Wide-field optical coherence tomography for microstructural analysis of key tissue types: a proof-of-concept evaluation
Bibliographic record
Abstract
Introduction: The presence of positive margins following tumor resection is a frequent cause of re-excision surgery. Nondestructive, real-time intraoperative histopathological imaging methods may improve margin status assessment at the time of surgery; optical coherence tomography (OCT) has been identified as a potential solution but has not been tested with the most common tissue types in surgical oncology using a single, standardized platform. Methods: This was a proof-of-concept evaluation of a novel device that employs wide-field OCT (WF-OCT; OTIS 2.0 System) to image tissue specimens. Various cadaveric tissues were obtained from a single autopsy and were imaged with WF-OCT then processed for permanent histology. The quality and resolution of the WF-OCT images were evaluated and compared to histology and with images in previous literature. Results: A total of 30 specimens were collected and tissue-specific microarchitecture consistent with previous literature were identified on both WF-OCT images and histology slides for all specimens, and corresponding sections were correlated. Application of vacuum pressure during scanning did not affect specimen integrity. On average, specimens were scanned at a speed of 10.3 s/cm 2 with approximately three features observed per tissue type. Conclusion: The WF-OCT images captured in this study displayed the key features of the most common human tissue types encountered in surgical oncology with utility comparable to histology, confirming the utility of an FDA-cleared imaging platform. With further study, WF-OCT may have the potential to bridge the gap between the immediate information needs of the operating room and the longer timeline inherent to histology workflow.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".