Poster — Thur Eve — 57: Craniospinal irradiation with jagged‐junction IMRT approach without beam edge matching for field junctions
Bibliographic record
Abstract
PURPOSE: Craniospinal irradiation were traditionally treated the central nervous system using two or three adjacent field sets. A intensity-modulated radiotherapy (IMRT) plan (Jagged-Junction IMRT) which overcomes problems associated with field junctions and beam edge matching, improves planning and treatment setup efficiencies with homogenous target dose distribution was developed. METHODS AND MATERIALS: Jagged-Junction IMRT was retrospectively planned on three patients with prescription of 36 Gy in 20 fractions and compared to conventional treatment plans. Planning target volume (PTV) included the whole brain and spinal canal to the S3 vertebral level. The plan employed three field sets, each with a unique isocentre. One field set with seven fields treated the cranium. Two field sets treated the spine, each set using three fields. Fields from adjacent sets were overlapped and the optimization process smoothly integrated the dose inside the overlapped junction. RESULTS: For the Jagged-Junction IMRT plans vs conventional technique, average homogeneity index equaled 0.08±0.01 vs 0.12±0.02, and conformity number equaled 0.79±0.01 vs 0.47±0.12. The 95% isodose surface covered (99.5±0.3)% of the PTV vs (98.1±2.0)%. Both Jagged-Junction IMRT plans and the conventional plans had good sparing of the organs at risk. CONCLUSIONS: Jagged-Junction IMRT planning provided good dose homogeneity and conformity to the target while maintaining a low dose to the organs at risk. Jagged-Junction IMRT optimization smoothly distributed dose in the junction between field sets. Since there was no beam matching, this treatment technique is less likely to produce hot or cold spots at the junction in contrast to conventional techniques.
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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 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".