Poster — Thur Eve — 52: Head and Neck IMRT Complexity Characterization and Prediction of Deliverability Using the Modulation Complexity Score
Bibliographic record
Abstract
Intensity modulated radiation therapy (IMRT) is often referred to as being highly complex, but the definition of complexity can be varied. A single metric, the modulation complexity score (MCS), has previously been developed to quantify IMRT complexity. The purpose of this study is to evaluate the use of MCS in the characterization of segmentation complexity and beam deliverability in the context of head and neck IMRT. Fifty treatment plans were evaluated by calculating the MCS per beam. Patient‐specific measurements were obtained as part of the standard IMRT QA process using a diode array and retrospective analysis was completed using various gamma analysis criteria. The MCS, as well as single beam parameters (e.g. number of MU or control points), were compared to dosimetric results. 375 individual treatment beams were analyzed, with an average MCS of 0.245 (range: −0.338 – 0.754) and 125 MU on average (range: 32 to 295). All beams had >90% of diodes passing the standard gamma analysis (3%/3mm) with an average of 98%. There was a linear relationship between the number of MU and the MCS score (r2=0.75). The relationship between pass rate and complexity (characterized by MCS or MU) is not simple, however it may be possible to predict good dosimetric results based on the plan complexity. In conjunction with the ability to compile complexity statistics for specific treatment sites or protocols, MCS could impact the radiation therapy process at many points, including during planning, plan evaluation and QA.
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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 | 0.003 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.007 |
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".