Dosimetric evaluation of VMAT treatment plans for patients with stage IIB or III non-small cell lung carcinomas
Bibliographic record
Abstract
Abstract Introduction: Volumetric-modulated arc therapy (VMAT) has emerged as a promising radiation treatment technique. One of the challenges in VMAT planning for lung carcinoma is the lack of consistency among different institutions with respect to what is considered an acceptable treatment plan in terms of target coverage and doses to the organs at risk (OAR). Additionally, the accuracy of dose calculations in the presence of heterogeneous medium (i.e. air) is another challenge in lung VMAT planning. Our objective is to develop an institutional criteria for non-stereotactic body radiotherapy (non-SBRT) lung treatment plans by evaluating the dosimetric impact of plan normalisation and dose calculation algorithms, including the Anisotropic Analytical Algorithm (AAA), AcurosXB (AXB) and Monte Carlo (MC) simulation, on VMAT plans for non-small cell lung cancer (NSCLC). Methods: The CT dataset of 20 patients with NSCLC was randomly selected to ensure a spectrum of target sizes and locations. All treatment planning was accomplished with 2–3 VMAT arcs and a prescription of 60 Gy in 30 fractions. Two plan normalisation methods were employed: (i) planning target volume (PTV) V 100% = 95% and (ii) PTV V 95% = 95%. Results: All three dose calculation algorithms revealed heterogeneous and conformal plans irrespective of plan normalisations. The PTV and OARs dose–volume constraints were met using both normalisation methods. However, we observed that AAA overestimated the minimum PTV doses by 2–5% regardless of plan normalisation. The mean PTV-V 100% was lower for AAA in comparison with AXB and MC algorithms. Conclusions: VMAT is an effective radiotherapy technique for achieving greater target dose conformity, heterogeneity and dose fall-off from the PTV for the treatment of NSCLC. The results of this study can provide the basis for the development of local plan acceptability criteria for NSCLC VMAT plans, and the clinical implementation can be achieved with minimal or no imposition on resources and time constraints. Occasionally, plan normalisation of PTV-V 95% = 95% may be required to ensure that the OAR dose tolerances are not exceeded.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".