Modelling novel PDT approaches to target peripheral lung cancers (Conference Presentation)
Bibliographic record
Abstract
While the prevalence of central bronchial tumours is declining, that of peripheral lung tumours is increasing. Peripheral lung tumours present either as individual index lesion or as field cancerization, requiring for the former targeting of particular confined volumes of lung tissue versus a therapy for an entire lung or particular lobes thereof. Using FullMonte, a Monte Carlo code; the ability to achieve a tumour selective PDT by transbronchial light source placement was simulated for 525, 665 and 808 nm wavelength. Simulations were executed utilizing in silica models with up to 10 generations of the bronchial tree, tissue photosensitizer concentrations taken from literature or measure in preclinical model systems and tissue optical properties measured with alive ex vivo pig and human lungs perfused with either blood or a transparent low cellular (STEEN) fluid. The measured effective attenuation coefficients [cm-1] at the three wavelengths for ventilated lungs with either blood 1.26±1.07, 1.93±0.534, 1.09±0.93 or STEEN fluid 1.01±0.873, 0.901±0.318, 0.641±0.31 used as perfusate. When modelling the PDT dose distribution in the lung’s the bronchial air ducts up to the eight generations perturb the fluence considerably. In all simulations, a dose sufficient to cause necrosis in 98% of the target volume placement of 3 source fibres albeit with various extent of normal lung tumour damage. Full coverage of an entire lung lobe with only three source fibres placed does not provide for effective coverage of the diffuse disease unless a very high selective uptake of the photosensitizer in malignant tissues can be achieved.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".