Comparison of dosimetric variation between prostate IMRT and VMAT due to patient's weight loss: Patient and phantom study
Bibliographic record
Abstract
AIM: This study compared the dosimetric impact between prostate IMRT and VMAT due to patient's weight loss. BACKGROUND: Dosimetric variation due to change of patient's body contour is difficult to predict in prostate IMRT and VMAT, since a large number of small and irregular segmental fields is used in the delivery. MATERIALS AND METHODS: Five patients with prostate volumes ranging from 32.0 to 86.5 cm(3) and a heterogeneous pelvis phantom were used for prostate IMRT and VMAT plans using the same set of dose-volume constraints. Doses in IMRT and VMAT plans were recalculated with the patient's and phantom's body contour reduced by 0.5-2 cm to mimic size reduction. Dose coverage/criteria of the PTV and CTV and critical organs (rectum, bladder and femoral heads) were compared between IMRT and VMAT. RESULTS: In IMRT plans, increases of the D99% for the PTV and CTV were equal to 4.0 ± 0.1% per cm of reduced depth, which were higher than those in VMAT plans (2.7 ± 0.24% per cm). Moreover, increases of the D30% of the rectum and bladder per reduced depth in IMRT plans (4.0 ± 0.2% per cm and 3.5 ± 0.5% per cm) were higher than those of VMAT (2.2 ± 0.2% per cm and 2.0 ± 0.6% per cm). This was also true for the increase of the D5% for the right femoral head in a patient or phantom with size reduction due to weight loss. CONCLUSIONS: VMAT would be preferred to IMRT in prostate radiotherapy, when a patient has potential to suffer from weight loss during the treatment.
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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) | 0.000 | 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) | 0.001 | 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 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".