Laser interstitial thermal therapy of lung lesions near large vessels: a numerical study
Bibliographic record
Abstract
Abstract Objective. Laser interstitial thermal therapy (LITT) is an evolving hyperthermia-based technology that may offer a minimally invasive alternative to inoperable lung cancer. LITT of perivascular targets is challenged by higher risk of disease recurrence due to vascular heat sinks, as well as risk of damage to these vascular structures. The objective of this work is to examine the impact of multiple vessel parameters on the efficacy of the treatment and the integrity of the vessel wall in perivascular LITT. Approach. A finite element model is used to examine the role of vessel proximity, flow rate, and wall thickness on the outcome of the treatment. Main result. The simulated work indicates that vessel proximity is the major factor in driving the magnitude of the heat sink effect. Vessels situated near the target volume may act as a protective measure for reducing healthy tissue damage. Vessels with thicker walls are more at risk of damage during treatment. Interventions to reduce the flow rate may reduce the vessel’s heat sink effect but may also result in increased risk of vascular wall damage. Lastly, even at reduced blood flow rates, the volume of blood reaching the threshold of irreversible damage (>43 °C) is negligible compared to the volume of blood flow throughout the treatment duration. Significance. This investigative simulation yields results that may help guide clinicians on treatment planning near large vessels.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".