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Published on: April 22, 2019
Survival Trends Associated With Histopathological Risk Factors in Oral Squamous Cell Carcinoma.
Shyam Sthanukrishnan1, Smitha Sammith Shetty1, Chetana Chandrashekar1
1Department of Oral and Maxillofacial Pathology, Manipal College of Dental Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India, manipal.edu.
Histopathological indicators like invasion depth and perineural invasion (PNI) are key predictors of oral squamous cell carcinoma (OSCC) outcomes. A new risk model using these features can improve patient stratification and survival.
Area of Science:
- Oncology
- Pathology
- Cancer Research
Background:
- Histopathological risk models for oral squamous cell carcinoma (OSCC) have limited success in patient stratification.
- Key features like perineural invasion (PNI) and worst pattern of invasion are underutilized in current staging.
- There's a need to integrate these histological indicators for better prognosis prediction.
Purpose of the Study:
- To assess and characterize histological prognostic indicators in OSCC.
- To elucidate the significance of these indicators in relation to patient prognosis.
- To develop a risk assessment model correlating indicators with survival and risk stratification.
Main Methods:
- Retrospective chart audit of OSCC cases.
- Histologic parameters staged using AJCC 8th Edition and CAP Protocol 2018.
- Pearson's chi-square and Kaplan-Meier survival analyses were employed.
Main Results:
- Higher grades of invasion patterns correlated with increased PNI and depth of invasion (p < 0.05).
- Depth of invasion was significantly associated with extranodal extension (ENE) (p < 0.05).
- Worst pattern of invasion, depth of invasion, and PNI emerged as predictors of poorer survival.
Conclusions:
- A risk stratification model based on these histological indicators can enhance patient outcomes.
- Integrating detailed histopathological analysis improves survival rates and treatment efficacy in OSCC.
- The study highlights the importance of specific histological features for accurate OSCC risk assessment.
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