Sci-Sat AM: Stereo - 09: Accuracy of Liver Cancer Treatment on Cyberknife® with Synchrony™ Optical Tracking Throughout the Respiratory Cycle
Bibliographic record
Abstract
The Cyberknife® robotic stereotactic body radiation therapy system is well-suited for treating liver lesions over the respiratory cycle as it includes room-mounted orthogonal x-ray tracking of internal fiducial markers and optical tracking of external markers. The Synchrony™ software generates a model of internal target positions during patient respiration and correlates it to the external optical tracking system for real-time optical-based position corrections of the linear accelerator during beam delivery. Although clinical studies have provided preliminary outcomes for liver lesions treated with the Cyberknife system, to date, there is little data demonstrating the ability of the Synchrony software to track targets in the liver, which deforms throughout the respiratory cycle. In this study, we investigated the respiratory motion model performance for predicting tumour motion. We conducted a retrospective analysis of fifteen liver cancer patients treated on the Cyberknife using the Synchrony optical tracking system. We analyzed Cyberknife tracking information stored in the log files to extract the left-right (LR), anterior-posterior (AP) and superior-inferior (SI) correlation errors between the model-predicted position and the internal fiducial centroid position determined by x-ray imaging. Only translational tracking and corrections were applied during treatment. Overall, the correlation errors were greatest in the SI direction. We calculated radial correlation errors, and determined that the 95th, 98th and 99th percentile errors were 3.4 mm, 4.4 mm and 5.1 mm, respectively. Based on translational correlation tracking errors we expect the clinical target volume will be within 3.4 mm of the planning target volume for 95 % of beam delivery time.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 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".