Sichuan University, to evaluate the agreement of grading. Besides, 1 000 patches were generated from the 60 digital pathological slides and were divided into 500 small-sized patches (224 pixel×224 pixel) and 500 large-sized patches (1 024 pixel×1 024 pixel), to evaluate the agreement of feature detection. Gradings and feature detections were completed by three pathological experts from the oral pathology departments of two Grade 3, Class A stomatological hospitals in China. Kappa coefficient was used to quantify the inter-observer agreement among pathologists. Results: Minimal agreement was found in the grading of oral epithelial dysplasia among pathologists (Kappa=0.30 in the pathological slide group, Kappa=0.30 in the tissue microarray group). None agreement was found in feature detection within the small-sized patches group (median Kappa=0.14 for architectural features, median Kappa=0.18 for cytological features), and minimal agreement was found in feature detection within the large-sized patches group (median Kappa=0.25 for architectural features, median Kappa=0.25 for cytological features). Conclusions: Generally, the agreement of grading and feature detection of oral epithelial dysplasia in oral leukoplakia is poor. Development of a more objective grading system of oral epithelial dysplasia based on artificial intelligence may be helpful to improve the agreement.
