Abstract CN06-04: Optical coherence tomography imaging of preneoplastic lung lesion
Bibliographic record
Abstract
Abstract Although autofluorescence bronchoscopy improves the detection rate of high grade dysplasia and carcinoma in-situ compares to white-light examination, the natural history of preneoplastic bronchial lesions is still poorly understood because the biopsy procedure for histological confirmation can mechanically remove these small lesions. The lack of accurate information regarding the spontaneous progression and regression rates of preneoplastic lesions make if difficult to assess the effect of chemoprevention agents. It is therefore important to develop non-biopsy methods that can characterize preneoplastic lesions in the bronchial epithelium. Optical coherence tomography (OCT) is an optical imaging method that can visualize cellular and extra-cellular structures at and below tissue surface. In principle, it is similar to B-type ultrasound. Instead of using sound waves, a low coherence near infra-red light such as from a broadband 1300 nm superluminescent diode source is passed into the tissue. By detecting the reflected light as it interacts with tissue structures as a function of depth, a cross sectional image is created through optical interferometry. Its contrast is the backscattered light from interfaces at different depths in the tissue, due to the heterogeneity of optical refractive indices from different tissue compositions and densities. Changes in the extracellular matrix can be readily seen due to the strong back-scattering properties of collagen and elastin. OCT provides cross-sectional tomographic images about anatomic layered structures of tissues with a large field-of-view, a spatial resolution of 3 to 16 microns and a depth penetration of 2 – 3 mm to provide near-histological images. The imaging procedure is performed using fiber optic probes that can be miniaturized to enable imaging of airways down to the terminal bronchiole. Unlike ultrasound, light waves do not require liquid based coupling medium and thus are more compatible with airway imaging. There are no associated risks from the weak near infrared light. OCT has distinct advantages over CT and MRI for imaging small airways in-vivo in that it has superior resolution approaching near-microscopic resolution and requires no ionizing radiation. It has advantages over confocal microscopy in that it can penetrate tissue three times deeper, does not require contact with the tissue surface and is less susceptible to motion artifacts due to real-time image acquisition. In addition to morphometric information, functional OCT, such as Doppler OCT, can also provide quantitative information on blood flow of blood vessels and micro-vasculature. OCT imaging has been applied to study bronchial and lung tissues and has been shown to be promising in the detection and characterization of preneoplastic lesions tumor in central and peripheral airways. Citation Information: Cancer Prev Res 2010;3(1 Suppl):CN06-04.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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 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".