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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月10日

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  • JingWen Li,
  • YaFang Zhou,
  • MengJing Zhang,
  • John Adeoye,
  • Jane JingYa Pu,
  • MiMi Zhou,
  • ChuanXia Liu,
  • LiJie Fan,
  • Colman McGrath,
  • Dian Zhang,
  • LiWu Zheng

摘要

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.