Comparison of bulk electron density and voxel‐based electron density treatment planning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The use of magnetic resonance imaging (MRI) alone for radiation planning is limited by the lack of electron density for dose calculations. The purpose of this work is to evaluate the dosimetric accuracy of using bulk electron density as a substitute for computed tomography (CT)-derived electron density in intensity-modulated radiation therapy (IMRT) treatment planning of head and neck (HN) cancers. Ten clinically-approved, CT-based IMRT treatment plans of HN cancer were used for this study. Three dose distributions were calculated and compared for each treatment plan. The first calculation used CT-derived density and was assumed to be the most accurate. The second calculation used a homogeneous patient density of 1 g/cm3. For the third dose calculation, bone and air cavities were contoured and assigned a uniform density of 1.5 g/cm3 and 0 g/cm3, respectively. The remaining tissues were assigned a density of 1 g/cm3. The use of homogeneous anatomy resulted in up to 4%-5% deviations in dose distribution as compared to CT-derived electron density calculations. Assigning bulk density to bone and air cavities significantly improved the accuracy of the dose calculations. All parameters used to describe planning target volume coverage were within 2% of calculations based on CT-derived density. For organs at risk, most of the parameters were within 2%, with the few exceptions located in low-dose regions. The data presented here show that if bone and air cavities are overridden with the proper density, it is feasible to use a bulk electron density approach for accurate dose calculation in IMRT treatment planning of HN cancers. This may overcome the problem of the lack of electron density information should MRI-only simulation be performed.
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.
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 | 0.001 | 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 | 0.001 |
| 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 it