Multidisciplinary Approaches to Oral Cancer Diagnosis
A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Pathology and Molecular Diagnostics".
Deadline for manuscript submissions: 31 August 2026 | Viewed by 25
Special Issue Editor
Special Issue Information
Dear Colleagues,
The diagnosis of oral cancer necessitates a comprehensive, multidisciplinary approach that integrates clinical, radiological, histopathological, molecular, and technological expertise to ensure accurate detection, staging, and treatment planning. Given the complex nature of oral malignancies, collaboration among dental specialists, oral surgeons, radiologists, pathologists, oncologists, and molecular biologists is essential for achieving optimal patient outcomes.
Clinically, early detection begins with thorough oral examinations, highlighting the importance of visual and tactile assessments to identify suspicious lesions. Adjunctive diagnostic tools such as toluidine blue staining, autofluorescence, and brush biopsies can enhance a clinician’s ability to detect potentially malignant disorders. However, definitive diagnosis relies heavily on histopathological analysis, which provides detailed insights into cellular atypia, invasion, and differentiation.
Radiological imaging plays a critical role in local and regional staging. Techniques such as computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) enable detailed assessment of tumour extent, lymph node involvement, and distant metastasis. Advances in imaging modalities facilitate precise surgical planning and monitoring of treatment response.
Molecular and genetic analyses have emerged as vital components in understanding tumour biology and predicting prognosis. Techniques such as gene expression profiling, biomarker identification, and next-generation sequencing aid in stratifying patients, identifying actionable targets, and tailoring personalized therapies. These molecular insights are increasingly integrated into diagnostic algorithms, supporting precision medicine.
The advent of artificial intelligence (AI) and machine learning has further enhanced diagnostic capabilities by enabling analysis of large datasets, identification of subtle patterns, and improvements in accuracy in image interpretation and risk stratification. Combining AI with traditional diagnostic modalities fosters a more holistic and precise approach.
Overall, the integration of clinical examination, imaging, histopathology, molecular biology, and emerging technologies exemplifies the multidisciplinary paradigm essential for early detection, accurate staging, and effective management of oral cancer. This collaborative approach not only improves diagnostic precision but also paves the way for personalised treatment strategies, ultimately enhancing patient outcomes and survival rates.
We look forward to receiving innovative contributions that will advance the field of oral cancer diagnosis and pathology through cutting-edge AI solutions integrated with other, more established diagnostic methods.
Dr. Siu Wai Choi
Guest Editor
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Keywords
- oral cancer diagnosis
- oral cancer progression
- machine learning
- prediction models
- biomarkers
- early prediction
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