Abstract 2307: Clinicopathological analysis of oral lichenoid mucositis with dysplasia
Bibliographic record
Abstract
Abstract Introduction: Lichenoid mucositis (LM) is characterized histologically by a band of lymphocytic infiltrate at the epithelial-stromal junction. The malignant potential of LM with dysplasia (LMD) has long been debated, as heavy inflammation may induce epithelial atypia resembling dysplasia. Some propose that there are two subtypes of LMD, with differing malignant risks. However, this hypothesis has not been tested. Objectives: To apply a clinicopathological algorithm to determine whether there are subtypes within LMD, one with primary lichenoid and secondary dysplastic features (L1D2), and the other with primary dysplastic and secondary lichenoid features (D1L2), and to compare the proportion of malignant progression in these groups. Methods: Patients were selected from the Oral Cancer Prediction Longitudinal (OCPL) study, a prospective cohort study that has generated a database of demographic, clinicopathologic, and outcome data (defined as progression to severe dysplasia, carcinoma in situ, or squamous cell carcinoma). Patients with LMD, no history of head and neck cancer, at least 5 years of follow-up and with accessible tissue biospecimens will be selected. The clinicopathological scenario for L1D2 is: a patient with no risk habit use presenting with a bilateral, reticular lesion histologically exhibiting basal cell (BC)/basement membrane (BM) degeneration and a subepithelial lymphocytic band. In a D1L2 scenario, patients may present with a bilateral or unilateral lesion clinically resembling oral lichen planus or leukoplakia and histologically exhibiting a diffuse band of mixed inflammation and an intact BC/BM. Risk habit use may or may not be present. Immunohistochemistry was performed to assess for BM degeneration using collagen IV. Inflammatory cells were visualized through hematoxylin and eosin staining. Clinical features were assessed through examination of intraoral photos and medical records. Inferential statistical analysis was performed using Chi-Square test, Fisher's Exact test, and logistic regression. Results: 43 cases meet the inclusion criteria (10 progressors and 33 non-progressors). Based on a previously established algorithm, only 14 cases could be sorted into a subtype according to clinicopathology. In the L1D2 group (n=4), 1 progressed, and in the D1L2 group (n=10), 3 progressed. The proportion of progression did not differ significantly between groups (P=1.000). Although L1D2 cases were less likely to progress, this did not reach statistical significance (OR=0.778; 95% CI 0.056 - 10.861; P=0.852). Conclusion: This pilot study does not support the existence of two LMD subtypes with differing malignant risks, however, a firm conclusion can not be made due to the small sample size. In future, larger studies incorporating molecular analyses may aid in the subtyping of LMD and provide greater insight to its potential for malignant progression. Citation Format: Iris LIN, Denise M. Laronde, Ilena Yim, Lewei Zhang, Miriam P. Rosin, Leigha D. Rock. Clinicopathological analysis of oral lichenoid mucositis with dysplasia [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2307.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".