Dosimetric verification of lung phantom calculated by collapsed cone convolution: A Monte Carlo and experimental evaluation
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Bibliographic record
Abstract
OBJECTIVE: To evaluate the dose calculation accuracy in the Prowess Panther treatment planning system (TPS) using the collapsed cone convolution (CCC) algorithm. METHODS: The BEAMnrc Monte Carlo (MC) package was used to predict the dose distribution of photon beams produced by the Oncor® linear accelerator (linac). The MC model of an 18 MV photon beam was verified by measurement using a p-type diode dosimeter. Percent depth dose (PDD) and dose profiles were used for comparison based on three field sizes: 5×5, 10×10, and 20×20cm2. The accuracy of the CCC dosimetry was also evaluated using a plan composed of a simple parallel-opposed field (11×16cm2) in a lung phantom comprised of four tissue simulating media namely, lung, soft tissue, bone and spinal cord. The CCC dose calculation accuracy was evaluated by MC simulation and measurements according to the dose difference and 3D gamma analysis. Gamma analysis was carried out through comparison of the Monte Carlo simulation and the TPS calculated dose. RESULTS: Compared to the dosimetric results measured by the Farmer chamber, the CCC algorithm underestimated dose in the planning target volume (PTV), right lung and lung-tissue interface regions by about -0.11%, -1.6 %, and -2.9%, respectively. Moreover, the CCC algorithm underestimated the dose at the PTV, right lung and lung-tissue interface regions in the order of -0.34%, -0.4% and -3.5%, respectively, when compared to the MC simulation. Gamma analysis results showed that the passing rates within the PTV and heterogeneous region were above 59% and 76%. For the right lung and spinal cord, the passing rates were above 80% for all gamma criteria. CONCLUSIONS: This study demonstrates that the CCC algorithm has potential to calculate dose with sufficient accuracy for 3D conformal radiotherapy within the thorax where a significant amount of tissue heterogeneity exists.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it