Evaluating CBCT-Guided Adaptive Radiotherapy for Pancreatic Cancer Using Synthetic CBCT Data
Bibliographic record
Abstract
Ethos adaptive radiotherapy is employed frequently in the pelvis to improve treatment accuracy by adapting to daily anatomical changes. The use of this CBCT-guided platform for abdominal treatments is made challenging by motion-related image artifacts that are detrimental to the Ethos auto-contouring process. We present a preliminary in silico study enabled by synthetic CBCT data of Ethos adaptive radiotherapy for pancreatic cancer. Simulation CT and daily CBCT images were collected from nonadaptive patients treated on Ethos. Contoured CBCTs drove structure-guided deformable registration from the CT to daily CBCTs, providing an approximate daily CT used to produce synthetic CBCT data. Two adaptive workflows were simulated using an Ethos emulator. Over 70 fractions across 10 patients in a solely deformation-based workflow, PTV prescription coverage increased by 23.3±9.4% through plan adaptation. Point doses to the stomach were reduced by 10.2±9.3%. Ultimately, un-adapted plans satisfied target coverage and OAR constraints in 0% and 6% of fractions while adapted plans did so in 80% of fractions. Anatomical variation led to poor performance in rigidly aligned un-adapted plans, illustrating the promise of Ethos adaptive radiotherapy in this region. This promise is balanced by the need for artifact reduction and questions regarding auto-contouring performance in the abdomen.
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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) | 0.001 | 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".