Correlation between computer-assisted quantitative autofluorescence imaging results and the pathological grading of oral epithelial dysplasia in oral leukoplakia
10.3969/j.issn.1674-8115.2024.09.009
- VernacularTitle:计算机辅助下自体荧光图像定量结果与口腔白斑病上皮异常增生等级的相关性
- Author:
Chenxi LI
1
;
Zirui WANG
;
Tianhao JIN
;
Zengtong ZHOU
;
Guoyao TANG
;
Linjun SHI
Author Information
1. 上海交通大学医学院附属第九人民医院口腔黏膜病科,上海交通大学口腔医学院,国家口腔医学中心,国家口腔疾病临床医学研究中心,上海市口腔医学重点实验室,上海市口腔医学研究所,上海 200011
- Keywords:
autofluorescence imaging;
oral leukoplakia;
epithelial dysplasia;
ordered multinomial Logistic regression model;
confusion matrix
- From:
Journal of Shanghai Jiaotong University(Medical Science)
2024;44(9):1146-1154
- CountryChina
- Language:Chinese
-
Abstract:
Objective·To explore the correlation between the quantitative results of autofluorescence imaging under computer assistance and the grade of epithelial dysplasia in oral leukoplakia.Methods·From April 2016 to January 2024,357 patients with oral leukoplakia who visited the Department of Oral Mucosal Diseases at Shanghai Ninth People's Hospital,Shanghai Jiao Tong University School of Medicine,were included.Autofluorescence images of the lesions were obtained using a handheld autofluorescence device.These images were converted to grayscale images to obtain quantitative metrics.An ordered multinomial Logistic regression model was fitted in Python,and cumulative probability plots were generated.The dataset was divided into training and testing sets,and a decision tree was generated.Different hyperparameters were adjusted to achieve optimal model performance.Accuracy,precision,and F1 scores were calculated.The model performance was visualized using a confusion matrix.Results·As the degree of epithelial dysplasia increased,the relative mean color level showed a declining trend.In the binary classification of epithelial dysplasia,there was no overlap between the cumulative probability curves of different categories.In the four-category classification,only severe epithelial dysplasia overlapped with other category curves,indicating good discriminative ability of the model.In binary pathological grading,when the training and testing set ratio was 4∶1 and the maximum depth was 2,the accuracy,precision,and F1 scores were 0.792,0.801,and 0.795,respectively.In the four-category pathological grading,when the training and testing set ratio was 9∶1 and the maximum depth was 4,the accuracy,precision,and F1 scores were 0.611,0.537,and 0.569,respectively.Conclusion·Computer-assisted quantitative analysis of autofluorescence images can be used by oral mucosal specialists as a reference to predict the degree of epithelial dysplasia in patients with oral leukoplakia and to monitor their risk of cancer.
