Oral Squamous Cell Carcinoma Associated with Precursor Lesions
Bibliographic record
Abstract
Abstract Oral squamous cell carcinoma (OSCC) may be associated with precursor lesions known as oral potentially malignant disorders (OPMD). Few studies have reported on how OPMD diagnosis affects early detection and outcome of OSCC. We reviewed a large series of OSCC to determine the proportion that was associated with preceding OPMD and to compare the outcome of OSCC with or without precursor. Cases of oral–oropharyngeal carcinoma diagnosed between 2005 and 2015 were retrieved from the Ontario Cancer Registry (OCR) and matched to records of OPMD between 2001 and 2015 in two large oral pathology diagnostic services and the pathology databases of two hospitals with oral pathology services, to identify cases with precursor. Of 10,987 cancer cases, 378 (3.44%) had a preceding OPMD. Patients living in Central Ontario were more likely to have OPMD diagnosed before carcinoma than those in North Ontario (4.73% vs. 1.63%, P = 0.05). 329 of 5,257 cases of oral cancer were linked to a precursor, compared with 24 of 4,174 cases of oropharyngeal cancer (6.26% vs. 0.57%, P < 0.0001). Oral cancers with precursor were predominantly diagnosed at stage I (49.30%), compared with those without precursor, where stage IV disease predominated (41.28%). Sixty-nine of 309 (22.33%) patients with precursor-associated oral cancer have died of disease, compared with 1,551 of 4,656 (33.31%) patients without a precursor (P = 0.02). We conclude that patients with OSCC associated with a precursor had significantly lower odds of dying from disease. The beneficial effect of precursor lesion diagnosis on outcome is related to a higher proportion of stage I disease. Prevention Relevance: OSCC causes significant morbidity and mortality, especially if diagnosed at late stages. Precursor lesions to OSCC can be recognized by clinical examination. Our study shows that early diagnosis of OSCC at the precursor stage can improve the outcome of oral cancer.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.005 | 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".