SU‐E‐T‐526: Evaluation of Dose Mapping Errors via Use of a Volume‐Based Dose Mapping Method
Bibliographic record
Abstract
PURPOSE: To quantify dose mapping errors (DMEs) of a point-based dose mapping method for 4D lung treatment plans. METHODS: Point-based dose mapping methods utilize deformation vector fields (DVFS) to interpolate dose from a deformed image. Volume-based dose mapping methods consider the volume overlap between deformed and reference voxels; defining dose as the integral energy divided by the integral mass of the voxel, and conserving integral dose . DME is defined as the dose differences between volume-based and point-based mapped dose (DME=(DpointBased-DvolumeBased)/DRx). The DME for a 4D lung case is compared with a bitmap DME method, both using a Pinnacle research version 8.1y DVF. DME is computed for ten 4D lung cases (five 10 phases, five 3 phase) with Pinnacle research version 9.100 DVFs. Multi-phase accumulated 4D DMEs are also evaluated. RESULTS: For all cases, the largest DMEs are located in the dose/density gradient regions. With Pinnacle 8.1y DVF, mapping dose from phase 9 to phase 0, results in a DME=-0.2%±6.1% (range of -76%∼112%). The same case with Pinnacle 9.100 DVFs, DME=0.3%±4.8%(-41%∼32%). Locations of large DME are consistent with those from the bitmap method. For the ten 4D lung cases, accumulated mean DME are within ±0.07% (std. deviations: 1∼5%, range -102%∼64%). Maximum tumor DMEs are less than 30cGy (DRx=7200cGy) for all patients. CONCLUSIONS: Due to its inherent integral dose conservation, volume-based dose mapping methods can quantify errors in point-based dose mapping methods. While mean DME values are small for the cases tested, standard deviations near 5% indicate that a substantial number of voxels have ∼5% dose mapping errors, however these dose errors do not occur in the target structures. Work supported by NIH P01CA116602.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".