TH‐C‐137‐05: A MOrphing Technique That Projects 3D Surface Objects to a STandard Metric (MOST); Geometric Modeling of Cervix Cancer Patients for Adaptive Radiation Treatment
Bibliographic record
Abstract
Purpose: Adaptive radiation therapy (ART) had been proposed based in order to correct the dosimetric discount due to geometric variations by means of feedback control. Numerical modeling of inter‐and intra‐patients' geometric variations is one of the important parts in ART. To this end, a MOrphing technique that projects 3D surface objects to a STandard metric (MOST) was developed. Methods: A total of 174 clinical target volumes (CTVs) were obtained from 32 patients and used to evaluate the MOST. The MOST consists of three main steps; 1) deforming 3D mesh of CTVs to a sphere by parametric active contour (PAC) model, 2) sampling the deformed mesh at evenly distributed girds to be the standard metric, and 3) projecting the 3D data into 2D plane for further analysis. The performance of the MOST was evaluated with respects to 1) iteration number, 2) computation time, and 3) residual deformation, the residual distance between a sphere surface and the deformed node. Result: MOST successfully transformed complex 152 meshes from 28 patients to the standard metric. Convergence was achieved with average iteration of 65 and one standard deviation (STD) of 74 for 137 cases. The computation time was 17.8 hours and this corresponds to 51.3% of the time for all 152 cases. The average±STD of residual deformation was 0.9±0.7‐mm and more than 98% nodes had less than 3‐mm residual deformation. Conclusions: MOST demonstrated its ability to transform complex 3D meshes of cervix cancer into a simple standard metric. Inter‐and intra‐patients' geometric variation could be directly compared and analyzed using the transformed results by virtue of the regularity of standard metric. Numerical modeling of tumor dynamics, implemented using this technique, may provide a systematic way for the estimation of tumor evolution and optimal correctional action for ART. This study is supported by funding from Ray Search Laboratories and Ontario Consortium for Adaptive Interventions Radiation Oncology (OCAIRO).
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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) | 0.001 | 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".