Imaging Microbubbles With Contrast-Enhanced Endobronchial Ultrasound
Bibliographic record
Abstract
OBJECTIVE: Endobronchial ultrasound (EBUS) is commonly used to guide transbronchial needle biopsies for the staging of lymph nodes in non-small cell lung cancer patients. Although contrast-enhanced ultrasound (CEUS) and microbubbles (MBs) can improve the diagnostic accuracy in tumors, the ability of contrast-enhanced EBUS (CE-EBUS) to image MBs has not yet been comprehensively evaluated. In this study, we assessed the ability of a CE-EBUS system (Olympus EU-ME2 PREMIER and BF-UC180F bronchoscope) to detect laboratory-synthesized MBs in comparison to clinical (Toshiba Aplio SSA-790A) and pre-clinical (VisualSonics Vevo 2100) CEUS systems in vitro and in vivo, respectively. METHODS: Agar flow phantoms and reference tissue were used to assess CE-EBUS MB imaging in vitro, and A549 tumor-bearing athymic nude and AE17-OVA tumor-bearing C57BL/6 mice were used to assess MB detectability and perfusion in vivo, respectively. RESULTS: Results revealed that despite the lower sensitivity of CE-EBUS to MB concentration in comparison to clinical CEUS, CE-EBUS yielded a similar contrast-to-tissue ratio (CTR) in vitro of 28.9 ± 4.5 dB for CE-EBUS, compared with 29.7 ± 2.6 dB for clinical CEUS (p < 0.05). In vivo, CE-EBUS generated a perfusion curve highly correlated with that obtained with the pre-clinical CEUS system (Pearson correlation coefficient = 0.927, p < 0.05). Moreover, CE-EBUS yielded a CTR 2.7 times higher than that obtained with the pre-clinical ultrasound system. CONCLUSION: These findings together suggest that CE-EBUS can perform contrast imaging comparable to that produced by commercial pre-clinical and clinical ultrasound systems, with potential for clinical characterization of mediastinal lymph nodes in lung cancer patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".