TH-C-201C-04: Breathing Motion and Deformation of Pancreatic Cancer Patients and Its Effect on Planning Dose
Bibliographic record
Abstract
Purpose: Generate a deformable model of the pancreas and surrounding anatomy and evaluate the dosimetric impact of respiration motion on the pancreas and the surrounding organs-at-risk (OARs) during radiation therapy for pancreatic cancer. Methods and Materials: A multi-organ biomechanical model-based deformable image registration platform (MORFEUS) characterize exhale to inhale breathing motion from the 4DCT treatment planning images of seven pancreatic cancer patients. The deformed pancreas and OARs were compared to their respective volumes contoured on the inhale images using a volume overlap criteria (DICE). The dose delivered to the OARs during free breathing was calculated using MORFEUS and compared to the static planning dose. Results: The DICE using MORFEUS for the pancreas duodenum left and right kidneys was (mean±SD) 0.79±0.06 0.71±0.08 0.89±0.05 and 0.88±0.06 respectively. The effect of breathing on the max and mean dose was small however differences were seen in the dose to a percentage of the volume. For the duodenum 4 patients had an average dose difference of 1.9±0.9 Gy for 60–90% volume (max 4 Gy). Similarly for the stomach 6 patients had an average dose difference of 1.8±1.1 Gy for 20–50% of the volume (3 patients had > 3 Gy). One patient had 1.8 Gy difference to 40% of the bowel volume. Conclusion: A deformable model of the pancreatic cancer anatomy was developed and shown to provide good volume overlap. Due to the overlap of the CTV with the OARs the dosimetric effect of breathing on the overall max dose was small however changes of more than 1 Gy were observed in the duodenum stomach and bowel to a percent of the volume which may have clinical significance especially in these patients treated with combined radiosensitizers. Research sponsored by NIH 5RO1CA124714-02 Elekta Oncology Systems and K. Brock is supported by a Cancer Care Ontario Research Chair.
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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 | 0.002 |
| 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) | 0.001 | 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".