Ocular surface squamous cell neoplasia: risk factors for aggressive growth behaviour and the role of Ki-67
Bibliographic record
Abstract
OBJECTIVE: Analyzing characteristics of ocular surface squamous cell neoplasia (OSSN) at first diagnosis and potential risk factors for aggressive growth behaviour. DESIGN: Retrospective. METHODS: Including patients with first diagnosis of OSSN at a tertiary center from 2013 until 2022. Cases were analyzed regarding demographics, clinical findings, and histopathological findings, including Ki-67 expression. RESULTS: A total of 153 patients with first diagnosis of histopathological confirmed OSSN were included. Mean age was 72 years (36-98), with a slight male predominance (66%; n = 101). Most patients had invasive squamous cell carcinoma (SCC; 45.8%, 70), followed by carcinoma in situ (CIS; 37.9%, 58) and epithelial dysplasia (ED; 16.3%, 25). Duration of symptoms varied significantly: ED 6 months (0-36), CIS 1.5 (0-48), SCC 3 (0-36) (p = 0.048). 44.3% (51/115) of cases were previously misdiagnosed, and, therefore, inadequately treated. Orbital involvement was observed in 8.5% (13), intraocular in 1.3% (2), metastasis in 2.7% (4) at initial diagnosis. Ki-67 labeling index (LI) varied significantly across subtypes: ED 35% (2-87%), CIS 45% (11-85%), SCC 50% (18-93%) (p = 0.007) and was higher with involvement of the caruncle, lower fornix, lower eyelid margin, or tarsus (p = 0.023). Patients with globe or orbit invasion had significantly longer median symptom duration (6 months (0-48) vs 2 (0-48); p = 0.01). Patients with metastasis exhibited significantly higher Ki-67 LI (p = 0.027). CONCLUSIONS: Our study found extended time intervals from first symptoms to first correct diagnosis correlate with higher risk for advanced SCC. Further, elevated Ki-67 LI correlated with more invasive tumor entities, such as SCC and CIS, and indicate an increased risk of metastasis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".