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Multidimensional Artificial Intelligence-Based Cancer Progression Prediction in Oral Leukoplakia.

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  • 1Division of Applied Oral Sciences and Community Dental Care, Faculty of Dentistry, University of Hong Kong, Hong Kong SAR, China.

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|October 18, 2025
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Summary

A new AI model, OralCancerPredict, accurately predicts oral cancer risk in patients with oral potentially malignant disorders. This tool integrates patient data, histology, and biomarkers to improve cancer progression assessment and clinical management.

Keywords:
clinical decision-makinghyperplasiamachine learningoral cancerprecancerous conditionssurvival analysis

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Area of Science:

  • Oncology
  • Artificial Intelligence
  • Biomarkers

Background:

  • Oral cancer frequently arises from oral potentially malignant disorders (OPMDs).
  • Oral leukoplakia (OL) is the most common OPMD, but cancer risk prediction remains challenging.
  • Current risk stratification methods for OL have limitations.

Purpose of the Study:

  • To develop and validate an artificial intelligence (AI)-based time-to-event model, OralCancerPredict, for precise cancer progression prediction in OL.
  • To integrate diverse data types including patient characteristics, histologic features, and immunohistochemical indicators.

Main Methods:

  • Retrospective analysis of 456 patient data and 1,312 tissue samples from OL patients.
  • Quantitative analysis of KRT13 and p53 immunohistochemistry combined with clinical and histopathologic data.
  • Model training, testing, and external validation using independent datasets (119 patients, 322 tissue samples).

Main Results:

  • OralCancerPredict demonstrated strong performance with high concordance indices (0.855-0.867) and AUCs (0.877-0.882).
  • Explainability analysis highlighted the importance of KRT13/p53 expression and WHO dysplasia grading.
  • The model showed superior net benefit compared to existing grading systems.

Conclusions:

  • OralCancerPredict accurately predicts cancer risk and progression-free survival in OL patients.
  • The explainable AI model offers improved risk stratification and clinical management potential.
  • This tool can aid in streamlining interventions and close monitoring for OL patients.