A Varian DynaLog file‐based procedure for patient dose‐volume histogram‐based IMRT QA
Bibliographic record
Abstract
In the present study, we describe a method based on the analysis of the dynamic MLC log files (DynaLog) generated by the controller of a Varian linear accelerator in order to perform patient-specific IMRT QA. The DynaLog files of a Varian Millennium MLC, recorded during an IMRT treatment, can be processed using a MATLAB-based code in order to generate the actual fluence for each beam and so recalculate the actual patient dose distribution using the Eclipse treatment planning system. The accuracy of the DynaLog-based dose reconstruction procedure was assessed by introducing ten intended errors to perturb the fluence of the beams of a reference plan such that ten subsequent erroneous plans were generated. In-phantom measurements with an ionization chamber (ion chamber) and planar dose measurements using an EPID system were performed to investigate the correlation between the measured dose changes and the expected ones detected by the reconstructed plans for the ten intended erroneous cases. Moreover, the method was applied to 20 cases of clinical plans for different locations (prostate, lung, breast, and head and neck). A dose-volume histogram (DVH) metric was used to evaluate the impact of the delivery errors in terms of dose to the patient. The ionometric measurements revealed a significant positive correlation (R² = 0.9993) between the variations of the dose induced in the erroneous plans with respect to the reference plan and the corresponding changes indicated by the DynaLog-based reconstructed plans. The EPID measurements showed that the accuracy of the DynaLog-based method to reconstruct the beam fluence was comparable with the dosimetric resolution of the portal dosimetry used in this work (3%/3 mm). The DynaLog-based reconstruction method described in this study is a suitable tool to perform a patient-specific IMRT QA. This method allows us to perform patient-specific IMRT QA by evaluating the result based on the DVH metric of the planning CT image (patient DVH-based IMRT QA).
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.007 |
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".