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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".