A comparison of voxel sampling approaches for intensity‐modulated radiation therapy
Bibliographic record
Abstract
BACKGROUND: Intensity-modulated radiation therapy (IMRT) treatment planning technology has made personalized cancer care a reality for millions of patients in recent decades. The complexity of these tools, however, often leads to intractability and suboptimal mitigation strategies in otherwise well-optimized plans. An example of such a strategy is reducing data granularity through voxel sampling. PURPOSE: While the practice of sampling is nearly universal in treatment planning, methods vary widely across studies and clinical practices, and few studies directly compare these approaches. The goal of this study is to examine the relative quality loss of some of the more common voxel sampling methods proposed in the literature. METHODS: Five core sampling approaches identified from the literature, and their variations, were distilled into eight distinct sampling methods. A comparative framework, with multiple fluence map optimization models was developed into a testing pipeline built on MATLAB, C++, and CPLEX. This pipeline was then used to evaluate the trade-off between computational complexity and information loss across all sampling approaches. The pipeline was run on the open-source CORT dataset as well as a retrospective clinical lung, prostate, chest wall, esophagus and neck studies, at sampling rates that disregarded roughly 87.5%, 93.7%, and 96.9% of patient voxels, respectively. RESULTS: -means sampling were also beneficial in certain cases, but experienced high run times or worse information loss in others. CONCLUSIONS: The proposed framework was successfully able to inform the selection of sampling approach based on patient data characteristics in the IMRT setting. Future extensions could lead to enhanced sampling methods as well as methodological expansions to more radiation treatment modalities.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".