Abstract
Background: Histopathological risk assessment models in oral squamous cell carcinoma (OSCC) have had varying degrees of success in stratifying patients, with models developed on retrospective cohorts being able to predict recurrence and metastasis. In spite of mounting evidence on their impact on survival outcomes, the incorporation of histopathological features, including perineural invasion (PNI) and the worst pattern of invasion, in staging and risk stratification is still lacking. Thus, our study aimed to assess and characterise histological prognostic indicators in OSCC, elucidate their significance in relation to patient prognosis, and develop a risk assessment model by correlating these indicators with survival parameters and risk stratification. Methods: A retrospective chart audit was performed, and histologic parameters were staged according to the American Joint Committee on Cancer’s 8th Edition Cancer Staging Manual and objectively scored based on the College of American Pathologists’ (CAP) Protocol 2018. Pearson’s chi-square test and Kaplan–Meier survival analysis were carried out to identify the significant correlations between specific histopathological indicators and patient prognosis. Results: Higher grades of pattern of invasion were significantly associated with greater PNI and depth of invasion (p < 0.05). Depth of invasion was also significantly associated with extranodal extension (ENE) (p < 0.05). Survival analysis showed a clear trend with higher grades of the worst pattern of invasion and depth of invasion, as well as PNI, as possible independent predictors of poorer survival outcomes. Conclusion: The development of a risk stratification model based on these indicators provides a valuable tool for enhancing patient outcomes. Incorporation of detailed histopathological analysis into routine clinical decision-making improves survival rates and treatment efficacy in OSCC patients.
| Original language | English |
|---|---|
| Article number | 8821799 |
| Journal | International Journal of Dentistry |
| Volume | 2026 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 2026 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
All Science Journal Classification (ASJC) codes
- General Dentistry
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