Sci-Thur PM: YIS - 01: Inverse treatment planning for modulated electrons and mixed photon and electron radiotherapy
Bibliographic record
Abstract
Modulated electron radiotherapy (MERT) takes advantage of the low distal dose of electrons to reduce dose to healthy tissue. The dosimetric advantage of MERT is clear when compared against single-field electron irradiation where MERT demonstrates superior target homogeneity and sparing; however the dosimetric advantage is unclear when comparing MERT with photon intensity-modulated radiotherapy (IMRT) where MERT techniques struggle to match the IMRT target homogeneity but with less total energy delivered to healthy tissues. In an effort to improve dosimetric benefits of MERT, this study investigated an inverse planning technique for the creation of hybrid MERT-IMRT mixed beam radiotherapy (MBRT) plans. The optimization process decouples the photon and electron beamlets for combined modality optimization. The input to the optimization algorithm was a series of patient-specific 3D dose distributions for the corresponding electron and photon beamlets, while the output was a list of weights that satisfied the optimization constraints. A photon IMRT Eclipse (Varian, Palo Alto, CA) plan and a MERT plan were created for a patient-specific sarcoma irradiation. The MERT plan was competitive in its ability to reduce dose to organs at risk and total-body dose; however, the plan suffered from poorer target conformity compared with the IMRT plan. The MBRT plan was created by adding two photon fields, divided into beamlets, to the electron beamlets of the MERT plan for reoptimization. The MBRT plan improved MERT target coverage with only minimal cost to healthy tissue dose. The MBRT plan provided clear dosimetric advantages over the IMRT and MERT plan.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".