Poster — Thur Eve — 39: Dosimetric Evaluation of Bulk Electron Density Based Treatment Planning in IMRT Head and Neck Patients: Can It Be Used for MRI‐Based Planning?
Bibliographic record
Abstract
One limitation of using MRI alone for radiation planning is the lack of electron density for dose calculation. We evaluated the dosimetric accuracy of using bulk density overrides as a substitute for CT‐derived densities in IMRT treatment planning for head and neck cancer. Ten clinically‐approved, CT‐based treatment plans were used for this study. Three dose distributions were calculated for each treatment plan. The first calculation used CT‐derived density as a basis for heterogeneity correction. The second calculation assumed a homogeneous patient density of 1 g/cm3. For the third dose calculation we contoured bones and air cavities and assigned them a uniform density of 1.5 g/cm3 and 0 g/cm3, respectively. The remaining tissue was assigned a density of 1 g/cm3. All three calculations utilized identical beam parameters (angles, segments and MUs). Actual MR images were not used for contouring to avoid effects of gradient distortion and volumetric uncertainties associated with them. All calculations were done using the Collapsed Cone Superposition algorithm in the Pinnacle3 treatment planning system. Our results show that the assignment of bulk density to bones and air cavities was a feasible approach to IMRT treatment planning for head and neck patients. In almost all cases, the dosimetric results were within 2% of the treatment plans based on CT‐derived density. This method may overcome the lack of electron density information in MR‐based planning. The use of homogeneous geometry, while simpler and less time consuming, resulted in unacceptably high errors in the dose distribution compared to the nominal plan. This research project is supported by Philips Medical Systems.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".