Expression of invadopodia markers can identify oral lesions with a high risk of malignant transformation
Bibliographic record
Abstract
Oral squamous cell carcinoma (OSCC) is the most common malignant tumor of the oral cavity and is usually preceded by a range of premalignant tissue abnormalities termed oral potentially malignant disorders. Identifying malignant transformation is critical for early treatment and consequently improved survival and decreased morbidity. Invadopodia (INV) are specialized subcellular structures required for cancer cell invasion. We developed a new method to visualize INV in keratinocytes using fluorescent immunohistochemistry (FIHC) and semi-automated images analysis. The presence of INV was used to determine the risk of malignant transformation. We analyzed 145 formalin-fixed, paraffin-embedded (FFPE) oral biopsy samples from 95 patients diagnosed as nondysplastic, dysplastic, and OSCC including 49 patients whose lesions transformed to OSCC (progressing) and 46 cases that did not transform to OSCC (control). All samples were stained for Cortactin, tyrosine kinase substrate with five SH3 domains (Tks5) and matrix metallopeptidase 14 (MMP14) using FIHC, imaged using confocal microscopy and analyzed using a multichannel colocalization analysis. The areas of colocalization were used to generate an INV score. Using the INV score, we were able to identify progressing lesions with a sensitivity of 75-100% and specificity of 72-76%. A positive INV score was associated with increased risk of progression to OSCC. Our results suggest that INV markers can be used in conjunction with the current diagnostic standard for early detection of OSCC.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".