Bibliographic record
Abstract
Intensity modulated radiation therapy (IMRT) uses nonuniform intensity distributions to conform high dose to a tumor and low dose to surrounding sensitive structures. Because of the large number of beams (5–11) and the wide range of intensities, treatment planning is typically an inverse problem in which the intensity distributions are optimized. Three areas addressed in this thesis are plan complexity, beam directions, and dose–volume constraints. Inverse treatment planning is flexible and can deliver complex dose distributions that are sometimes not warranted. The first goal of this thesis is to demonstrate simple alternatives to inverse planning that use just enough degrees of freedom for the problem so that the solution is not overly sensitive to a slight change in dose constraints and patient geometry. With the addition of simple beam direction optimization, a suitable IMRT plan can be created while maintaining clinical practicality. The second goal of the thesis is to introduce and analyze a new algorithm which systematically analyzes and selects beam directions in the fewest number of beams possible. In IMRT, the optimization of beam directions is complicated due to the interdependence with beam intensities. Our beam direction algorithm has the capability of achieving plans that are better than standard IMRT techniques, often with a fewer number of beams. The third goal of this thesis is to propose a new formulation of the inverse treatment planning optimization problems that include dose–volume constraints which are known to destroy convexity. This is compared to a formulation that has been addressed in the literature. We solve both formulations with a new technique based on direct search optimization with a systematic search region reduction. This is compared to a standard fast simulated annealing technique. The results of using the new formulation show a direct correspondence between the minimum objective function values and the resulting dose distributions and dose–volume histograms. The research of this thesis is performed using examples of lung, prostate, and brain stem radiotherapy. We provide evidence that dose–volume based formulations of inverse treatment planning optimization for IMRT have the ability to achieve optimal plans that are clinically relevant.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".