Next-generation AI framework for comprehensive oral leukoplakia evaluation and management.

Journal: NPJ digital medicine
Published Date: Aug 10, 2025

Abstract

Oral potentially malignant disorder poses a significant risk of malignant transformation, particularly in cases with epithelial dysplasia (OED). Current OED assessment methods are invasive and lack reliable decision-support tools for cancer risk evaluation and follow-up optimization. This study developed and validated OMMT-PredNet, a fully automated multimodal deep learning framework requiring no manual ROI annotation, for non-invasive OED identification and time-dependent cancer risk prediction. Utilizing data from 649 histopathologically confirmed leukoplakia cases across multiple institutions (2003-2024), including 598 cases in the primary cohort and 51 in the external validation set, the model integrated paired high-resolution clinical images and medical records. OMMT-PredNet achieved an AUC of 0.9592 (95% CI: 0.9491-0.9693) for cancer risk prediction and 0.9219 (95% CI: 0.9088-0.9349) for OED identification, with high specificity (MT: 0.9490; OED: 0.9182) and precision (MT: 0.9442; OED: 0.9303). Calibration and decision curve analyses confirmed clinical applicability, while external validation demonstrated robustness. This multidimensional model effectively predicts OED and cancer risk, highlighting its global applicability in enhancing oral cancer screening and improving patient outcomes.

Authors

  • Jingwen Li
    Cloud Computing and Big Data Research Institute, China Academy of Information and Communications Technology, People's Republic of China.
  • Yafang Zhou
    College of Computer Science, Xiangtan University, Xiangtan, China.
  • MengJing Zhang
    Department of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China.
  • John Adeoye
    Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong Special Administrative Region.
  • Jane JingYa Pu
    Division of Oral & Maxillofacial Surgery, Faculty of Dentistry, University of Hong Kong, Hong Kong SAR, China.
  • Mimi Zhou
    Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Zhejiang Provincial Clinical Research Center for Oral Diseases, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Hangzhou, China.
  • Chuanxia Liu
    Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Zhejiang Provincial Clinical Research Center for Oral Diseases, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Hangzhou, China.
  • LiJie Fan
    Stomatology Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
  • Colman McGrath
    Faculty of Dentistry, The University of Hong Kong, Hong Kong Special Administrative Region, Hong Kong, People's Republic of China.
  • Dian Zhang
    School of Computer Science, Northwestern Polytechnical University, Xi'An, 710129, ShaanXi, China. Electronic address: dianzhang@mail.nwpu.edu.cn.
  • LiWu Zheng
    Division of Oral & Maxillofacial Surgery, Faculty of Dentistry, University of Hong Kong, Hong Kong SAR, China. lwzheng@hku.hk.

Keywords

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