Electromagnetic navigation improves minimally invasive robot-assisted lung brachytherapy
Bibliographic record
Abstract
Objective: Recent advances in minimally invasive thoracic surgery have renewed an interest in the role of interstitial brachytherapy for lung cancer. Our previous work has demonstrated that a minimally invasive robot-assisted (MIRA) lung brachytherapy system produced results that were equal to or better than those obtained with standard video-assisted thoracic surgery (VATS) and comparable to results with open surgery. The purpose of this project was to evaluate the performance of an integrated system for MIRA lung brachytherapy that incorporated modified electromagnetic navigation and ultrasound image guidance with robotic assistance.Methods: The experimental test-bed consisted of a VATS box, ZEUS® and AESOP® surgical robotic arms, a seed injector, an ultrasound machine, video monitors, a computer, and an endoscope. Our previous custom-designed electromagnetic navigational software and the robotic controller were modified and incorporated into the MIRA III system to become the next-generation MIRA IV. Inactive brachytherapy seeds were injected as close as possible to a small metal ball target embedded in an opaque agar cube. The completion time, the number of attempts, and the accuracy of seed deployment were compared for manual placement, standard VATS, MIRA III, and the new MIRA IV system.Results: The MIRA IV system significantly reduced the median procedure time by 61% (104 s to 41 s), tissue trauma by 75% (4 attempts to 1 attempt), and mean seed placement error by 64% (2.5 mm to 0.9 mm) when compared to a standard VATS. MIRA IV also reduced the mean procedure time by 48% (85 s to 44 s) and the seed placement error by 68% (2.8 mm to 0.9 mm) compared to the MIRA III system.Conclusions: A modified integrated system for performing minimally invasive robot-assisted lung brachytherapy was developed that incorporated electromagnetic navigation and an improved robotic controller. The MIRA IV system performed significantly better than standard VATS and better than MIRA III.
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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 | 0.001 |
| 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".