The utility of optical coherence tomography for diagnosis of basal cell carcinoma: a quantitative review
Bibliographic record
Abstract
BACKGROUND: Optical coherence tomography (OCT) is a noninvasive near-infrared light imaging technology that can be utilized to diagnose basal cell carcinomas (BCCs) based on specific morphological features. OBJECTIVES: To conduct a quantitative review using tumour-level data from published studies to assess: (i) the in vivo diagnostic accuracy of different OCT systems; (ii) correlation between OCT features and histopathological diagnosis; and (iii) factors that impact the accuracy of tumour depth estimation. METHODS: Primary tumour-level data were extracted from published studies on the use of time-domain (TD-OCT), frequency-domain (FD-OCT) and high-definition (HD-OCT) systems for diagnosis of BCCs. Quality assessment was performed using the Newcastle-Ottawa Scale and the Cochrane Risk of Bias Tool. Sensitivity and specificity for diagnosis of BCC, prevalence of morphological features and correlation of tumour depth between OCT and histopathology were analysed. RESULTS: In total, 901 BCCs from 31 studies were included. The sensitivity and specificity were 89·3% and 60·3% overall, and were highest for FD-OCT (93·7% and 61·4%, respectively). The most prevalent morphological features were lobular pattern (80·2%, 315 of 393 tumours) and hyper-reflective peritumoral stroma (51·7%, 203 of 393). Concordance between OCT and histopathological tumour depth categories was moderate (Pearson coefficient 0·48); it was highest for tumours < 1 mm and those on the extremities. The overall bias was 0·075 mm with an agreement range from -0·88 to 1·03 mm. HD-OCT and FD-OCT were superior to TD-OCT at identifying morphological features, but not at tumour depth estimation. CONCLUSIONS: OCT is a viable tool for in vivo diagnosis of BCCs. FD-OCT and HD-OCT outperformed TD-OCT in diagnostic accuracy and detection of morphological features, but not tumour depth estimation.
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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 | 0.011 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.019 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".