Oral oncology · 2026
Artificial intelligence model predicts malignant transformation of oral leukoplakia and optimizes interventions.
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Villa Alessandro, Peterson Douglas E, Lingen Mark W, Pearson Alexander, Gona Saideep, Morlandt Anthony
Аннотация
PURPOSE: Oral leukoplakia (OL), the most common oral potentially malignant disorder (OPMD), poses a significant risk for transformation to oral squamous cell carcinoma (OSCC). Despite its prevalence and variable malignant transformation rates, optimal management remains controversial due to limited evidence on causation, risk stratification, and treatment efficacy. This commentary addresses these unresolved challenges and delineates opportunities for development of precision prognostic technologies in context of future clinical trials.
MATERIALS AND METHODS: We reviewed current OL literature, focusing on epidemiology, molecular biomarkers, and management strategies. Data were synthesized from systematic reviews, cohort studies, and clinical trials (2020-2025), assessing malignant transformation rates, genomic and immune biomarkers (e.g., LOH, TP53, PD-L1), and emerging artificial intelligence (AI) applications.
RESULTS: OL malignant transformation rates vary by subtype: localized OL (4-40%) and proliferative leukoplakia (65-100%). Non-dysplastic lesions transform at an approximately 5% rate, while dysplastic lesions carry a 12-18% 5-year transformation risk. Genomic alterations (e.g., 9p21 LOH, TP53 mutations) and immune markers (e.g., PD-L1) show prognostic promise but lack clinical validation. Despite surgical excision, OL can exhibit a high recurrence rate at the site of surgery (e.g., 30%), and non-surgical therapies (e.g., immunotherapy) are currently under investigation. AI-driven models integrating multi-omic data offer potential for personalized risk prediction but require standardized validation.
CONCLUSION: OL management is hindered by heterogeneous transformation risks, limited high-quality studies, and reliance on histopathology. Multidisciplinary care, AI-enhanced risk stratification, and large-scale randomized controlled trials have now become essential in order to refine surveillance, optimize interventions, and reduce OSCC burden. Until then, the treat-or-observe dilemma persists, underscoring the need for interprofessional collaboration at the international level.
MATERIALS AND METHODS: We reviewed current OL literature, focusing on epidemiology, molecular biomarkers, and management strategies. Data were synthesized from systematic reviews, cohort studies, and clinical trials (2020-2025), assessing malignant transformation rates, genomic and immune biomarkers (e.g., LOH, TP53, PD-L1), and emerging artificial intelligence (AI) applications.
RESULTS: OL malignant transformation rates vary by subtype: localized OL (4-40%) and proliferative leukoplakia (65-100%). Non-dysplastic lesions transform at an approximately 5% rate, while dysplastic lesions carry a 12-18% 5-year transformation risk. Genomic alterations (e.g., 9p21 LOH, TP53 mutations) and immune markers (e.g., PD-L1) show prognostic promise but lack clinical validation. Despite surgical excision, OL can exhibit a high recurrence rate at the site of surgery (e.g., 30%), and non-surgical therapies (e.g., immunotherapy) are currently under investigation. AI-driven models integrating multi-omic data offer potential for personalized risk prediction but require standardized validation.
CONCLUSION: OL management is hindered by heterogeneous transformation risks, limited high-quality studies, and reliance on histopathology. Multidisciplinary care, AI-enhanced risk stratification, and large-scale randomized controlled trials have now become essential in order to refine surveillance, optimize interventions, and reduce OSCC burden. Until then, the treat-or-observe dilemma persists, underscoring the need for interprofessional collaboration at the international level.