MO‐F‐108‐01: Deformable Dose Reconstruction to Evaluate Image‐Guidance Strategies in Free‐Breathing Liver SBRT
Bibliographic record
Abstract
Purpose: To evaluate the delivered dose following image‐guidance strategies for liver SBRT. Methods: The delivered dose was reconstructed using deformable image‐registration (DIR) on the retrospectively sorted (4D) cone‐beam CT (CBCT) for 30 SBRT patients (15 with multiple tumors). Plans created on exhale 4D‐CT, for 27–60 Gy/6 fractions, were delivered in free‐breathing after 3D‐CBCT rigid liver alignment. Dose was also reconstructed after simulating two rigid alignment strategies on the exhale 4D‐CBCT: the liver or the tumor directly (using DIR to predict tumor position). Delivered doses were compared to the planned 4D breathing dose, modeled with DIR of 4D‐CT. Results: Residual mean tumor errors >3 mm occurred in nine patients (30%) following free‐breathing 3D‐CBCT (max: 10 mm), and two patients (6.6%) following liver or tumor 4D‐CBCT alignment (maximum: 3.3 mm). With free‐breathing 3D‐CBCT, the delivered minimum tumor doses decreased by more than 1 Gy compared to planned dose for 4 (13%) patients. For 2 of these 4 patients, aligning the liver or tumor on 4D‐CBCT reduced the dose decreases from −1.4 Gy, to within −0.5 Gy. The other 2 patients had multiple tumors and substantial liver deformation, resulting in minimum tumor doses decreases (max: −4.3 Gy) following free‐breathing 3D‐CBCT, that were reduced with either liver (max: −2.7 Gy) or tumor (max: −2.2 Gy) 4D‐CBCT alignment. For normal gastrointestinal tissues receiving >30 Gy, the delivered maximum doses deviated by −11.5 to 2.6 Gy, with one exceeding the planning constraint by 0.7 Gy following free‐breathing 3D‐CBCT. Aligning the liver or tumor on 4D CBCT reduced normal tissue deviations (range: −6.3, 2.7 Gy) without exceeding planning constraints. Conclusion: 4D‐CBCT guidance for liver SBRT has been clinically implemented. The improved correlation between the 4D planning dose and delivered dose can be largely accomplished with 4D alignment of the liver, and should allow for planning margin reduction. This research is supported by the NIH, 5RO1CA124714‐02, and a Canadian Institutes for Health Research Fellowship. Patient data was acquired during clinical trials supported by the National Cancer Institute of Canada, #18207, and CIHR, #202477. K.K. Brock has financial interest in the deformable registration technology through a licensing agreement with RaySearch Laboratories.
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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 | 0.002 |
| 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".