Technical Note: Dose‐volume histogram analysis in radiotherapy using the Gaussian error function
Bibliographic record
Abstract
A mathematical model based on the Gaussian error and complementary error functions was proposed to describe the cumulative dose-volume histogram (cDVH) for a region of interest in a radiotherapy plan. Parameters in the model (a, b, c) are related to different characteristics of the shape of a cDVH curve such as the maximum relative volume, slope and position of a curve drop off, respectively. A prostate phantom model containing a prostate, the seminal vesicle, bladder and rectum with cylindrical organ geometries was used to demonstrate the effect of interfraction prostate motion on the cDVH based on this error function model. The prostate phantom model was planned using a five-beam intensity modulated radiotherapy (IMRT), and a four-field box (4FB), technique with the clinical target volume (CTV) shifted in different directions from the center. In the case of the CTV moving out of the planning target volume (PTV), that is, the margin between the CTV and PTV is underestimated, parameter c (related to position of curve drop off) in the 4FB plan and parameters b (related to the slope of curve) and c in the IMRT plan vary significantly with CTV displacement. This shows that variation of the cDVH is present in the 4FB plan and such variation is more serious in the IMRT plan. These variations of cDVHs for 4FB and IMRT are due to the different dose gradients at the CTV edges in the anterior and posterior directions for the 4FB and IMRT plan. It is believed that a mathematical representation of the dose-volume relationship provides another viewpoint from which to illustrate problems with radiotherapy delivery such as internal organ motion that affect the dose distribution in a treatment plan.
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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".