Sci‐AM2 Sat ‐ 02: Energy optimization for inverse planning
Bibliographic record
Abstract
Purpose: To simultaneously optimize the photon beam energy, orientations(gantry, table), wedge filters and beam weights in aperture‐based IMRT, a new inverse planning system has been developed. This system, Ballista, uses anatomy‐based MLC fields as aperture. It is an alternative for beamlet‐based IMRT. The aim of this study is to include the photon energy in the optimization process. Method and Materials: Ballista uses a fast simulated annealing algorithm to select the optimal configuration of beams with respect to the objectives specified by the planner. To include the energy as a free parameter in the existing overall optimization system, this variable is interpreted by the algorithm as an angle. That is, each eligible energy is assigned to a circular section of the space of all possible solutions. In addition to varying the energy of the individual beams, an option was added to the process which allows associating a combination of energies to various segments of a single field. Results: The effectiveness of energy optimization has been tested on several sites. The lung cancer has provided good results. The value of V20(29.6%vs.35%) is reduced as well as the mean dose to the heart(14.5%vs.21.5%). Also, a better tumor coverage (V95%=97.8%vs.96.6% and IH=1.07vs.1.10) is obtained in the case of a non‐coplanar plan realized with this new approach in comparison with a standard plan (23MV only). Conclusion: The inclusion of energy gives the optimization process an additional advantage over standard planning, and better close the gap between the optimized approach and the clinical treatment methods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".