Schedule for CT image guidance in treating prostate cancer with helical tomotherapy
Bibliographic record
Abstract
The aim of this study was to determine the effect of reducing the number of image guidance sessions and patient-specific target margins on the dose distribution in the treatment of prostate cancer with helical tomotherapy. 20 patients with prostate cancer who were treated with helical tomotherapy using daily megavoltage CT (MVCT) imaging before treatment served as the study population. The average geometric shifts applied for set-up corrections, as a result of co-registration of MVCT and planning kilovoltage CT studies over an increasing number of image guidance sessions, were determined. Simulation of the consequences of various imaging scenarios on the dose distribution was performed for two patients with different patterns of interfraction changes in anatomy. Our analysis of the daily set-up correction shifts for 20 prostate cancer patients suggests that the use of four fractions would result in a population average shift that was within 1 mm of the average obtained from the data accumulated over all daily MVCT sessions. Simulation of a scenario in which imaging sessions are performed at a reduced frequency and the planning target volume margin is adapted provided significantly better sparing of organs at risk, with acceptable reproducibility of dose delivery to the clinical target volume. Our results indicate that four MVCT sessions on helical tomotherapy are sufficient to provide information for the creation of personalised target margins and the establishment of the new reference position that accounts for the systematic error. This simplified approach reduces overall treatment session time and decreases the imaging dose to the patient.
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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 | 0.003 |
| 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) | 0.002 | 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".