Sci—Fri PM: Delivery — 02: CT image guidance strategies for dose‐adaptive IMRT of the prostate
Bibliographic record
Abstract
On-line CT imaging in the radiotherapy room has become the norm for targeted intensity-modulated radiotherapy (IMRT), enabling precise adjustments of the daily patient setup based on soft tissue visualization. Corrections for plasticity of the anatomy and dose deformation are within technological reach but will require more on-line resources. We have developed a computer model that allows exploration of "what if" scenarios for assessing the benefits of Image Guidance strategies in terms of the multi-fraction dose distribution and DVH metrics (Target D95 and rectum V70). In this work we report on changes in anatomy and resultant dose distribution as observed in 35 daily megavoltage CT (MVCT) scans of the pelvis during prostate therapy for 13 patients. Our goal is to assess the effectiveness and efficiency of various adaptive strategies involving imaging schedule with and without dose re-planning of 5-field IMRT with 18 MV x-rays. Our research questions are: To what extent do radiation dose distributions delivered to individual patients (in vivo) diverge from the planned dose distributions (in silico)? Is there a robust schedule of CT image guidance, with or without dose re-planning that will mitigate discrepancies? For prostate IMRT, we conclude that image guidance schedule can be relaxed when generous GTV margins (10/7mm) are used. Tighter margins (isotropic 5 mm) reduce the dose to the rectum as expected. However, daily re-planning may be required to maintain adequate target coverage as planned when tighter margins are used.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.037 | 0.008 |
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".