Histopathologic Predictors for Locoregional Recurrence in Patients With Oral Squamous Cell Carcinoma – A Single-Center Retrospective Study
Bibliographic record
Abstract
BACKGROUND: Oral squamous cell carcinoma (OSCC) is known for its aggressive behavior and the high potential for locoregional recurrence (LRR), contributing to poor prognostic outcomes. The aim of this study was to investigate the role of histologic parameters in predicting LRR in patients with OSCC. MATERIALS AND METHODS: A retrospective analysis was performed on 58 OSCC patients treated between January 2018 and December 2022. Data were collected from medical records, focusing on demographics, clinicopathologic features, and treatment details. Different histopathologic factors such as depth of invasion, tumor stage (T), pathologic node stage (N), histologic grade of differentiation, perineural invasion, lymphovascular invasion, extranodal extension (ENE), and margin of resection were correlated with LRR. RESULTS: Out of 58 patients, 20 (34.4%) reported LRR within the first year of follow-up. In the recurrence group, 14 patients succumbed to death within 24 months. Among all the histopathologic parameters, our study found a statistically significant correlation between higher pathologic node stage, presence of ENE, and closest margin of resection (≤5 mm) with LRR. CONCLUSION: Higher pathologic node stage, presence of ENE, and closest margin of resection (≤5 mm) were the histopathologic factors associated with LRR, and can serve as deciding prognostic factors. Treatment intensification in early-stage disease with higher pathologic nodal stage, presence of ENE, and closest margin of resection (≤5 mm) may improve survival outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".