Development and evaluation of an ultrasound‐guided tracking and gating system for hepatic radiotherapy
Bibliographic record
Abstract
PURPOSE: Respiratory motion must be accounted for daily in order to permit optimum radiotherapy of hepatic malignancies. However, existing tracking systems are often invasive or poorly tolerated by patients. The authors describe the development and validation of an ultrasound-guided tracking and gating system for stereotactic body radiation therapy of the liver. METHODS: This noninvasive system is designed to determine the correlation between tumor and external fiducial motion and to verify the optimum gating level for treatment delivery daily. A tracked ultrasound probe moves with patient respiration, obtaining 2D ultrasound images of tumor motion throughout the respiratory cycle. The target volume is registered to the static radiotherapy treatment beams in order to verify optimum gating levels. These gating levels are then transferred to an existing gating system for treatment delivery. The authors examined the temporal and spatial accuracy of this system using a custom-built phantom and verified the accuracy of gating level transfer and delivery. RESULTS: The temporal accuracy of the ultrasound-guided system was shown to be comparable to the existing clinical x-ray imaging system. Using ultrasound rather than x-rays to image internal targets provides good soft-tissue contrast without the invasiveness of implanting fiducial markers. High frame rates enable continuous monitoring of the target throughout the respiratory cycle. The authors anticipate this passive monitoring system should be well tolerated by patients. CONCLUSIONS: The system developed provides good quality video of the laboratory motion phantom and can be successfully used in gated beam delivery.
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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 | 0.002 | 0.003 |
| 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 | 0.001 | 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".