Dosimetric implications of two registration based patient positioning methods in prostate image guided radiation therapy (IGRT)
Bibliographic record
Abstract
Dosimetric implications of two registration based patient positioning methods in prostate image guided radiation therapy (IGRT)Background. We compare the dosimetry of daily patient positioning based on prostate matching versus bone matching for patients treated with helical tomotherapy.Methods. Ninety-nine pre-treatment 3D megavoltage (MV) CT images of four high risk prostate patients were registered to their respective planning images using two automatic registration algorithms, one achieving bone matching and the other prostate matching. Dose distributions that would have been delivered had patient positioning been based on each matching method were evaluated. Contours were delineated on each MVCT image and prostate, bladder, and rectum dose volume histograms were compared for each image guidance strategy using endpoints adapted from inverse planning constraints.Results. The standard deviation of per fraction prostate ΔD95 values, defined as prostate matching D95 minus bone matching D95, was 0.01 Gy (Range: -0.02 to 0.02 Gy). Defined analogously, bladder ΔD45 and rectum ΔD30 values were 0.12 Gy (Range: -0.22 to 0.52 Gy) and 0.14 Gy (Range: -0.40 to 0.34 Gy), respectively.Conclusions. Bladder ΔD45 and rectum ΔD30 standard deviation values corresponding to 6.1% and 7.5% of their respective planning constraints suggesting critical structure doses are dependent on positioning method. A relationship between critical
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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) | 0.000 | 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".