Image-guided Preoperative Localization of Pulmonary Nodules for Video-assisted and Robotically Assisted Surgery
Bibliographic record
Abstract
Video-assisted thoracic surgery (VATS) and robotically assisted surgery are used increasingly for minimally invasive diagnostic and therapeutic resection of pulmonary nodules. Unsuccessful localization of small, impalpable, or deep pulmonary nodules can necessitate conversion from VATS to open thoracotomy. Preoperative localization techniques performed by radiologists have improved the success rates of VATS resection for small and subsolid nodules. Any center at which VATS diagnostic resection of indeterminate pulmonary nodules is performed should be supported by radiologists who offer preoperative nodule localization. Many techniques have been described, including image-guided injection of radioisotopes and radiopaque liquids and placement of metallic wires, coils, and fiducial markers. These markers enable the surgeon to visualize the position of an impalpable nodule intraoperatively. This article provides details on how to perform each percutaneous localization technique, and a group of national experts with established nodule localization programs describe their preferred approaches. Special reference is made to equipment required, optimization of marker placement, prevention of technique-specific complications, and postprocedural treatment. This comprehensive unbiased review provides valuable information for those who are considering implementation or optimization of a nodule localization program according to workflow patterns, surgeon preference, and institutional resources in a particular center. ©RSNA, 2019
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".