Lung cancer photothermal ablation by low-power near-infrared laser and topical injection of indocyanine green
Bibliographic record
Abstract
OBJECTIVES: Surgical treatment is the gold standard for the treatment of early-stage non-small-cell lung cancer. However, minimally invasive tumour ablation can be an alternative treatment for patients not eligible for surgery due to comorbidities. The aim of this study was to evaluate the efficacy of photothermal ablation therapy using low-power near-infrared laser and topical injection of indocyanine green (ICG), a photosensitizer, in a preclinical study using a rabbit VX2 lung cancer model. METHODS: Six New Zealand white rabbits were used. Five hundred microlitres of a suspension containing 0.5 × 107 VX2 cancer cells with growth factor-reduced Matrigel was inoculated into the right lung using an ultrathin bronchoscope. Three rabbits were treated with laser ablation therapy with topical injection of ICG, whereas another 3 rabbits were treated with laser ablation alone. All tumours were irradiated with a laser with 500-mW output at 808 nm for 15 min. The tumours were examined histopathologically to assess the state of ablation. RESULTS: The maximum tumour surface temperatures in rabbits treated using ICG/laser and laser alone were higher than 58°C and lower than 40°C, respectively. The ablated areas in the rabbits treated with ICG/laser were significantly larger than those in the rabbits treated with laser alone (0.49 ± 0.27 vs 0.02 ± 0.002 cm2, respectively) (P < 0.05). CONCLUSIONS: The photothermal treatment using the combination of low-power near-infrared laser and topical injection of ICG can ablate a larger tumour area than laser treatment alone.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".