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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 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".