<scp>EGSNRC</scp>Monte Carlo study of the effect of photon energy and field margin in phantoms simulating small lung lesions
Bibliographic record
Abstract
The dose distribution in small lung tumors (coin lesions) is affected by the combined effects of reduced attenuation of photons and extended range of electrons in lung. The increased range of electrons in low-density tissues can lead to loss of field flatness and increased penumbra width, especially at high energies. The EGSNRC Monte Carlo code, together with DOSXYZNRC, a three-dimensional voxel dose calculation module has been used to study the characteristics of the penumbra in the region of the target-lung interfaces for various radiation beam energies, lung densities, target-field edge distances, target size, and depth. The Monte Carlo model was validated by film measurements made in acrylic (simulating a tumor) imbedded in cork (simulating the lung). Beam profiles that are deemed to be acceptable are defined as those in which no point within the planning target volume (target volume plus 1 cm margin) received less than 95% of the dose prescribed to the center of the target. For parallel opposed beams and 2 cm cube target size, 6 MV photons produce superior dose distribution with respect to penumbra at the lateral, anterior, and posterior surfaces and midplane of the simulated target, with a target-field edge distance of 2.5 cm. A lesser target-field edge distance of 2.0 cm is required for 4 MV photons to produce acceptable dose distribution. To achieve equivalent dose distribution with 10 and 18 MV photons, a target-field edge distance of 3.0 and 3.5 cm, respectaively, is required. For a simulated target size of 4 cm cube, a target-field edge distance of 2, 2.5, and 3 cm is required for 6, 10, and 18 MV photons, respectively, to yield acceptable PTV coverage. The effect, which is predominant in determining the target dose, depends on the beam energy, target-field edge distance, lung density, and the depth and size of the target.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".