SU‐E‐J‐42: Patient Dependent Options for Image Guidance Procedures in Radiotherapy: Prostate Cancer
Bibliographic record
Abstract
Purpose: Retrospective analysis of image guidance data in radiation treatments of prostate cancer patients allows for simulation of imaging scenarios with less frequent or modified procedures. The correlation of patient specific features with inter‐fraction prostate variations can be explained using patient cohorts with large amounts of daily image data. Methods: The 6,085 setup correction shifts performed during radiotherapy of 216 prostate cancer patients on helical tomotherapy units in two cancer centers were analyzed with respect to automatic and manual matching procedures in co‐registration of planning kV and pre‐treatment MVCT studies. Margins needed to account for inter‐fraction target motion for a daily automatic corrections scheme and three schemes with limited imaging based on one, three and five first fractions as a reference were calculated. The body mass index (BMI) was calculated for all patients and the times required for different steps in the co‐registration process were evaluated. Results: The margin calculated for the daily automatic shift scheme was significantly lower than any of the margins calculated for the limited imaging schemes. The average time needed for management of the daily automatic correction shift during the whole course of the treatment was equal to 5 minutes, and was 30 times shorter than time needed for the daily registration based on automatic and manual correction shifts. Larger setup correction shifts were observed for the patients with higher BMI values. Conclusion: The patients with normal BMI values (BMI<25) require a significantly smaller setup correction and may be treated with a limited number of imaging sessions followed by treatment without imaging using external marks that take into account average systematic shift and personalized margins obtained from the data of the first several fractions. The overweight (25 30) patients may be treated with daily automatic co‐registration and reduced personalized margins.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".