SU-GG-T-63: Feasibility Study of Longitudinal Field Junctioning with Helical Tomotherapy
Bibliographic record
Abstract
Purpose: To examine junctioning of longitudinally adjacent PTVs treated with helical tomotherapy (HT). Method and Materials: Cylindrical PTVs were defined in an elliptic cylindrical homogeneous phantom. Dose distributions (95% PTV to receive 2 Gy) created using 2.5 and 5.0 cm long HT fields were calculated and verified dosimetrically. Cranial — Caudal (CC) dose profiles were summed to study the junctioning of PTVs to create a single contiguous PTV. Junctioning adjacent PTVs with different inter-PTV spacing, created by equal or different field sizes was studied for dose homogeneity. The use of dose stepped PTVs near the junction region was also examined. Here the SUP end of the INF PTV or the INF end of the SUP PTV was divided into smaller subPTVs of decreasing prescription dose. The resulting dose distributions were summed as a function of inter-PTV spacing. Simulated dose profiles were verified by film dosimetry. Results: The most homogenous dose resulted when adjacent PTVs had the same CC dose profile (field size). Independent of the Inter-PTV spacing, PTVs of different CC dose profiles could not produce homogeneous doses. Minimizing the volume dose excursion from prescription resulted in cold spots (−26%) and hot spots (+29%) with 8% of the PTV receiving < 95% of prescription. Dividing each PTV into four multiple contiguous subPTVs, with constantly decreasing prescribed dose (2, 1.5, 1.0, 0.5Gy) allowed PTV matching with dose homogeneity similar to junctioning PTVs of equal CC slope. 95% of the PTV received at least 101% of the prescribed dose, with dose excursions of −19% to +13% from prescription, (1% of the PTV received less than 95% of prescribed dose). Conclusion: Junctioning adjacent PTVs is possible, but PTVs created by different field widths present a challenge. Homogeneity is improved by breaking PTVs into multiple contiguous subPTVs modified to feather (broaden) the effective junctioning region.
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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 | 0.001 | 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) | 0.001 | 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".