Optical Coherence Tomography of Peri-Ocular Skin Cancers: An Optical Biopsy
Bibliographic record
Abstract
INTRODUCTION: Optical coherence tomography (OCT) imaging has been used as a diagnostic tool for retinal disease for several years, and OCT apparatuses are becoming increasingly powerful. However, OCT has yet to reach its full potential in ophthalmology clinics. Alike retinal layers, it has been shown that OCT is able to generate cross-sectional images of the skin and allows visualization of skin lesions in a histopathology-like manner. OBJECTIVE: We aim to validate OCT as an imaging modality for peri-ocular skin cancer. Through a series of cases, we highlight findings for 3 common eyelid malignancies: basal cell carcinoma, squamous cell carcinoma and sebaceous carcinoma. We propose an OCT image-based signature for basal cell carcinoma. METHODS: This is a prospective study. Fifty-eight lesions suspicious of malignancy from 57 patients were subjected to OCT imaging prior to the surgical excision of the lesion. OCT images were analysed and scored according to previously identified OCT features. Eight representative examples are presented, highlighting the OCT patterns for each malignancy side by side to its corresponding histopathological sections. RESULTS: Out of the 58 lesions analysed, 53 were malignant. A loss of the dermal-epidermal junction is observed in all malignant lesions. A strong link is observed between the presence of subepithelial hyporeflective nests on OCT and the diagnosis of basal cell carcinoma (present in 83% of cases). Conversely, lesions of epithelial origin such as squamous cell carcinoma are most often represented on OCT by acanthosis. Two supplementary cases, one basal cell carcinoma and one sebaceous carcinoma, are provided to illustrate how OCT imaging is a valuable tool in cases where clinical observations may be unusual. CONCLUSIONS: We provide evidence supporting the use of OCT for the evaluation of peri-ocular cancers. OCT enables visualization of the skin layers in vivo, before biopsy. Our results show that certain OCT features can contribute to include or exclude a diagnosis of basal cell carcinoma. By integrating this non-invasive imaging methodology into the routine assessment of peri-ocular skin lesions, especially in health care centres where access to specialists is limited, OCT imaging can increase clinical precision, reduce delays in patient referral and enhance patient care.
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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 | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".