F-085INDOCYANINE GREEN FLUORESCENCE FOR LOCALIZATION OF HUMAN LUNG CANCER: A PRELIMINARY ANIMAL STUDY
Bibliographic record
Abstract
Objectives: Recent advance in CT technology has enabled detection of small pulmonary nodules. Minimally invasive thoracic surgery is becoming the standard of care for surgical resection of such tumours. However, localization of small lung tumour is a challenge. The purpose of this study was to determine the optimal interval between indocyanine green (ICG) administration and the detection of ICG fluorescence at the tumour site, and clarify the feasibility of ICG fluorescence to localize pulmonary nodules. Methods: Subcutaneous xenograft model study: Nude mice and human non-small-cell lung cancer cell lines (A549, H460, and MGH-7) were used for this study (n = 3/cell line). 2 mg/kg of ICG was administered intravenously and fluorescence images were obtained using in-vivo fluorescence imager. The tumour to background ratio (TBR) was calculated. Orthotopic lung cancer model study: Detection of i.v. administered ICG fluorescence in lung cancer was examined in human orthotopic lung cancer xenograft model. Eleven mice and H460 cells were used for this study. Based on the results of the previous study, potential optimal intervals were determined, followed by ICG fluorescence imaging of lung cancer. Results: The peak of TBRs was at 24-72 h after ICG administration regardless cancer cell types. The subcutaneous tumour to normal lung ratio in A549 mice was much higher compared to those in H460 and MGH-7 mice (P < 0.001). 9h, 48 h and 120 h were selected as the potential optimal intervals. The lung cancer to normal lung ratio was much higher at 48 h after ICG administration compared to those at 9 h and 120 h after ICG injection (P < 0.0001). Conclusions: ICG fluorescence exhibited the peak TBR at 48 h after systemic administration. Further study using the high sensitivity fluorescence endoscope may enable detection of ICG fluorescence in lung cancer. It may be one of the fundamental basies for proceeding to clinical trials. Disclosure: No significant relationships.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".