The use of inhomogeneity corrections for inverse planned IMRT /
Bibliographic record
Abstract
In this thesis, the use of inhomogeneity corrections in intensity modulated radiotherapy (IMRT) inverse treatment planning is investigated. Firstly, the dosimetric consequences of CT beam hardening artifacts present on images used for treatment planning are estimated and found to be of little clinical significance (<1% dose difference). Secondly, experiments to evaluate the PEREGRINE Monte Carlo system (Nomos, Cranberry, PA) are undertaken for a 6 MV photon beam. The use of inhomogeneity corrections in clinical treatment planning is assessed for five clinical head and neck cancer cases. The cases are planned with the CORVUS optimization module and the dose distributions are then calculated with CORVUS and PEREGRINE in order to compare the two calculation techniques with emphasis on how each method handles tissue inhomogeneities. The plans are assessed in terms of dose, dose-volume distributions and the biological indices of TCP and NTCP. On average, PEREGRINE calculates a 1% lower mean dose to the GTV and a 2% lower mean dose to the CTV compared to the CORVUS calculations with EPL inhomogeneity corrections. In the last part of this work, quality assurance (QA) measurements are performed for a clinical case to investigate how the CORVUS and PEREGRINE calculations agree with the dose measurements on a QA phantom.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 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".