Predictive Biomarkers of Oral Cancer Development and Progression
A special issue of Journal of Personalized Medicine (ISSN 2075-4426). This special issue belongs to the section "Disease Biomarker".
Deadline for manuscript submissions: closed (10 January 2024) | Viewed by 169
Special Issue Editors
Interests: oral lesions; precision medicine; oral medicine; oral and maxillofacial pathology; vascular biology
Special Issues, Collections and Topics in MDPI journals
Interests: biomaterials, implant; oral medicine; periodontics; surgery
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Using biomarkers to predict the risk of progression of oral premalignant disorders to cancer would aid in selecting treatment options and improve patient outcomes. However, despite well-characterized clinical and molecular biomarkers of oral cancer, their predictive and prognostic value has proven elusive. Artificial intelligence (AI) and machine learning (ML) employ specific algorithms and neural networks to predict early disease onset and progression. When combined with single-cell RNA sequencing and AI-assisted digital image analysis, AI-based technologies can improve disease diagnosis and treatment efficacy. In addition, AI-assisted methods and technologies would be most beneficial in evaluating high-risk populations in low-resource communities. This Special Issue will focus on the role of biomarkers in predicting the risk of developing oral squamous cell carcinoma and salivary gland cancer. Original research articles and concise reviews that focus on applying clinical, cellular, and molecular biomarkers that have predictive value, as well as the application of AI and ML in predicting the early onset and diagnosis of oral cancer in individuals and high-risk populations, will be considered for publication.
Prof. Dr. Peter Polverini
Dr. Felipe Nör
Guest Editors
Manuscript Submission Information
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Keywords
- predictive biomarkers
- risk assessment
- preneoplasia
- salivary gland cancer
- artificial intelligence
- single-cell RNA Seq
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