66: Retrospective Analysis of Inter-Fractional Weight Loss and Setup Uncertainties for Head and Neck Cancer Patients
Bibliographic record
Abstract
Purpose: The accumulated delivered dose to mobile organs can be estimated by the use of Deformable Image Registration (DIR).This study assessed the quality of CT-Cone Beam CT (CBCT) DIR based on inclusion of various guiding volumes and/or points for the pelvic region.Methods and Materials: Reference CT and 13 CBCTs from each of 13 prostate patients with intraprostatic fiducial markers (IFM) were retrieved.Prostate, bladder and rectum were delineated on all image datasets.Each CBCT was deformed to the CBCT using DIR by the following: 1) image intensity; 2) 3 IFMs as the guiding points (POIG); 3) bladder and rectum as the guiding volumes (VOIG); and 4) VOIG+POIG.For each DIR, ProstateDIR, BladderDIR, and RectumDIR were generated and compared with the manually delineated volumes on CBCT.Distance between surfaces (DSS) < 2 mm is considered as having good agreement between the volumes.Results: A total of 2028 volumes were generated for analysis.Volumes generated by DIR using image intensity had the lowest agreement (Range of Mean DSS: ProstateDIR = 2 -6 mm; BladderDIR = 6 -23 mm; RectumDIR = 2 -6 mm).The use of POIG decreased the DSS for ProstateDIR but had no impact on either BladderDIR or RectumDIR.Agreement of these volumes increased when VOIG or VOIG+POIG was used (Range of Mean DSS: ProstateDIR = 1 -2 mm; BladderDIR = 0.9 -1 mm; RectumDIR = 0.1 -0.6 mm).Difference between VOIG and VOIG+POIG was not statistically significant (p = 0.6).Conclusions: CT-CBCT DIR using VOIG and VOIG+POIG resulted in the smallest difference between volumes.There is no additional benefit of including guiding points when guiding volumes are used to perform DIR.Impact of interobserver variability in contouring guiding volumes on DIR needs to be further investigated.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".