Endoscopic optical coherence tomography (OCT) and autofluorescence imaging (AFI) of ex vivo fallopian tubes
Bibliographic record
Abstract
Ovarian cancer is one of the most lethal gynecological conditions in the developed world. Current screening methods have only made marginal differences in overall survival over the past 30 years. The deficit of early-stage detection methods is a critical factor in the mortality associated with this disease. Recent evidence has shown that the fallopian tubes are a critical site in carcinogenesis of ovarian cancers. We present the first endoscopic co-registered OCT-AFI imaging of ex vivo fallopian tubes. This work aims to evaluate the potential of OCT-AFI to identify pre-cancerous lesions in the fallopian tubes. The BC Cancer Research Centre’s Optical Imaging Lab has developed a multimodal imaging system and catheter which enables both optical coherence tomography (OCT) and autofluorescence imaging (AFI). The imaging probe consists of a dual-clad fiber optical core inside a 0.9mm diameter sterile sheath. This system allows for resolutions of 20-30μm and imaging depths of up to 1.5mm. Samples are collected from patients consented through the OVCARE Gynecological Cancer Tissue Bank banking protocol. Volumetric OCT-AFI images are acquired for the entire catheterizable length of the sample at pullback speeds of 1mm/s. After imaging, histology is conducted according to the “sectioning and extensively examining the fimbriated end” protocol to serve as a gold standard. We present methods for obtaining scans of the ex vivo fallopian tubes, sample cases correlated with histology, and our preliminary results. As of January 2020, we have imaged 21 patients and 27 fallopian tubes including 6 cancerous specimens.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".