SU‐GG‐J‐102: Image Guided Radiotherapy of the Cervix with Biomechanical Model‐Based Deformable Registration
Bibliographic record
Abstract
Purpose: Evaluate the potential geometric and dosimetric improvements in image‐guided radiotherapy of the cervix using deformable registration. Method and Materials: Weekly MR images obtained prior to and during radiotherapy for 29 women with cervix cancer were selected. The bladder, rectum, and GTV were contoured on each image following rigid, bony anatomy based registration. Deformable registration was performed using MORFEUS, a multi‐organ finite element model‐based deformable registration algorithm. The bladder and rectum were explicitly registered between planning and each week. The GTV displacement was estimated by the biomechanical model and the bladder and rectum displacement. The improvements using couch shifts based on deformable registration were assessed using 3 metrics: 1) improvement in localization of tumor COM, 2) improvements in GTV coverage by the PTV, and 3) improvements in the dosimetric coverage of the GTV and CTV accumulated over the treatment using deformable dose accumulation in ORBIT Workstation. Results: The average error in COM alignment of the tumor improved by 0.3, 2.1, and 1.1 mm in the LR, AP, and SI directions, respectively with deformable registration. Following bony registration, 69% of GTVs were at least 95% covered by the PTV, improving to 86% following deformable registration. Dosimetric coverage of the GTV improved from 93% of patients achieving 98% volume coverage by 4900 cGy to 100% following deformable registration‐based couch shift for tumor COM correction. CTV coverage improved from 83% of patients achieving 98% volume coverage by 4750 cGy to 93% following deformable registration‐based couch shift for tumor COM correction. Conclusion: A method has been developed to perform deformable registration of surrounding anatomy to calculate the COM of the tumor for application in volumetric image guidance where the tumor is not visible. Both geometric and dosimetric improvements were demonstrated. Supported by RaySearch Laboratories and National Cancer Institute of Canada — Terry Fox Foundation.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".