Development of a pathomics-based model for the prediction of malignant transformation in oral leukoplakia.

Researchers

Journal

Modalities

Models

Abstract

Accurate prognostic stratification of oral leukoplakia (OLK) with risk of malignant transformation into oral squamous cell carcinoma is crucial. We developed an objective and powerful pathomics-based model for the prediction of malignant transformation in OLK using hematoxylin and eosin staining images. In total, 759 hematoxylin and eosin staining images from multi-center cohorts were included. A training set (n = 489), validation set (n = 196), and testing set (n = 74) were used for model development. Four deep learning methods were used to train and validate the model constructed using H&E staining images. Pathomics features generated through deep learning combined with machine learning algorithms were used to develop a pathomics-based model. Immunohistochemical staining of Ki67, p53, and PD-L1 was used to interpret the ‘black-box’ of the model. Pathomics-based models predicted the malignant transformation of OLK (validation set AUC = 0.899, testing set AUC = 0.813) and significantly identified high risk and low risk populations. The prediction performance of malignant transformation from dysplasia grading (validation set AUC = 0.743) was lower than that of the pathomics-based model. The expressions of Ki67, p53, and PD-L1 were correlated with various pathomics features. The pathomics-based model accurately predicted the malignant transformation of OLK and may be useful for the objective and rapid assessment of the prognosis of OLK patients.Copyright © 2023. Published by Elsevier Inc.