Tracking the dose distribution in radiation therapy by accounting for variable anatomy
Bibliographic record
Abstract
The goal of this research is to calculate the daily and cumulative dose distribution received by the radiotherapy patient while accounting for variable anatomy, by tracking the dose distribution delivered to tissue elements (voxels) that move within the patient. Non-linear image registration techniques (i.e., thin-plate splines) are used along with a conventional treatment planning system to combine the dose distributions computed for each 3D computed tomography (CT) study taken during treatment. For a clinical prostate case, we demonstrate that there are significant localized dose differences due to systematic voxel motion in a single fraction as well as in 15 cumulative fractions. The largest positive dose differences in rectum, bladder and seminal vesicles were 29%, 2% and 24%, respectively, after the first fraction of radiation treatment compared to the planned dose. After 15 cumulative fractions, the largest positive dose differences in rectum, bladder and seminal vesicles were 23%, 32% and 18%, respectively, compared to the planned dose. A sensitivity analysis of control point placement is also presented. This method provides an important understanding of actual delivered doses and has the potential to provide quantitative information to use as a guide for adaptive radiation treatments.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".