Robot-assisted thoracoscopic brachytherapy for lung cancer: Comparison of the ZEUS robot, VATS, and manual seed implantation
Bibliographic record
Abstract
Objective: Interstitial brachytherapy is becoming an accepted treatment option for lung cancer patients for whom surgery poses a high risk. Robotic surgery has the potential to deliver brachytherapy seeds into tumors while keeping surgeons at a safe distance from the radioactive source. Our aim was to compare the accuracy, number of attempts, and time needed to place seeds next to a target when using a manual technique, video-assisted thoracoscopic surgery (VATS), and the ZEUS robot for minimally invasive surgery (MIS).Methods: A brachytherapy seed injector was developed and attached to one of the ZEUS robotic arms. Four subjects each inserted inactive dummy brachytherapy seeds into clear agar-gelatin cubes containing a 1.6-mm stainless steel ball target. Two orthogonal radiographs were taken of each agar cube, and the corresponding distances were measured in triplicate using ImageJ processing software. The mean distance between the center of each seed and the corresponding target was calculated using the Pythagorean theorem. Comparisons were made using analysis of variance, t-tests, and Kruskal–Wallis tests, as appropriate.Results: A total of 384 tests (128 for each technique) were performed. The median accuracies for the manual technique, VATS, and ZEUS were 1.8 mm (range: 0.9–6.7 mm), 2.4 mm (range: 1.0–11.3 mm), and 3.6 mm (range: 1.3–16.7 mm), respectively (p < 0.01). The median numbers of attempts for the manual technique, VATS, and ZEUS were 1 (range: 1–5), 4 (range: 1–14), and 3 (range: 1–20), respectively (p < 0.01). The median times for the manual technique, VATS, and ZEUS were 3.0 s (range: 1–43 s), 86.5 s (range: 6–372 s), and 64.5 s (range: 5–356 s), respectively (p < 0.01).Conclusions: The manual technique is the most accurate, least traumatic, and fastest method of inserting seeds into tumors. The ZEUS robotic platform was able to place seeds beside a target within a clinically acceptable distance, with an acceptable amount of trauma and time required. It achieved results equal to or better than those obtained with VATS.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".