Three-dimensional virtual lung reconstruction in robotic segmentectomy: A safety and feasibility trial
Bibliographic record
Abstract
Objective Robotic pulmonary segmental resection is a technically challenging procedure. Near-infrared fluorescence mapping with intravascular indocyanine green dye is a valuable adjunct; however, conversion to lobectomy still occurs in up to 40% of cases. We hypothesized that the incorporation of 3-dimensional virtual lung reconstruction would result in low rates of conversion from segmentectomy to lobectomy and increased confidence in the surgical plan. Methods A prospective single-center cohort trial was conducted to determine the safety and feasibility of this approach. Patients undergoing robotic segmentectomy for clinical stage I non–small cell lung cancer less than 3 cm were enrolled, and 3-dimensional reconstruction was performed with confidence scores assigned before and after 3-dimensional reconstruction. Adverse events, rates of conversion to lobectomy, and changes in confidence scores were recorded and analyzed. Results A total of 79 patients were enrolled from December 2022 to April 2024, and 76 patients (96.20%) underwent surgery. Three-dimensional reconstruction was successfully performed in 88.16% (67/76) of cases, and indocyanine green dye was used in 68.66% (46/67) with no adverse events related to its use. The 30-day mortality was 1.49% (1/59). The majority of patients (80.60%; 54/67) underwent a successful segmentectomy, whereas 8.96% (6/67) of cases were converted to lobectomy after segmentectomy was started. The planned operation was modified after 3-dimensional reconstruction in 36.07% (22/61) of cases leading to a significant increase in confidence scores ( P < .001). Conclusions Three-dimensional lung reconstruction in targeted robotic segmental resection is associated with low rates of conversion to lobectomy and increased surgeon confidence. Further studies are warranted to establish the effectiveness of this technique.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".