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

doi:10.3969/j.issn.1674-8115.2024.09.009

Abstract

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

CLC Number:

R781.5⁺9

LI Chenxi, WANG Zirui, JIN Tianhao, ZHOU Zengtong, TANG Guoyao, SHI Linjun. 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.

Figures/Tables 7

TrendMD

References 27

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Maximum depth of decision tree	Index	The proportion of the test set in all samples
Maximum depth of decision tree	Index	0.10	0.15	0.20	0.25	0.30	0.35	0.40	0.45	0.50
2	Accuracy	0.667	0.741	0.792	0.656	0.648	0.632	0.650	0.671	0.665
	Precision	0.675	0.749	0.801	0.659	0.655	0.555	0.604	0.622	0.605
	F1 score	0.669	0.744	0.795	0.547	0.534	0.522	0.556	0.586	0.578
3	Accuracy	0.639	0.685	0.750	0.589	0.648	0.680	0.629	0.603	0.609
	Precision	0.643	0.700	0.764	0.485	0.619	0.665	0.560	0.536	0.536
	F1 score	0.640	0.690	0.755	0.508	0.596	0.663	0.551	0.554	0.554
4	Accuracy	0.667	0.704	0.750	0.711	0.620	0.664	0.630	0.634	0.631
	Precision	0.667	0.713	0.764	0.707	0.579	0.649	0.572	0.552	0.544
	F1 score	0.667	0.707	0.755	0.709	0.576	0.650	0.566	0.562	0.557

Maximum depth of decision tree	Index	The proportion of the test set in all samples
Maximum depth of decision tree	Index	0.10	0.15	0.20	0.25	0.30	0.35	0.40	0.45	0.50
2	Accuracy	0.667	0.741	0.792	0.656	0.648	0.632	0.650	0.671	0.665
	Precision	0.675	0.749	0.801	0.659	0.655	0.555	0.604	0.622	0.605
	F1 score	0.669	0.744	0.795	0.547	0.534	0.522	0.556	0.586	0.578
3	Accuracy	0.639	0.685	0.750	0.589	0.648	0.680	0.629	0.603	0.609
	Precision	0.643	0.700	0.764	0.485	0.619	0.665	0.560	0.536	0.536
	F1 score	0.640	0.690	0.755	0.508	0.596	0.663	0.551	0.554	0.554
4	Accuracy	0.667	0.704	0.750	0.711	0.620	0.664	0.630	0.634	0.631
	Precision	0.667	0.713	0.764	0.707	0.579	0.649	0.572	0.552	0.544
	F1 score	0.667	0.707	0.755	0.709	0.576	0.650	0.566	0.562	0.557

Max imum depth of decision tree	Index	The proportion of the test set in all samples
Max imum depth of decision tree	Index	0.10	0.15	0.20	0.25	0.30	0.35	0.40	0.45	0.50
3	Accuracy	0.528	0.537	0.611	0.556	0.519	0.512	0.546	0.565	0.553
	Precision	0.302	0.344	0.512	0.395	0.385	0.394	0.476	0.352	0.346
	F1 score	0.384	0.419	0.556	0.425	0.424	0.414	0.421	0.433	0.423
4	Accuracy	0.611	0.574	0.514	0.522	0.537	0.536	0.552	0.571	0.559
	Precision	0.537	0.500	0.337	0.422	0.418	0.459	0.541	0.515	0.455
	F1 score	0.569	0.533	0.407	0.454	0.427	0.438	0.465	0.489	0.455
5	Accuracy	0.528	0.537	0.542	0.544	0.556	0.528	0.483	0.528	0.525
	Precision	0.414	0.386	0.340	0.443	0.454	0.550	0.468	0.525	0.465
	F1 score	0.422	0.446	0.417	0.460	0.467	0.453	0.423	0.506	0.460

Max imum depth of decision tree	Index	The proportion of the test set in all samples
Max imum depth of decision tree	Index	0.10	0.15	0.20	0.25	0.30	0.35	0.40	0.45	0.50
3	Accuracy	0.528	0.537	0.611	0.556	0.519	0.512	0.546	0.565	0.553
	Precision	0.302	0.344	0.512	0.395	0.385	0.394	0.476	0.352	0.346
	F1 score	0.384	0.419	0.556	0.425	0.424	0.414	0.421	0.433	0.423
4	Accuracy	0.611	0.574	0.514	0.522	0.537	0.536	0.552	0.571	0.559
	Precision	0.537	0.500	0.337	0.422	0.418	0.459	0.541	0.515	0.455
	F1 score	0.569	0.533	0.407	0.454	0.427	0.438	0.465	0.489	0.455
5	Accuracy	0.528	0.537	0.542	0.544	0.556	0.528	0.483	0.528	0.525
	Precision	0.414	0.386	0.340	0.443	0.454	0.550	0.468	0.525	0.465
	F1 score	0.422	0.446	0.417	0.460	0.467	0.453	0.423	0.506	0.460

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