4DCT Simulation With Synchronized Contrast Injection in Liver SBRT Patients
Bibliographic record
Abstract
BACKGROUND/PURPOSE: Delivering stereotactic body radiotherapy for liver metastases remains a challenge because of respiratory motion and poor visibility without intravenous contrast. The purpose of this article is to describe a novel and simple computed tomography (CT) simulation process of integrating timed intravenous contrast that could overcome the uncertainty of target delineation. METHODS AND RESULTS: The simulation involves two 4-dimensional CT (4DCT) scans. The first scan only encompasses the immediate region of the tumor and surrounding tissue, which reduces the 4DCT scan time so that it can be optimally timed with intravenous contrast injection. The second 4DCT scan covers a larger volume and is used as the primary CT data set for dose calculation, as well as patient setup verification on the treatment unit. The combination of the two 4DCT scans allows us to optimally visualize liver metastases over all phases of the breathing cycle while simultaneously acquiring a long enough 4DCT data set that is suitable for planning and patient setup verification. CONCLUSION: This simulation technique allows for a better target definition when treating liver metastases, without being invasive.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".