Algorithm Validation for Treatment Planning Systems in Lung Region
Bibliographic record
Abstract
Abstract Accurate dose calculation in radiotherapy is crucial for effective treatment of cancer while minimizing radiation exposure to normal tissues. In this study, the accuracy of anisotropic analytical algorithm (AAA) in radiotherapy treatment planning systems (RTPSs) for lung cancer is evaluated by comparing calculated and measured dose distributions using CIRS Thorax Phantom (Model No.: 002 LFC, CIRS Inc., Norfolk, VA). Three treatment planning techniques—3D conformal radiation therapy (3D CRT), intensity-modulated radiation therapy (IMRT), and volumetric modulated arc therapy (VMAT)—were compared using two X-ray energies (6 MV and 10 MV). Absolute dosimetry was performed on a one-dimensional (1D) water phantom under standard conditions, and dose delivery was checked using a DOSE-1 reference class electrometer. The percentages of error between calculated and measured doses for 6 MV beams were 0.31% for 3D CRT, 2.52% for IMRT, and 0.15% for VMAT. For 10 MV beams, the errors were 0.21%, 0.26%, and 1.41%, respectively. These results demonstrate strong agreement between calculations and measurements, remaining within the 3% tolerance for the lung region. The causes of differences were inhomogeneity of lung tissue, scatter effects, and limitations of the dose algorithm. High-energy beams (10 MV) with increased scattering affecting dosing precision were seen in this study. Among the three techniques, VMAT and 3D CRT exhibited better agreement with planned doses compared to IMRT. These findings confirm the validity of modern treatment planning algorithms for handling tissue heterogeneity and precise dose delivery in lung cancer radiotherapy.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".