A Numerical Study of Gold-Nanorod-Enhanced Noninvasive Laser Ablation for Central Lung Cancer Using an Optical-Thermal-Fluid Model
Bibliographic record
Abstract
Abstract Central lung cancer presents significant challenges due to its proximity to vital thoracic structures, making traditional treatments often less effective and more harmful. Laser ablation (LA) has emerged as a promising minimally invasive therapy, particularly when enhanced with gold nanorods (GNRs), which possess unique optical properties that amplify the effects of LA. This study introduces a comprehensive optical-thermal-fluid model designed to simulate the spatiotemporal distributions of GNRs and temperature involved in the noninvasive GNR-enhanced LA for central lung cancer. The effects of GNR enhancement on heat transfer and tumor ablation were investigated with regard to three cases of central lung cancer in different sizes and locations. The results demonstrate that GNRs significantly improve the heating efficiency within smaller tumors by concentrating laser energy, thus reducing the time needed to reach therapeutic temperatures. However, in larger tumors, particularly where the tumor size approaches the penetration depth of the laser, the GNRs cause substantial photon absorption near the emission surface, resulting longer treatment durations attributing to heat transfer. Nevertheless, GNRs consistently confine the thermal energy, minimizing damage to surrounding healthy tissue in tumors. This study highlights the potential of GNR-enhanced LA as a noninvasive treatment for central lung cancer. It also underscores the importance of considering tumor size in the treatment planning of GNR-enhanced LA.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".