Sci‐Fri AM: YIS‐06: On‐line adaptive radiation therapy based on the intra‐fractional digital tomosynthesis images
Bibliographic record
Abstract
PURPOSE: To investigate the feasibility of performing on-line adaptive radiation therapy (ART) based on the intra-fractional digital tomosynthesis (DTS) images. METHOD AND MATERIALS: Intra-fractional DTS images were reconstructed as the gantry rotated between treatment positions. An edge detection algorithm was used to automatically segment the DTS images as the gantry arrived at each treatment position. The original treatment plan was then re-optimized for the most recent DTS image contours and dose was delivered from each treatment position based on the newly re-optimized plan. Plan re-optimization was performed using modified direct aperture optimization (DAO). To test our system, a model representing typical prostate, bladder and rectum anatomy was generated. First, a treatment plan based on this original anatomy was created using our DAO system. To simulate prostate deformations, three clinically relevant deformations (small, medium and large) were modeled by systematically deforming the original anatomy. The ability of our approach to adapt the original treatment plan and account for the anatomy deformations was investigated. RESULTS: Based on the dose-volume constraints from the RTOG 0415 prostate protocol, the original treatment plan would have been clinically unacceptable for all three deformations. Using our approach to on-line ART, the original treatment plan was successfully adapted to arrive at a clinically acceptable plan for all three anatomy deformations. CONCLUSION: We have shown that performing on-line ART based on intra-fractional DTS images is feasible. The advantages are reduced treatment time and the ability to detect and account for patient motion during the treatment fraction.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.051 | 0.007 |
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".