Journal of Shanghai Jiao Tong University (Medical Science) >

2024 , Vol. 44 >Issue 9: 1146 - 1154

DOI: https://doi.org/10.3969/j.issn.1674-8115.2024.09.009

Clinical research

Correlation between computer-assisted quantitative autofluorescence imaging results and the pathological grading of oral epithelial dysplasia in oral leukoplakia

  • Chenxi LI ,
  • Zirui WANG ,
  • Tianhao JIN ,
  • Zengtong ZHOU ,
  • Guoyao TANG ,
  • Linjun SHI
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  • 1.Department of Oral Mucosal Diseases, Shanghai Ninth People′s Hospital, Shanghai Jiao Tong University School of Medicine; College of Stomatology, Shanghai Jiao Tong University; National Center for Stomatology; National Clinical Research Center for Oral Diseases; Shanghai Key Laboratory of Stomatology; Shanghai Research Institute of Stomatology, Shanghai 200011, China
    2.Department of stomatology, Shanghai Xin Hua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China
TANG Guoyao, E-mail: tanggy@shsmu.edu.cn.
SHI Linjun, E-mail: drshilinjun@126.com

Received date: 2024-05-03

  Accepted date: 2024-07-30

  Online published: 2024-09-28

Supported by

National Natural Science Foundation of China(82170952);National Key Research and Development Program(2022YFC2402900);“Two-Hundred Talents” Program of Shanghai Jiao Tong University School of Medicine(20221813);Clinical Research Program of Shanghai Municipal Health Commission(20214Y0192)

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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.

Key words： autofluorescence imaging; oral leukoplakia; epithelial dysplasia; ordered multinomial Logistic regression model; confusion matrix

Cite this article

Chenxi LI , Zirui WANG , Tianhao JIN , Zengtong ZHOU , Guoyao TANG , Linjun SHI . Correlation between computer-assisted quantitative autofluorescence imaging results and the pathological grading of oral epithelial dysplasia in oral leukoplakia[J]. Journal of Shanghai Jiao Tong University (Medical Science), 2024 , 44(9) : 1146 -1154 . DOI: 10.3969/j.issn.1674-8115.2024.09.009

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