Evaluation of Eclipse© Monte Carlo dose calculation for clinical electron beams using heterogeneous phantoms
Bibliographic record
Abstract
Accurate dose calculations of photon and electron transport in tissue materials are an important step in the appropriate delivery of cancer radiotherapy. Various commercial treatment planning systems used in radiotherapy provide algorithms for fast dose calculations. It is the responsibility of medical physicists to commission and evaluates these algorithms. In this work, we have evaluated the electron Monte Carlo (MC) algorithm in Eclipse using solid water phantoms with various tissue heterogeneities (water, lung, cortical bone, air) embedded, and using CT data from a real patient. For heterogeneous phantoms, the evaluation is done by comparing dose profiles and percent depth doses (PDDs) calculated on Eclipse with measurements, and with MC simulations using DOSXYZnrc. Measurements of dose profiles and PDDs are taken using EBT Gafchromic films, and we have developed a piece of software in Matlab for extracting dose from EBT Gafchromic films. For the real patient case, we use DOSXYZnrc results as a benchmark against which Eclipse is evaluated. Although Eclipse has been evaluated previously, the originality of the present work lays on the use of digitally reproduced phantom copies on Eclipse and DOSXYZnrc instead of CT scanned phantoms, the use of absolute dose for all comparisons, and the consideration of a real clinical patient. In addition, we have developed a tool for extracting absolute dose profiles and PDDs from EBT Gafchromic films. Our results indicate that, MC results agree in general better with measurements (within 5% or less) than Eclipse MC, whose discrepancies with measurements can be as high as 15% for physical phantoms used and as high as 10% in the case of real patient CT data. Largest discrepancies between measurement (or MC) and Eclipse MC occur at depths near and below tissue heterogeneities with relatively sharp density gradients. The slightly better performance of Eclipse for the real patient case is related to the smoother changes in hetero
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".