Dynamic CT angiography for cyberknife radiosurgery planning of intracranial arteriovenous malformations: a technical/ feasibility report
Bibliographic record
Abstract
BACKGROUND: Successful radiosurgery for arteriovenous malformations (AVMs) requires accurate nidus delineation in the 3D treatment planning system (TPS). The catheter biplane digital subtraction angiogram (DSA) has traditionally been the gold standard for evaluation of the AVM nidus, but its 2D nature limits its value for contouring and it cannot be imported into the Cyberknife TPS. We describe a technique for acquisition and integration of 3D dynamic CT angiograms (dCTA) into the Cyberknife TPS for intracranial AVMs and review the feasibility of using this technique in the first patient cohort. PATIENTS AND METHODS: Dynamic continuous whole brain CT images were acquired in a Toshiba 320 volume CT scanner with data reconstruction every 0.5 sec. This multi-time-point acquisition enabled us to choose the CT data-set with the clearest nidus without significant enhancement of surrounding blood vessels. This was imported to the Cyberknife TPS and co-registered with planning CT and T2 MRI (2D DSA adjacent for reference). The feasibility of using dCTA was evaluated in the first thirteen patients with outcome evaluation from patient records. RESULTS: dCTA data was accurately co-registered in the Cyberknife TPS and appeared to assist in nidus contouring for all patients. Imaging modalities were complementary. 85% of patients had complete (6/13) or continuing partial nidus obliteration (5/13) at 37 months median follow-up. CONCLUSIONS: dCTA is a promising imaging technique that can be successfully imported into the Cyberknife TPS and appears to assist in radiosurgery nidus definition. Further study to validate its role is warranted.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".