Preoperative assessment of CD44‐mediated depth of invasion as predictor of occult metastases in early oral squamous cell carcinoma
Bibliographic record
Abstract
BACKGROUND: Epithelial-mesenchymal transition and cancer stem-like cells (CSC) have been linked to increased metastatic potential. We evaluated the prognostic impact of CD44, a CSC biomarker, on depth of invasion (DOI) and outcome in oral squamous cell carcinoma (OSCC). METHODS: Using a multivariable logistic regression model, we evaluated in early OSCCs the relationship between CD44 expression at the invasive tumor front, DOI, sentinel lymph node biopsy, extension of nodal involvement, and survival. We also assessed whether CT and/or MRI could predict DOI preoperatively. RESULTS: CD44 expression was associated with increased DOI (P = .018), worse disease-specific survival (P = .041) but not with positive sentinel lymph node biopsy (P > .05). Each millimeter increase in DOI was associated with a 31.1% higher risk for positive sentinel lymph node biopsy (95% CI: 5.8%-62.4%, P = .013) and with higher metastatic ratio (P = .015). Preoperative estimation of DOI by CT and/or MRI and histopathological DOI showed a strong correlation (P < .0001). CONCLUSIONS: CD44 expression correlates with DOI, which predicts occult lymph node metastasis. Preoperative CT and/or MRI provides an accurate estimation of histopathological DOI. Both pieces of information gained preoperatively can help surgeons tailor their operation in regard to the surgical management of the neck.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".