Doppler optical coherence tomography and co-registered autofluorescence imaging of peripheral lung cancer
Bibliographic record
Abstract
Diagnosis of peripheral lung nodules is challenging because they are rarely visualized endobronchially. Imaging techniques such as endobronchial ultrasound (EBUS) are employed to improve tumor localization. The current EBUS probe provides limited nodule characterization and has an outer diameter of 1.4 mm that restricts access to small peripheral airways. We report a novel co-registered autofluoresence Doppler optical coherence tomography (AF/DOCT) system with a 0.9 mm diameter probe to characterize peripheral lung nodules prior to biopsy in vivo. Method: Patients referred for evaluation of peripheral lung nodules underwent bronchoscopy with examination of standard EBUS and the novel AF/DOCT system. The lesion of interest was first identified with EBUS and then imaged with the AF/DOCT system. The abnormal area was biopsied. AF/DOCT images of pathology proved lung malignancies were reviewed by a panel of a pathologist, respirologists, and AF/DOCT experts. Results: Eleven patients with biopsy proven lung cancer underwent examination with AF/DOCT. The majority of the cancers were adenocarcinoma. AF/DOCT images were obtained in all patients. There were no complications to the procedures. Lung abnormalities visualized in AF/ OCT images were observed in 11 cases. In one case large blood vessels were identified and biopsy was avoided. Conclusion: In this pilot study, AF/DOCT obtained high quality images of peripheral pulmonary nodules. The present study supports the safety and feasibility of AF/DOCT for the evaluation of lung cancer. The addition of Doppler information may improve biopsy site selection and reduce hemorrhage.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".