Sci‐Thur PM Therapy‐07: Improving IMRT plans delivery for head and neck cases using aperture‐based MLC segments
Bibliographic record
Abstract
Purpose: To investigate the possibility of performing IMRT in head and neck treatment sites with less segments and monitor units (MU). Materials and methods: Six pharyngeal cases (n = 6) were analysed and four cases (n = 4), in the sinonasal region. For each one, an IMRT plan was first realized using a commercial software (Pinnacle3 — IMFAST segmentation algorithm —). Then, an‐in‐house inverse planning system, called Ballista, based on predetermined segments, was used to realize comparable plans. Its segments are generated with the subtraction of the projection of the OARs with the PTV (planning target volume). Results: For the pharyngeal Ballista plans, the average volume of the PTV that received at least 100% of the prescribed dose (V100) was 85.0±4.5% for the first prescription (PTV1) and the V100 for the second prescription (PTV2 — simultaneous integrated boost —) was 78.5±10.9%. With Pinnacle3, the V100 value was 86.6±4.8% and 81.5±12.4% respectively for PTV1 and PTV2 (see figure 2a and 2b). On average, Ballista plans have required 932±124 MUs and 52±10 segments compared to 1238±230 MU and 117±7 segments for Pinnacle3. For the sinonasal Ballista plans, the average V100 obtained was 80.0±3.1%. With Pinnacle3, the V100 gave 75.7±2.7%. Ballista plans have required an average of 406±54 MUs and 22±1 segments compared to 697±133 MUs and 99±14 segments for beamlet‐based IMRT. Conclusion: In step‐and‐shoot head and neck IMRT, an anatomy‐based MLC optimization system can achieve similar dosimetric plans comparable to traditional beamlet‐based IMRT with less number of segments and MU.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".