Dosimetric comparison between the prostate intensity-modulated radiotherapy (IMRT) and volumetric-modulated arc therapy (VMAT) plans using the planning target volume (PTV) dose–volume factor
Bibliographic record
Abstract
Abstract Background We demonstrated that our proposed planning target volume (PTV) dose–volume factor (PDVF) can be used to evaluate the PTV dose coverage between the intensity-modulated radiotherapy (IMRT) and volumetric-modulated arc therapy (VMAT) plans based on 90 prostate patients. Purpose PDVF were determined from the prostate IMRT and VMAT plans to compare their variation of PTV dose coverage. Comparisons of the PDVF with other plan evaluation parameters such as D 5% , D 95% , D 99% , D mean , conformity index (CI), homogeneity index (HI), gradient index (GI) and prostate tumour control probability (TCP) were carried out. Methods and materials Prostate IMRT and VMAT plans using the 6 MV photon beams were created from 40 and 50 patients, respectively. Dosimetric indices (CI, HI and GI), dose–volume points ( D 5% , D 95% , D 99% and D mean ) and prostate TCP were calculated according to the PTV dose–volume histograms (DVHs) of the plans. All PTV DVH curves were fitted using the Gaussian error function (GEF) model. The PDVF were calculated based on the GEF parameters. Results From the PTV DVHs of the prostate IMRT and VMAT plans, the average D 99% of the PTV for IMRT and VMAT were 74·1 and 74·5 Gy, respectively. The average prostate TCP were 0·956 and 0·958 for the IMRT and VMAT plans, respectively. The average PDVF of the IMRT and VMAT plans were 0·970 and 0·983, respectively. Although both the IMRT and VMAT plans showed very similar prostate TCP, the dosimetric and radiobiological results of the VMAT technique were slightly better than IMRT. Conclusion The calculated PDVF for the prostate IMRT and VMAT plans agreed well with other dosimetric and radiobiological parameters in this study. PDVF was verified as an alternative of evaluation parameter in the quality assurance of prostate treatment planning.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".